fundamentals of soot oxidation master test 2 · pdf fileof soot oxidation shazam williams ......
TRANSCRIPT
i
SURFACE INTERMEDIATES, MECHANISM AND REACTIVITY OF SOOT OXIDATION
By
Shazam Williams
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Department of Chemical Engineering and Applied Chemistry University of Toronto
© Copyright by Shazam Williams 2008
ii
Surface intermediates, mechanism, and reactivity
of soot oxidation Shazam Williams
Doctor of Philosophy
Department of Chemical Engineering and Applied Chemistry,
University of Toronto
2008
Abstract
Factors that may govern diesel particulate matter (DPM) oxidation at low temperatures
(~200°C) were studied using reactivity and TP-ToFSIMS analysis. Best-case scenarios that
give maximum gasification rates were determined for DPM impregnated with KOH and non-
catalyzed DPM using temperature programmed oxidation and isothermal experiments.
Conditions of intimate catalyst-carbon contact (K/C molar ratio=1/50) and high NO2
concentrations (1%) to improve the reactivity of the carbon reactive sites were unable to meet
the steady state gasification rate needed for particulate filter regeneration for a modern diesel
engine at 200°C. Oxygen-free thermal annealing (>500°C) caused reactivity losses of a
maximum of 40% that correspond to changes to surface morphology and/or concentration of
oxygen-containing functional groups.
TP-ToFSIMS identified surface functional group changes with temperature on non-dosed and
NOX pre-dosed (1.5%NO, 1%NO2, 4.5%O2, balance helium) diesel soot and sucrose char.
Detailed analysis of the NOX dosed sucrose char spectra using both inspection and principal
component analysis techniques revealed that the 1200 ion fragments created could be reduced
to five sets of ions that are chemically and kinetically distinct. These sets presumably
represent surface functional groups on the carbon. For example, Set IV may represent
carboxylic acid, lactone, or carboxylic anhydride functional groups. Based on these results a
mechanism for the surface reaction of NO2 with carbon under vacuum conditions was
postulated. At temperatures less than 200°C the ion fragments contain primarily carbon-NO2
iii
iii
type ions. As temperature increases between 200 and 400°C the ion fragments are primarily
carbon-NO and carbon-N type fragments. At higher temperatures (>500°C) the surface is
enriched with nitrogen containing functional groups. A surface reaction mechanism is
proposed where NO2 is bonded to an armchair site and with increasing temperatures and
molecular rearrangements the N is incorporated into the carbon ring. The initial surface
composition of NOx containing functional groups changes within the area of relevance of low
temperature soot regeneration (i.e. between 25° and 200°C). Further studies are needed to
understand the effect of N-incorporation on carbon reactivity. No rate processes either in
reactor studies or based on surface functional groups met the rate criteria for low temperature
DPM oxidation.
iv
iv
Acknowledgements There are many people who have played important roles in my journey to complete this thesis.
The most influential is Prof. Charles Mims, who has always been a great mentor, instructor and
a friend over the many years that I have known him. And yes Chuck, now I do believe that I
have made a significant contribution.
It is true that a good thesis is not possible without an outstanding committee. My sincere
thanks to my committee members – Prof. Greg Evans, Prof. Jim Wallace, Prof. Charles Jia,
Prof. Jane Phillips, and Prof. Brian Haynes – for their excellent feedbacks, comments on the
thesis and their valuable time. Special thanks go to Prof. Phillips for her mentoring and
instruction since my bachelor degree years.
This journey would not have begun without the encouragement and support of Mr. George
Swiatek, his wife Eva, John Muter and my many colleagues at DCL International Inc. Thank
you, George, for having the confidence in me.
Thanks to Peter Broderson, Chris Bertole, Vik Pandit, Tom Wood, Cassie Liu and Naim
Ghany, for the support and the discussions during this time.
Thank you to my family, Mom, Dad, grandparents, Kevin, Carissa, Sheri, Jay, Kris, Josh,
Brianna and many friends for their support. Finally, my fiancée Josephine, her constant smile
and encouragement have made the journey enjoyable.
Financial support of this research was provided by DCL International Inc. and NSERC.
v
v
Nomenclature
A is the frequency factor
Ao is the frequency factor at a fractional conversion of 10%
CAT – Soot collected from a Caterpillar 3306 engine
CO – Carbon monoxide
CO2 – Carbon dioxide
CS - Total quantity of carbon in the filter or reactor
DPF – Diesel Particulate Filter
DPM – Diesel Particulate Matter
DRIFTS- Diffuse Reflectance Infrared Fourier Transform Spectroscopy
fc – Fractional conversion of carbon
fs = carbonsites (site density)
FID – Flame Ionization Detector
FTIR – Fourier Transform InfraRed spectroscopy
GC – Gas Chromatography
HRTEM – High Resolution Transmission Electron Microscopy
IR – InfraRed spectroscopy
k - represents the TOF of single ion from ToFSIMS data using the differential analysis method.
k0.1 represents the gasification rate of a single ion based on TOF values or k values calculated
from ToFSIMS data using the differential analysis method.
K- Potassium based catalyst
K/C – Potassium to carbon ratio
ntj = total of all atoms in the ion j
nxj represents the number of atoms of element x in ion j
N(x)T – Normalized intensity ratio of component x used in ToFSIMS experiments
Na – Sodium based catalyst
NIST – National Institute of Science and Technology
NIST-ANN – Soot sample from NIST annealed in an inert atmosphere
NIST-0 – Soot sample from NIST as-is
vi
vi
NO – Nitrogen monoxide
NO2 – Nitrogen dioxide
NOx – Nitrogen oxides, a mixture of nitrogen monoxide and nitrogen dioxide
ri, sims – represents the microscopic gasification rate or TOF of single ion based on the integral
analysis of the ToFSIMS data.
ri, sims (average) - is the microscopic gasification rate of an ion group (Set) based on the integral
analysis of the ToFSIMS data
rg, sims - is the gasification rate of a given ion group based on the integral analysis of the
ToFSIMS data
Rfco is the rate of carbon gasification at a fractional conversion of 10%
RG – Macroscopic gasification rate
RGo – Macroscopic gasification rate under steady state conditions where mass of carbon in
filter or reactor system is not varying.
Ro,SIMS is the rate of ion x of the ToFSIMS data using the integral analysis method
S – reactive sites on carbon
SC_NOX- Sucrose char exposed to nitrogen oxides
SC-AIR – Sucrose char exposed to air
SIMS – Secondary Ion Mass Spectroscopy
SOF – Soluble Organic Fraction
ta – time of acquisition of ToFSIMS spectra
tr- time to temperature ramp heated stage from one temperature to another during ToFSIMS
experiments
ts – time to stabilze the vacuum pressure in the ToFSIMS equipment
th- time of operator interruptions during ToFSIMS experiments
tann - Time that carbon was annealed in an inert atmosphere
TEM – Transmission Electron Microscopy
Tfc – Temperature to reach a given fractional conversion of carbon during TPO
TGA – Thermal Gravimetric Analysis
TPR – Temperature Programmed Reaction
TPD – Temperature Programmed Desorption
TOFG –Turn over frequency or microscopic gasification rate of a reactive site
ToFSIMS – Time of Flight Secondary Ion Mass Spectroscopy
vii
vii
TP-ToFSIMS – Temperature Programmed Time of Flight Secondary Ion Mass Spectroscopy
XPS – X-ray Photoelectron Spectroscopy
yxj = atomic ratio of element x in ion j defined as yxj = nxj/ntj, where ntj = total of all atoms in
the ion j, nxj represents the number of atoms of element x in ion j
Reactivity terminology
RG - macroscopic gasification rate is defined as:
SG Cdt
dCR 1•−= (1/time = 1/h)
Where dC = moles of carbon reacted, CS = total quantity of carbon in the filter or reactor and, t
= time.
Microscopic reactivity rate – single site
RG = TOFG * S/C Where S/C is the number of reactive sites per total carbon in the system. Microscopic reactivity rate – multiple sites
RG = ∑(TOFG, i * Si/C) Where, subscript, i, represents a single reactivity type TOFG - Turnover frequency or microscopic reactivity rate
SdtdCTOFG
1•−= (mol/time *1/site) (h-1)
Here, dC = moles of carbon reacted, S is the number of carbon reactive sites, and dt is time of reaction yxj = atomic ratio of element x in ion j defined as yxj = nxj/ntj, where ntj = total of all atoms in the ion j, nxj represents the number of atoms of element x in ion j yx sample @ T is the mole fraction of a given element (x) in the complete spectrum at a single temperature
yx sample @ T = [Σ (Ij @ T * yxj)]/ I total @ T
Itotal @ T = Σ Ij @ T
where Ij @ T is the ion intensity of ion j at the specified temperature, T, and I total is the sum of all ion intensities at temperature T
viii
viii
N(x)T – Normalized intensity ratio of component x used in ToFSIMS experiments is defined as
)()(
2)( −=
CIxIxN T
where, I(x) = intensity of component x at T, I(C2-) = intensity of C2
- ion at T, N = normalized intensity of ion x at temperature T Ro,SIMS is the rate of ion x of the ToFSIMS data using the integral analysis method
Ro,SIMS25
21
N(x) *t N(x) - N(x)
=Δ=
T
TT
where , ∆t is the difference between the time of the start of the acquisition and the end of the acquisition. k - represents the TOF of single ion from ToFSIMS data using the differential analysis method.
k (1/h) = Δ N(x)/Δ t *1/N(x)o = slope/y-intercept Where N(x)o is the normalized intensity at time = 0.
k0.1 represents the gasification rate of a single ion based on TOF values calculated from ToFSIMS data using the differential analysis method.
k0.1(1/h) = k * sites/C where sites/C = 0.1 ri, sims represents the microscopic gasification rate or TOF of single ion based on the integral analysis of the ToFSIMS data.
ri, sims = a
i
NdtdN 1
∗
ri, sims (average) is the microscopic gasification rate of an ion group (Set) based on the integral analysis of the ToFSIMS data.
ri, sims (average) = set
ions
ionsimsi
n
r∑ ,
rg, sims is the gasification rate of a given ion group based on the integral analysis of the ToFSIMS data
rg, sims = - simsir , (average) * fs
where fs = carbonsites (site density)
ix
ix
Table of Contents Abstract..................................................................................................................................... ii
Acknowledgements...................................................................................................................iv
Nomenclature.............................................................................................................................v
Reactivity terminology ........................................................................................................... vii
Table of Contents......................................................................................................................ix
List of Figures...........................................................................................................................xi
List of Tables ...........................................................................................................................xv
1 Motivation/Overview.........................................................................................................1
1.1 Motivation..............................................................................................................1 1.2 Overview................................................................................................................4
1.2.1 Research Objectives.......................................................................................4 1.2.2 Thesis Structure .............................................................................................4
2 Background........................................................................................................................5
2.1 Fundamental understanding of carbon oxidation ..................................................5 2.1.1 Carbon structure.............................................................................................5 2.1.2 Identification of surface functional groups....................................................9 2.1.3 Carbon reaction mechanism ........................................................................11 2.1.4 Active Sites..................................................................................................12 2.1.5 Oxidant effects.............................................................................................13 2.1.6 Structural effects on soot oxidation .............................................................19 2.1.7 Catalyst effects.............................................................................................21
2.2 DPM filter technology and state of the art...........................................................24 2.2.1 Current legislation and technology..............................................................24 2.2.2 Relating fundamental kinetics to engineering targets..................................30
3 Experimental procedure for reactivity studies.................................................................35
3.1 Soot characterization and catalyst impregnation .................................................35 3.1.1 Materials ......................................................................................................35 3.1.2 Impregnation of carbon samples with catalyst precursors...........................36 3.1.3 Elemental characterization...........................................................................36 3.1.4 Spectroscopic characterization of soot ........................................................38
3.2 Reactivity studies.................................................................................................39 3.2.1 Reactor system.............................................................................................39 3.2.2 Reactor loading............................................................................................42 3.2.3 Data analysis ................................................................................................43
4 Reactivity Studies ............................................................................................................44
4.1 Introduction..........................................................................................................44 4.2 Overview: ............................................................................................................46 4.3 Published carbon reactivity studies .....................................................................46
4.3.1 Survey of literature carbon oxidation rates with NOX.................................47
x
x
4.3.2 Temperature Programmed Oxidation Experiments in O2 atmosphere (with and without catalyst)....................................................................................................51 4.3.3 Reaction testing of soot and catalyzed soot in NO2 atmosphere .................54 4.3.4 Thermal annealing (Isothermal experiments) O2 atmosphere ....................56 4.3.5 Temperature Programmed Oxidation - Thermal Annealing Experiments ..66
4.4 Conclusion/Summary ..........................................................................................72 4.5 Future Work/Suggestions ....................................................................................73
5 ToFSIMS study of surface functional group reactivity ...................................................74
5.1 Introduction..........................................................................................................74 5.2 Experimental Procedure.......................................................................................78
5.2.1 Sample preparation and pre-treatment.........................................................78 5.2.2 TP ToFSIMS experiment description..........................................................79 5.2.3 ToFSIMS spectra, data calibration and peak assignment............................82 5.2.4 Data Analysis...............................................................................................84 5.2.5 Reference ions and relative intensities ........................................................86 5.2.6 Plan of data analysis ....................................................................................86
5.3 Results..................................................................................................................87 5.3.1 SIMS atomic composition change with temperature...................................87 5.3.2 SIMS elemental compositions of diesel soots .............................................90 5.3.3 General SIMS atomic change observations.................................................96 5.3.4 SIMS molecular fragment changes..............................................................97 5.3.5 Rate analyses of ion fragment data ............................................................106 5.3.6 Effect of temperature on individual ion sets, SC_NOX ............................130 5.3.7 Reactivities of surface ion precursors........................................................133 5.3.8 Surface mechanistic considerations...........................................................138
5.4 Conclusions........................................................................................................149 5.5 Recommendations for future work ....................................................................150
6 Conclusions and Recommendations ..............................................................................151
7 References......................................................................................................................154
8 Appendix Information ...................................................................................................180
8.1 Appendix A: Soot impregnation technique and procedure................................181 8.2 Appendix B: SEM photos..................................................................................190 8.3 Appendix C: Raman experimental procedure and results .................................193 8.4 Appendix D: PCA plots .....................................................................................201 8.5 Appendix E: Certification and specification sheets...........................................211
xi
xi
List of Figures Figure 2.1.1: Oxygen containing functional groups on carbon .............................................6 Figure 2.1.2: Representation of hexane soot showing defects and distortions from Smith et
al. 18................................................................................................................................8 Figure 2.1.3: Marsh-Griffiths model of graphitisation from Marsh 22 ..................................8 Figure 2.1.4: Illustration of acidic and basic groups on carbon. 25 ......................................10 Figure 2.1.5: Carbon structure identifying zigzag and armchair sites .................................13 Figure 2.2.1: General comparison of on-road heavy-duty diesel (HDD) standards in the
US, Japan, and Europe. Estimated engine-out emissions for 2007 and 2010 (range) are shown. Steady-state cycle. 243 Reprinted with permission from SAE Paper# 2006-01-0030 © 2006 SAE International. ............................................................................25
Figure 2.2.2: Example of exhaust flow through wall flow filter .........................................26 Figure 2.2.3: Example of exhaust flow thorough (non-blocking) filter (DCL) 251 Reprinted
with permission from SAE Paper# 2007-01-4025 © 2007 SAE International............27 Figure 2.2.4: Base case gasification rate criteria .................................................................31 Figure 2.2.5: Example of gasification rate chart indicating desired reaction region...........32 Figure 3.2.1: Reactor system setup......................................................................................40 Figure 3.2.2: Example of FID calibration............................................................................40 Figure 3.2.3: Calculated FID flame chemistry limiting O2 cases ........................................41 Figure 3.2.4: Example of flow controller calibration ..........................................................43 Figure 4.3.1: Literature survey of gasification rate data for catalyzed carbon reaction with
NOX. Data normalized to total NOX values of 1000 ppm ..........................................50 Figure 4.3.2: Example of temperature programmed oxidation experiment. Sample NIST,
10% O2.........................................................................................................................51 Figure 4.3.3: Gasification rate plot with TPO O2 data: NIST soot and K-NIST (K/C mol
ratio = 1/50) compared to Yezerets et al. 2003 70, 2005 69 data; reaction conditions: 10% O2, 7000 h-1, ramp rate 5.8°C/min, Symbols with thin lines are literature values. Heavy lines represent thesis experimental data. ..........................................................53
Figure 4.3.4: Gasification rate plot with non-catalyzed and catalyzed NIST samples in O2 and NO2 atmospheres: Reaction conditions: Ramp rate: 5.8°C/min, 7000 h-1, O2 runs: 10% O2, NO2 runs: 4.5% O2, 1 % NO2, 4 % NO, K-NIST sample: K/C : 1/50 mol ratio, Na-NIST sample: Na/C : 1:50 mol ratio Symbols with thin line represent literature data. Heavy lines represent thesis experimental data. .................................56
Figure 4.3.5: Effect of isothermal thermal annealing: Reaction Temperature = 550°C, B1, B2: Base case- burnoff curves with 10% O2/He. T2: Step change in Oxygen concentration from 10% to 0%, Thermal annealed for 1 hour at 550°C with He only......................................................................................................................................61
Figure 4.3.6: Effect of in-situ high temperature annealing: Reaction Temperature = 550°C, B1, B2: Base case- burnoff curves with 10% O2/He. T2: Step change in Oxygen concentration from 10% to 0%, 1st Thermal anneal at 550°C for 1 hour and 2nd Thermal anneal at 700°C with He only. ......................................................................64
Figure 4.3.7: Effect of 200°C pre anneal and reaction with oxygen (T3): Temp2 = 550°C, B1, B2: Base case- burnoff curves with 10% O2/He. T2: Step change in Oxygen concentration from 10% to 0%, Thermal annealed for 1 hour at 700°C with He only. T3: Pre anneal at 200°C in He and then oxidation in O2. ............................................65
xii
xii
Figure 4.3.8: Effect of Annealing time on Temperature for a specified fractional conversion, 5% NO2, 10% O2, Annealing Temperature: 700°C, Ramp rate: 5.8°C/min.....................................................................................................................................68
Figure 4.3.9: K impregnated NIST soot (Thermal Annealing) TPO Annealing T= 680°C, K/C mol ratio = 1:50....................................................................................................70
Figure 4.3.10: K impregnated Carbon (Thermal Annealing) TPO Annealing T= 680°C. Fractional Conversion versus RGo Plot ........................................................................71
Figure 4.3.11: Comparison of slopes of rate curves. Same conditions as Figure 4.3.9.......72 Figure 5.1.1: Functional groups on soot surface listed according to their thermal stability.
Acidity represents only the general trend. (Muckenhuber et al.35 )............................75 Figure 5.2.1: Timing events during typical TP ToFSIMS experiments, ta= acquisition time,
tr= ramp time, th = operator interruptions, ts= vacuum stabilization time....................81 Figure 5.2.2: Example of TP-ToFSIMS spectra for Negative Ions – Sample NIST-0,
Temperatures of spectra displayed: room temperature (~25 °C), 100 °C, 200 °C, 400 °C, and 550 °C. Y-axis: Intensity (log scale), X-axis: mass units (m/z, linear scale) 83
Figure 5.2.3: Effect of temperature on the intensity of each individual ion for sample SC_NOX negative ions. Panel A = high intensity, low molecular weight ions: Panel B = lower intensity, higher molecular weight ion intensities (unlabeled) to show variety of temperature dependent behaviour. ..........................................................................85
Figure 5.3.1: Elemental (C, H, O, N) SIMS spectral composition as a function of temperature for SC_NOX negative Ions......................................................................88
Figure 5.3.2: Elemental (C, H, O, N) SIMS spectral composition as a function of temperature for SC_AIR Negative Ions. .....................................................................88
Figure 5.3.3: Effect of NOX treatment on sucrose char. Difference in elemental mole fractions between NOX treated sucrose char (SC_NOX) and non-treated sucrose char (SC_AIR). Positive values indicate higher mole fractions in SC_NOX. Negative values indicate higher mole fractions in SC_AIR. ......................................................90
Figure 5.3.4: Elemental Composition Change (C, H, O, N) with Temperature of NIST-0 Negative Ions. ..............................................................................................................91
Figure 5.3.5: Elemental Composition Change (C, H, O, N) with Temperature of CAT-0 Negative Ions. ..............................................................................................................92
Figure 5.3.6: Elemental Composition Change (C, H, O, N) with Temperature of NIST-ANN Negative Ions. ....................................................................................................93
Figure 5.3.7: Effect of thermal annealing at 700 °C in He on NIST diesel soot. Difference in elemental mole fractions between non-treated NIST diesel soot (NIST-0) and thermally annealed NIST soot (NIST-ANN). Positive values indicate higher mole fractions in NIST-0. .....................................................................................................93
Figure 5.3.8: Difference in elemental mole fractions between NOX -treated sucrose char (SC_NOX) and non-treated NIST diesel soot (NIST-0). Positive values indicate higher mole fractions in SC_NOX. Negative values indicate higher mole fractions in NIST-0. ........................................................................................................................94
Figure 5.3.9: Difference in elemental mole fractions between non-treated NIST diesel soot (NIST-0) and CAT 3306 diesel soot (CAT-0). Positive values indicate higher mole fractions in NIST-0. Negative values indicate higher mole fractions in CAT-0. .......95
Figure 5.3.10: Example of identified peaks from TP- ToFSIMS data ................................99
xiii
xiii
Figure 5.3.11: Effect of temperature on Individual Ion intensity ratio for each sample. (Intensity scale x100) where 0= NIST-0, 1= CAT-0, 2= NIST-ANN, 3= SC_NOX, 4= SC_AIR......................................................................................................................101
Figure 5.3.12: Effect of NOX exposure on sucrose char. Comparison of the CN- ion between SC_NOX and SC_AIR. ...............................................................................102
Figure 5.3.13: Effect of NOX exposure on sucrose char. Comparison of the CHNO- and C3NO- ion between SC_NOX and SC_AIR ..............................................................103
Figure 5.3.14: Change in nitrogen containing ions during temperature ramping, Nitrogen, oxygen, hydrogen and carbon containing ions only shown, where N=I(x)/I(C2
-), Sample: SC_NOX. The order of the ions in the figure is identical to the list in the right hand margin.......................................................................................................104
Figure 5.3.15: O depletion of C, H, O only containing ions during temperature ramping. Sample:(SC_NOX) The order of the ions in the figure is identical to the list in the right hand margin.......................................................................................................105
Figure 5.3.16 (a-d): Examples of positive and negative correlations for ion pairs of Integral rate versus Temperature. Units: h-1 panels (a,b): positive, panels (c,d): negative....108
Figure 5.3.17: Identified curve shapes for rate versus T plots grouped into Sets for sample SC_NOX. y-axis is rate, x-axis is temperature.........................................................110
Figure 5.3.18: Contribution of each integral rate set with Temperature, Top left: All Sets, Top right: Magnification of top fraction showing Sets II, III, and IV. No contribution from Set V. Bottom centre: Magnification of bottom fraction showing Sets I and II. Sample: SC_NOX......................................................................................................112
Figure 5.3.19: Time-dependent isothermal intensity changes in normalized intensity (CH- /C2
-) for CH- at 25°C for sample SC_NOX negative ions. Top (a): All data during data collection, Bottom (b): The same date averaged over 10 primary ion pulses...114
Figure 5.3.20: Effect of temperature on differential rates for CH-, NO-, CHN-, and CN- ions (Sample SC_NOX) ....................................................................................................117
Figure 5.3.21: Examples of positive (top panel) and negative (bottom panel) correlations for Sample (SC_NOX) negative ions. .......................................................................119
Figure 5.3.22: PCA Loading Plots for Sample SC_NOX negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified sets within the ToFSIMS data.............................................................................................................................126
Figure 5.3.23: PCA Loading Plots for Sample SC_NOX positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the ToFSIMS data............................................................................................................128
Figure 5.3.24: PCA Loading Plots for Sample SC-AIR negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the ToFSIMS data.............................................................................................................................129
Figure 5.3.25: Top (a): Effect of temperature on the non-normalized intensity of each individual Set, Centre (b): Effect of temperature on the normalized Set intensity, Bottom (c): Surface conversion of each Set with temperature ..................................131
Figure 5.3.26: Effect of Temperature on the gasification rate for each ion Set leaving the carbon surface. Sample SC_NOX: NOX dosed sucrose char negative ions ..............135
xiv
xiv
Figure 5.3.27: Specific gasification reaction rate of individual ions for sample SC_NOX. The lines are included to make it easier for the reader to locate the data points and are not intended to show trends. ......................................................................................137
Figure 5.3.28: Reaction Scheme 1 .....................................................................................139 Figure 5.3.29: Examples of NO2 bonding to carbon .........................................................146 Figure 5.3.30: Reaction Scheme 2 - Generalized surface reaction mechanism with NO2
bonding via nitrogen atom to the carbon surface.......................................................147 Figure 5.3.31: Reaction Scheme 3 - Generalized surface reaction mechanism of NO2
bonding via the oxygen atom to the carbon surface ..................................................148 Figure 8.1.1: Sample location for Image Experiment 1.....................................................183 Figure 8.1.2: Photo Images of Experiment 2 .....................................................................186 Figure 8.1.3: Photo Images of Experiment 1 .....................................................................187 Figure 8.2.1: Sucrose char .................................................................................................190 Figure 8.2.2: CAT diesel soot............................................................................................191 Figure 8.2.3: NIST diesel soot...........................................................................................192 Figure 8.3.1: Raman spectra of SCV3a x-axis: wavenumber, y axis: intensity, dots= data,
solid line fitted peaks .................................................................................................197 Figure 8.3.2: Raman Spectra of SCVM_4 x-axis: wavenumber, y axis: intensity, dots=
data, solid line fitted peaks ........................................................................................198 Figure 8.3.3: D/G peak ratios of Raman spectra ...............................................................200 Figure 8.4.1: PCA Loading Plots for Sample CAT-0 negative ions: Loading plot (a) and
Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.............................................................................................................................201
Figure 8.4.2: PCA Loading Plots for Sample NIST-ANN negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data...........................................................................................................202
Figure 8.4.3: PCA Loading Plots for Sample SC_NOX negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.............................................................................................................................203
Figure 8.4.4: PCA Loading Plots for Sample SC_AIR negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicates identified groupings within the TOFSIMS data.............................................................................................................................204
Figure 8.4.5: PCA Loading Plots for Sample NIST-0 negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicates identified groupings within the TOFSIMS data.............................................................................................................................205
Figure 8.4.6: PCA Loading Plots for Sample NIST-0 positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.............................................................................................................................206
Figure 8.4.7: PCA Loading Plots for Sample CAT-0 positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured
xv
xv
by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.............................................................................................................................207
Figure 8.4.8: PCA Loading Plots for Sample NIST_ANN positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data...........................................................................................................208
Figure 8.4.9: PCA Loading Plots for Sample SC_NOX positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.............................................................................................................................209
Figure 8.4.10: PCA Loading Plots for Sample SC_AIR positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.............................................................................................................................210
List of Tables Table 3-1: Sample designation ............................................................................................36 Table 3-2: Prepared catalyst impregnated carbons..............................................................36 Table 3-3: Comparison of carbon content in NIST soot......................................................37 Table 3-4: ICP and PIXE analysis of catalyzed and non-catalyzed soots ...........................37 Table 4-1: Information on literature data sources used in Figure 4.3.1 below....................49 Table 4-2: Information on literature data sources in Figure 4.3.3 .......................................53 Table 5-1: List of Samples used in TPD-ToFSIMS analysis ..............................................79 Table 5-2: Integral rate sets, SC_NOX, Ions in bold are suspect. .....................................111 Table 5-3: Differential rate sets, SC_NOX. Ions in italics are suspect. ...........................118 Table 5-4: Max negative and positive rate categories, SC_NOX......................................121 Table 8-1: Catalyst impregnated carbon samples prepared for image analysis experiments
...................................................................................................................................182 Table 8-2: Estimate of oxidation for Experiment 1 Image Analysis (Peak Temperature 425
ºC) ..............................................................................................................................184 Table 8-3: Estimate of oxidation for Experiment 2 Image Analysis (Peak Temperature 350
ºC) ..............................................................................................................................185 Table 8-4: Sample designation of Raman Samples. # indicates file of Raman spectra and
that the measurement was repeated on a different part of the sample. ......................196
1
1 Motivation/Overview
1.1 Motivation Diesel engines are the workhorses for industrial, commercial and personal transportation and
also play a vital role in power generation. The combustion process in the diesel engine is
extremely efficient which provides excellent fuel economy and torque. Unfortunately there is
a major negative effect, the emission of diesel particulate matter (DPM); it is composed
primarily of carbon or soot with minor components of organic compounds from unburned fuel,
lubricating oil and inorganic compounds such as ash (inorganic minerals) and sulphur
compounds. Reduction of diesel particulate matter (DPM) emissions is of prime importance
for both environmental 1 and health concerns 2. These concerns have lead to many
governmental agencies legislating stricter emissions regulations (US EPA 3, the European
Union and others globally). In order to meet these regulations and prevent DPM emissions
into the atmosphere, the current accepted method of removing diesel particulate matter is to
trap the DPM using a filter in the exhaust. As the filter accumulates DPM, it builds up
backpressure that has many negative effects such as decreased fuel economy and possible
engine and/or filter failure. To prevent these negative effects, the trapped DPM needs to be
periodically removed by gasification/oxidation to carbon monoxide (CO) and carbon dioxide
(CO2). The ease of removal is governed by the intrinsic kinetics of carbon and is discussed in
greater detail in Chapter 2. The understanding of the fundamentals that govern carbon
reactivity is important to improve technology for carbon filtration and oxidation.
The filtration device systems have limitations that strongly depend on the duty cycle of a given
engine or vehicle. Duty cycles where the exhaust temperature is low for the majority of its
operation do not allow the regeneration of the filter as a consequence of normal operation.
Engines that spend a lot of their time at low speeds and loads (e.g. buses, garbage trucks, and
forklifts) make it difficult to oxidize the DPM trapped on the filter due to the low average
exhaust temperature and occasionally low levels of active oxidant such as oxygen or nitrogen
dioxide. Many investigators have studied the application of these filtration devices onto
engines and the regeneration of them 4-8. It is known that the regeneration of the filtration
2
device is dependent on its temperature, type of oxidants, engine make and model, and most
importantly the nature of the carbon particle 5. How the current technology for DPM filtration
performs is well described in the literature and is discussed in detail in Chapter 2.
The underlying limiting chemical process for regeneration of these filtration systems is the
oxidation kinetics of the carbon. The reaction kinetics is complex because the carbon’s
reactivity is dependent on its history (formation during combustion, residence time in the
filter), the morphology of the carbon particle and the functional groups located on the carbon
surface. Carbon oxidation kinetics and its mechanism have been heavily studied. As fully
discussed in Chapter 2, the reaction kinetics and mechanism are dependent on a variety of
factors such as the carbon structure, gas composition, and aging effects on the carbon structure,
attached carbon functional groups, catalytic impurities present with the carbon and others.
Functional group chemistry is central to the reactivity. Although there is a vast amount of
knowledge in this area, particularly for the carbon-oxygen reaction some important questions
still are not answered. For example, it is unclear what are the reaction mechanism and the
reaction intermediates formed on the carbon surface during its oxidation. The reaction of NO2-
carbon is faster than O2-carbon and plays a critical role in current DPM filter technology 9.
The NO2-carbon reaction has received less attention and its mechanism is less well understood.
Aging of carbon particles can reduce the reaction rate. Aging can refer to changes in structure
caused by thermal and also to poisoning effects on the carbon or in some cases specifically the
filter device by additional species in the exhaust. Information on thermal aging effects on
carbon present on filters is very limited 10,11. As discussed in Chapter 2, thermal aging effects
on carbon itself have been shown. However, information on the effects of thermal annealing
on soot and its implications on filter operation at low temperatures is lacking. Other species in
the exhaust have been shown to play a role. A recent study by Caterpillar 11 indicates that the
lube oil poisoning causes a loss in the activity of the carbon, mostly likely due to the lube oil
blocking the reactive sites of the carbon.
A considerable amount of information is available on DPM oxidation at temperatures from
250°C and above under real and simulated exhaust environment conditions. However, little
information is available on fundamental limitations of the technology with regards to carbon
3
oxidation rates at the low temperatures relevant to advancement of technology under low load,
idle engine conditions.
For example,
• Are all the reactive sites on the carbon being used to maximize the rate of oxidation?
• Is there a limit to this rate?
• Is this limiting rate sufficient to prevent accumulation of DPM inside the filter system?
This thesis will examine these fundamental issues to study carbon oxidation with a practical
goal of extending the low temperature limit of filter operation.
This thesis will:
i) Provide evidence towards the lower limits for carbon kinetics and practical limits
towards low temperature operation for real world systems.
ii) Investigate thermal annealing of carbon and its effect on carbon reactivity and its
relevance towards filter operation.
iii) Study the identity and reactivity of surface functional groups on the carbon
structure that could be involved in the carbon gasification/oxidation mechanism by
a new technique, ToFSIMS (Time of Flight Secondary Ion Mass Spectroscopy).
The three goals above are chosen to assess fundamental information towards the limitations of
DPM filter operation at low temperatures of 200°C and provide input towards achieving this
goal under real-life exhaust conditions.
4
1.2 Overview
1.2.1 Research Objectives
The two questions that are being addressed in this thesis are 1) can gasification rates at 200°C
be achieved that can maintain or reduce accumulated carbon loads in or on a filter media, 2)
can information be provided to better understand the fundamental parameters that affect DPM
gasification with and without a catalyst by studying the affects of change on active sites and
surface functional groups on the carbons.
1.2.2 Thesis Structure
The thesis is organized in the following manner:
• Chapter 2 contains introductory and background material. It is broken down into two parts:
o Section 2.1: A review of the fundamental information on carbons and what is
known about the carbon reaction mechanism and definitions of the fundamental
terms used throughout the document.
o Section 2.2: A review of the technology available and approaches used to oxidize
carbon on vehicles.
• Chapter 3 describes the material and the experimental procedures used in this study to
investigate reaction rates.
• Chapter 4 describes the results of the reaction kinetic studies under selected “standard”
conditions.
• Chapter 5 describes the surface group study by ToFSIMS.
• Chapter 6 reviews the conclusions of this study and how it helps advance the understanding
of carbon oxidation and its application to DPM filter technology. It also provides
recommendations on additional studies to advance the understanding of carbon oxidation
and its practical application to exhaust filtration devices.
• Several appendices contain additional information and exploratory studies that do not add
to main body of the thesis.
5
2 Background The process of carbon oxidation/gasification is critical for many applications. These
applications range from environmental issues (diesel exhaust after-treatment) to power
generation (coal gasification) and others. In the case of exhaust after-treatment and coal
gasification, the reaction of carbon + oxidant is used to produce carbon oxides, water and
energy. This apparently simple reaction of carbon + O2 to produce carbon dioxide or carbon
monoxide has been extensively studied for over 50 years and is still not fully understood. This
is mainly due to carbon’s complex structure and chemistry. The reaction of NO2-carbon is
faster than O2-carbon and is less well studied. The structural and compositional chemistry of
the carbon, various oxidants and catalysts has been found to affect the carbon conversion
process. Carbon oxidation with respect to fundamental understanding is reviewed below in
Section 2.1. Section 2.2 reviews the application of technology for exhaust filtration and the
current state of the art and provides a bridge to the required carbon oxidation kinetics.
2.1 Fundamental understanding of carbon oxidation
2.1.1 Carbon structure The structure of carbon is complex. The basic structure of carbon, graphite, consists of
trigonally bonded carbon atoms (Figure 2.1.1) forming a single plane or basal plane 12-15.
These are stacked in layers and have an interspatial spacing of about 3.5 angstroms 12. Carbon
types differ due to morphology changes caused by stacking of the aromatic layers and
imperfection within the layers. The layers of carbon sheets in soot can be visualized as being
similar to layers of an onion. As the carbon layers or particles grow, defects, distortions and
inclusion of atoms into the structure cause the carbon to become disordered (Figure 2.1.2) 12.
Peripheral carbons (“edges”) of the graphitic sheet are the reactive centres for the reactions
with carbon - trigonally bonded “interior” carbons are not. During the course of reaction the
edges can be populated with an array of functional groups. The reactive edges of each of these
layers can terminate with functional groups such as hydroxyls, lactones, carboxylic acid,
anhydrides, bridged peroxides, pyrones and many others 12,13,16,17 (Figure 2.1.2). These
oxygen-containing functional groups are reported to comprise up to 50% of the functional
6
groups on the surface of the carbon 18. These functional groups can be directly involved in the
reaction mechanism as intermediates resulting in liberation of CO or CO2. Conversely, these
functional groups can be “spectators” to the reaction and thus can modify the reactivity of the
active centres. The degree of disorder and type of surface functional groups can affect the
reactivity of the carbon by providing more surface area and a greater number of active sites.
Active sites are locations on the carbon where reaction with the oxidant is more likely to occur.
The concept of active sites is discussed in more detail later.
Figure 2.1.1: Oxygen containing functional groups on carbon
Carbon morphology is affected by temperature during formation. Diesel soot is formed by
short exposure times to high combustion temperatures (~1500°C and higher) (see Stanmore et
al.13 for greater details). These conditions produce a carbon particle whose morphology is
highly disordered. In this case, ordering refers to the stacking and the uniformity of the carbon
layers. Graphite is a highly ordered structure while diesel soot is more disordered. The
conditions that form diesel soot produce a carbon structure that has greater surface area than
graphite. This higher surface area may make diesel soot more reactive. Also, the fraction of
edge sites providing “reactive surface area” is strongly dependent on the thermal and reactive
history. The ordering of graphite and diesel soot can be visualized using the Marsh-Griffiths
model 12 (Figure 2.1.3). The model illustrates the graphitization process of carbons with
increasing temperature. The model shows that with exposure to higher temperatures the
carbon structure becomes ordered and thus the available surface area is reduced. For example,
diesel soot would have a structure similar to that on the far left of Figure 2.1.3, while graphite
7
would have a structure more like that on the far right. Vander Wal et al. 19 captured images of
the highly disordered nature of combustion soots using high resolution TEM. They produced
photos of various combustion soots for different fuels (e.g. propane, diesel, acetylene and
others). Images of all these combustion soots, especially diesel soot, showed high disorder in
the stacking of the aromatic layers in the structure. Particulate matter has differences in
graphitic nature as demonstrated by investigations using NEXAFS (Near edge x-ray adsorption
fine structure); it was shown that NIST SRM (National Institute of Science and Technology
Standard Reference Material) 1648 urban particulate matter (PM) is more graphitic than NIST
SRM 2975 forklift PM 20. Unfortunately, the effects of disorder and the details of individual
layer stacking on the reactivity of carbon are not very well understood. Quoting Mims21
discussing carbon structure and reactivity the possible important details are ‘1) the actual
chemical configurations of the domains and their deviation from ideal portions of a graphite
layer and 2) the inter domain bonding. Even for graphitised carbons, the number and types of
defects are not completely known.’ Disorder in the carbon structure is important in the
reactivity of the carbon due to availability of sites for oxidation; this will be discussed in more
detail later.
8
Figure 2.1.2: Representation of hexane soot showing defects and distortions from Smith et al. 18
Figure 2.1.3: Marsh-Griffiths model of graphitisation from Marsh 22
9
2.1.2 Identification of surface functional groups Identification of surface groups on the carbon surface and how they affect reactivity continues
to be studied. The nature and the quantity of surface groups formed depend on the history of
formation of the carbon surface, its surface area and the treatment temperature 23. Surface
functional groups on carbon and model compounds are identified with the use of a variety of
analytical techniques. These include chemisorption, chemical titration 16,24,25, infrared (IR) 26-
35, TPD-MS (temperature programmed desorption mass spectrometry) 35, x-ray photoelectron
spectroscopy (XPS) 17,36,37, SIMS (secondary ion mass spectrometry) and INS (inelastic
neutron scattering) 37. Chemical methods were found to not account for all of the chemisorbed
oxygen based on surface functional group estimation 38. Other attempts to use simple organic
compounds to predict surface groups and their reactivity are difficult due to possible
interaction between groups on the carbon surface 38. In the next paragraph is a brief summary
of the work performed in the area of surface group identification on carbons; additional
reviews can be found in Stanmore et al, Marsh, and Boehm. 12,13,16.
Chemical titrations have been used by a number of researchers to identify surface groups. It
has been proposed that three types of surface groups exist, acidic, neutral, and basic (Figure 4).
Early studies by Boehm24 used bases in increasing strength to classify the acidic surface groups
on carbon black and oxidized charcoal. These acidic surface oxides are formed when carbon is
treated with oxygen at temperatures up to 400°C. The acidic functional groups present on the
carbon are carboxylic, lactonic and phenolic, or a frozen layer of CO2 is proposed to render the
carbon surface polar in character (Bansal and Donnet and references therein 39). Rivin 40,41
combined acidimetry and vacuum pyrolysis technique to determine the distribution of
functional groups on several carbons and their attributed surface acidity to carboxylic,
phenolic, neutral lactone and to quinone groups. Decomposition of these surface groups is
reported to occur between the temperature range of 300– 800 °C under vacuum and inert
atmosphere by evolving CO2 39. Neutral surface oxides are formed by the irreversible
adsorption of oxygen at the unsaturated sites (ethylenic type) present on the carbon surface 39.
The oxygen atoms form a –C-C-O-O-C bond that decomposes into CO2 on heat treatment in
vacuum. The neutral surface oxide is more stable than the acidic surface oxide, and begins to
decompose in the temperature range 500-600°C 39. Basic surface oxides are also reported on
the carbon surface 16,25. Garten and Weiss proposed a pyrone structure (a heterocyclic oxygen-
10
containing ring with an activated =CH2 or =CHR group (R is an alkyl group) 42. Boehm and
Voll suggest a pyrone–like structure with the oxygen atoms located in two different rings of a
graphite layer 39. According to Montes-Moran 25, the thermodynamic stability and redox
potential of pyrone type structures makes them the primary source of basicity in basic carbons.
Figure 2.1.4: Illustration of acidic and basic groups on carbon. 25
Infrared spectroscopy is used by many investigators to study the surface functional groups on
carbon 18,26,30,43-45. A variety of IR assignments for various functional groups has been
determined, see Fanning and Vannice 30. These assignments are controversial (as discussed by
Boehm16), the band near 1600 cm-1 has been described as explaining stretching frequencies of
aromatic C=C bonds or could be describing a hydrogen-bonded, highly conjugated carbonyl
groups. Smith and Chungtai used IR to investigate reactions of NO2 and ozone with carbon
black in atmospheric type conditions (i.e. room temperature and atmospheric pressures) 18.
Detailed molecular characterization of surface chemistry of carbon has been pursued by many
techniques. These studies address static properties such as adsorption in addition to
gasification mechanism and kinetics. Quantitative information on the oxides present on carbon
surfaces has been collected using x-ray photoelectron spectroscopy (XPS) 17,33,46-48. The
various surface oxides present on carbon can be identified and estimated using the chemical
shift of the C1s peak. Vander wal et al. has shown the applicability of XPS to diesel soots 36.
Unfortunately getting information on concentration distributions of oxides on carbon using
XPS is difficult because of the small differences in binding energies for the different oxides
11
and the limited resolution of common XPS equipment 16. In addition, Boehm 16 suggests that
the results can be misleading when the exterior surfaces are more strongly oxidized by aging
on porous samples. Langley et al. 17 recently performed chemical derivatization reactions with
XPS to determine functional groups on a carbon surface. They were able to make functional
groups more readily observable by reacting fluorine containing groups onto the carbon.
SIMS (Secondary Ion Mass Spectroscopy) is a technique used to investigate surfaces. SIMS is
very surface sensitive and can measure the first one to two monolayers of the surface.
Furthermore, molecular ions can give rich molecular information. The SIMS technique
involves the bombardment of the surface with a primary ion. This ion collides with the surface
causing surface fragments (secondary ions) to be ejected from the surface that are sent to the
detector. State of the art SIMS is represented by Time of Flight Secondary Ion Mass
Spectrometry (ToFSIMS). As discussed in Chapter 5, ToFSIMS provides maximum
sensitivity and mass resolution giving the richest molecular information of the surface.
ToFSIMS also has a greater sensitivity than XPS. XPS sensitivity is in the 0.1% range while
SIMS can measure surface concentration at ppm levels 49,50. Albers et al. used SIMS in
conjunction with INS and XPS to study the hydrogen content and graphiticity of carbons from
diesel soot and various carbon blacks 37. The diesel soot was tested before and after an
oxidation catalyst in the exhaust stream and after extraction of the soluble organic fraction.
The C2-/C2H- and C2
-/CH- fragmentation ratio, collected on a Leybold IQ 12/38, was used as a
crude measure to distinguish between graphitic and poorly crystalline surfaces. Albers et al.37
suggests that the oxidation catalyst reduced the hydrogen content on the surface of the carbon.
This paper indicates that the use of a sensitive SIMS instrument could provide valuable
information on carbon surfaces. For a more detail review and description of the SIMS
technique see Briggs 49.
2.1.3 Carbon reaction mechanism
Carbon oxidation is dependent on the type of oxidant, its morphology, thermal effects and the
presence of catalytic materials to increase the reaction rate 12,13,21,51. How these variables affect
the carbon reaction mechanism is still not fully understood although much research has already
12
been performed and is discussed below. However, the reactivity of the different edge carbons,
the form of the reactive intermediate, and whether the attached functional groups on the carbon
edges affect the rate of oxidation remain unresolved. These factors may affect the active site
for reactivity of the carbon.
2.1.4 Active Sites
2.1.4.1 Reactive site concept (active site, reactive active site) As discussed earlier the edge carbons are the centre of carbon reactivity. Walker et al. first
introduced the concept of active sites to describe carbon reactivity in the 1960’s 52. This
concept was used by Boudart to describe catalytic reactions 53. An active site is an atomic or
molecular structure that is in an electronically favourable configuration for a reaction to most
likely occur on the catalyst. In the case of carbon these sites are located on the edges of the
aromatic layers 54 (See section 1 for details on carbon structure). These layers terminate to
form zigzag and armchair-type sites 55 and dangling carbon atoms 56. Computational methods
(Haynes and Sendt 57 and references therein) suggest that zig-zag sites are more reactive than
armchair sites (Figure 2.1.5).
13
Figure 2.1.5: Carbon structure identifying zigzag and armchair sites
Application of the active site concept to carbon is difficult. The number of active sites is
dynamic because they are involved in the reaction: the active sites are removed as CO or CO2
upon gasification/oxidation. Unlike an active site for a catalyst that is replenished, the carbon
active site is removed and it is unknown if a new active site is created immediately to replace
it. Thus, during the process of carbon oxidation, the number and type of active sites change as
the carbon is consumed. Calo and Hall 58 have stated that ‘…over time a consensus has
developed among workers in the field which focuses upon an understanding of the nature and
behaviour of “active sites” on carbon surfaces as the key to resolving this problem. It is
reasoned that if these sites and the surface complexes that occupy them can be identified,
characterized, and understood quantitatively, then the key to carbon reactivity would emerge.
Although it is difficult to argue with this hypothesis, the complexity of carbons and chars most
probably precludes the realization of ever completely knowing, to the degree necessary, the
detailed physico-chemical nature of the carbon surface and the surface complexes.’
2.1.5 Oxidant effects A variety of oxidants has been investigated for the carbon oxidation reaction. These include
molecular oxygen 52,59-73 on various carbons (eg. DPM, pyrolytic carbons, carbon black),
nitrogen oxides 35,74-79, ozone 80-83, gas phase ions produced by plasma, water 76, CO2 84 and
Zig-zag sites Armchair
sites
14
many others. In this section, the focus will be on the most common and readily available
oxidants in engine exhaust, oxygen and nitrogen oxides.
The kinetics and mechanism of oxygen reaction with carbon have been widely studied. A
variety of techniques has been used to elucidate the reaction mechanism and kinetic parameters
such as: IR (infrared spectroscopy), TPD (temperature programmed desorption), TGA (thermal
gravimetric analysis), and reactor studies. Reaction studies with oxygen are reported for a
wide range of carbon types from various coals to model compounds such as sucrose chars. The
kinetic parameters vary greatly depending on the type of carbon. Orders of reaction vary from
0.5 to 1.0 with activation energies spanning 102 kJ/mol to 210kJ/mol 13. Much of these
variations are due not only to the type of carbon but also the amount of contaminants present in
the carbon. In most cases, inorganic minerals play a role in catalyzing the gasification.
2.1.5.1 Oxygen reactions with carbon
A gap is present between the molecular structural information and mechanisms proposed in
relation to kinetics. Molecular information on the surface structure of carbon is incomplete.
This makes relating the structure to kinetics difficult to impossible. Typically for surface
reaction mechanisms or catalytic reaction mechanisms, the molecular conformation is not
specified and instead generic terms are used in kinetic models.
Many reaction mechanisms have been proposed for the reaction of oxygen on the surface of
carbon. In all cases, an oxygen intermediate is the proposed pathway to formation of carbon
monoxide or carbon dioxide. Haynes and references therein 85 describe two stoichiometrically
distinct surface reaction pathways: Type A reactions occur with the adsorption of oxygen
without gasification of the carbon substrate: C(_) + O2 C(O2) (two Type A O atoms
adsorbed). Type B chemisorption is favoured at higher temperatures and longer oxygen
exposures: C(_) + O2 C(O) + CO. Here, C(_) represents a free carbon site, C(O) represents
an oxidized carbon site with atomic oxygen and C(O2) represents an oxidized carbon site with
molecular oxygen. Biniak 86 proposed that superoxide ions O2- are formed on the carbon
surfaces by adsorbing molecular oxygen and use evidence provided by XPS 87 and IR 88 to
support their theory.
15
The following mechanism (Mechanism 1) was used by Haynes to develop a turnover model
that describes carbon site heterogeneity 85. Here, C represents a free carbon site and C(O)
represents an oxidized carbon site. The free carbon site (C(_)) reacts with gas phase molecular
oxygen to form an oxidized site (C(O)) and gas phase carbon monoxide (equation 2-1a). The
oxidized site can produce a free carbon site and gas phase carbon monoxide or carbon dioxide
(equation 2-1b). Also the oxidized site can react with molecular oxygen to form an additional
oxidized carbon sites and release carbon monoxide and carbon dioxide. The model was used
to describe how power law kinetics takes into account the intrinsic heterogeneity of the carbon
(Hurt and Haynes89 and references therein). It predicted data collected on Spherocarb 85,
polymer char 90,91, coal char 92 and graphite 93.
Mechanism 1
C(_) + O2 ….C(O) + CO {2-1a} C(O)…. C(_) + CO,CO2 {2-1b} C(O) + O2 C(O) + CO/CO2 {2-1c}
Other proposed mechanisms include additional intermediate reaction steps. The reaction
scheme (Mechanism 2) proposed by Marsh et al 12 involves a free carbon site Cf, chemisorbed
localized oxygen C(O2), chemisorbed mobile molecular oxygen C(O2)m, chemisorbed
localized atoms of oxygen C(O) and chemisorbed mobile atoms of oxygen C(O)m. Here, a
free carbon site reacts with molecular oxygen to form a C(O2) site or C(O2)m sites (Equation 2-
2a). The C(O2)m sites form C(O)m and/or C(O) sites (Equation 2-2b). Gas phase CO can be
released from C(O) and C(O)m (Equation 2-2c and 2-2d respectively). Also, gas phase CO2
and a free carbon site can be created from gas phase CO, C(O)m and C(O) (Equations 2-2e to
2-2h). Last, gas phase molecular oxygen can react with C(O) sites to form gas phase carbon
dioxide (Equation 2-2i).
16
Mechanism 2
Cf + O2 C(O2) or C(O2)m {2-2a} C(O2)m C(O) + C(O)m or/and C(O)m + C(O)m or/and C(O) + C(O) {2-2b}
C(O) CO {2-2c} C(O)m CO {2-2d} C(O)m + C(O)m Cf + CO2 {2-2e} C(O)m + C(O) Cf + CO2 {2-2f} CO + C(O) Cf + CO2 {2-2g} CO + C(O)m Cf + CO2 {2-2h} O2 +2 C(O) 2CO2 {2-2i}
Ahmed et al.71 suggest the formation of a stable carbon complex (Mechanism 3) where Cf
refers to an edge carbon atom (a free site), C(O2) refers to an adsorbed molecule before the
formation (of a stable surface complex) takes place, and (CO)c refers to the stable surface
complex. A free carbon site can react with molecular oxygen to form C(O2) (Equation 2-3a).
This C(O2) site can react with another Cf site to form the stable carbon complex (CO)c
(Equation 2-3b). These stable complexes can form gas phase carbon monoxide and a free site
(equation 2-3c). Or, react with a free site and gas phase molecular oxygen to form carbon
dioxide, another surface complex and a different free carbon site (equation 2-3d). The stable
carbon complex with C(O2) can form CO2, a stable carbon complex and a free carbon site
(Equation 2-3e). Or, can react with another stable carbon complex to form carbon dioxide and
a free carbon site. As well, Walker et al. 61 proposed that a fleeting carbon complex is formed
on the surface during the oxidation process. In all of these cases, the structure of the stable or
fleeting complexes that affect the reaction mechanism is not described. Again, the chemical
structure represented by these carbon oxygen complexes is unclear.
Mechanism 3
Cf +O2 C(O2) {2-3a} Cf + C(O2) 2(CO)c {2-3b} (CO)c CO(g) +Cf {2-3c} Cf + (CO)c + O2 CO2 + (CO)c + Cf {2-3d} (CO)c + C(O2) CO2 + (CO)c +Cf {2-3e} (CO)c + (CO)c CO2 + Cf {2-3f}
Even these “simplified” mechanisms speak to the complexity of the reaction and the lack of
detailed molecular knowledge.
17
2.1.5.2 NOX reactions with carbon Nitrogen oxides have been known for some time to increase the reactivity of carbon at lower
temperatures 9,13,74,75,94,95. This section discusses the information available on the reaction
kinetics of NO 9,13,94,96 and NO2 9,13,74,75 with carbon in the absence of a catalyst. In this
section, focus will be primarily on surface groups present on the surface of the carbon during
this reaction.
Nitrogen oxides are good reactants with carbon. Early work by Smith et al. 94 proposed that
oxygen surface complexes are formed during the reaction of NO with sucrose char. The
reaction of NO + O2 with carbon and in the absence of a catalyst is known to be slower than
NO2 but faster than O2 9,97-99. This observed higher reactivity of NO2 with carbon is the basis
of carbon oxidation for some commercialized exhaust filtration technology discussed later in
section 2.2. Kinetic studies indicate that C reacts with NO2 to form NO and CO as the main
product pathway 9,75,100. Reported activation energies for the temperature range from 180 °C to
350 °C are 50 to 86 kJ/mol 100-102. More recently, Bueno-Lopez 103 proposed the following
model (Mechanism 4) for the NOX –carbon reaction with oxygen for complete reduction of
NOX to N2 using model information from Yamashita (see reference Bueno-Lopez 103). Here a
free carbon site (Cf) reacts with molecular oxygen to form carbon-oxygen complexes ((CO)#)
that can decompose into carbon dioxide (equation 2-4a). All other carbons ( C ) can react with
these carbon-oxygen complexes ((CO)#) to form CO2 and additional free carbon sites (Cf)
(equation 2-4b). Nitrogen oxides react with free carbon sites (Cf) to give nitrogen gas and
carbon –oxygen complexes ((CO)#) (Equation 2-4c). Last, NOX with other carbons ( C ) and
carbon oxygen complexes ((CO)#) form carbon dioxide, nitrogen gas and free carbon sites (Cf)
(equation 2-4d).
18
Mechanism 4
Cf +O2 (CO)# {2-4a} n1C + (CO)# CO2 + n1Cf {2-4b} NOX + Cf ½ N2 + (CO)# {2-4c} NOX + n2C + (CO)# CO2 + ½ N2 + n2Cf {2-4d}
Where Cf are free sites or highly reactive unsaturated atoms of carbon, (CO)# are all surface
oxygen complexes that decompose as CO2 with the C to O ratio not necessarily being 1:1, C
represents all remaining C atoms not described by Cf and (CO)#. 103
Studies under atmospheric conditions show NO2 can interact with carbon (even at room
temperature) causing the formation of HONO intermediates 28,104-110. Early work performed by
Chughtai et al. 27, using FTIR at atmospheric conditions, showed that nitrogen-bonded
complexes are formed. From this information they proposed a dual path mechanism 27 that is
not discussed here. The nitrogen containing surface groups identified were C-NO2 and C-
ONO, as well as some other species
Muckenhuber et al 35 give evidence that an acidic functional group is formed that decomposes
into CO2 and NO at 140°C. Reaction of the NO2 with the carbon is proposed by them to react
directly with the surface and is not influenced by pre-existing surface groups.35 They propose
a reaction mechanism where two oxygen atoms from two different nitrogen dioxide molecules
produce CO2 and NO. Also, an acidic functional group is formed as an intermediate (acyl-
nitrite type) (O=C-O-NO). (Equation 2-5a,b)
Mechanism 5
C + NO2 C-O-NO {2-5a} C-O-NO ⎯⎯⎯ →⎯Tincrease C=O + NO {2-5b} C=O + C-ONO [O=C-O-NO]* CO2 + NO {2-5c}
Tomita et al 111 studied the high temperature (850°C) reaction between NO, pure carbon and
oxygen using isotope labelled reactants. They suggest that reaction between C(N) groups and
NO is the major route for N2 formation. In addition they propose that the O2 helps increase the
C(N) turnover by producing gaseous products.
19
Zawadski et al 106 studied the interaction of nitrogen oxides with the surface of the carbon
using in-situ FTIR between reaction temperatures of 295 to 573K. They observed that surface
species for both reactions of NO2 and NO-O2 were C-NO2, C-ONO, C-NCO and anhydride
structures. The authors state that the reduction of NO2 to N2 can be achieved with microporous
carbons without additional reductant; significant NO2 to N2 was observed at 623K. They
exposed carbon to NO/O2 (680ppm NO, 270 ppm NO2, and 5% O2) mixture at 473K and found
three distinct IR adsorption bands at 1851, 1782 and 1743 cm-1. They suggest that the bands at
1851 and 1782 cm-1 are due to the formation of lactone and anhydride groups. At 573K, the
mixture leads to the formation of surface oxygen compounds. Between 523 to 573K, the above
mixture leads to the formation of iso-cyanate species. The formed NCO species may come
from the reaction of NO and CO. Thermal stability of the surface functional groups was
checked after exposure to the mixture. The sample was heated to higher temperatures and the
sample analyzed using IR. Out-gassing to 723K indicated small amounts of surface
compounds are destroyed (1851 cm-1 and 1782 cm-1). At 773K, oxygen surface compounds
show minor destruction with no major changes in the NCO bands at 773K. At 873K, CN
surface species are decomposed.
As discussed above, the intermediates formed during the reaction of NOX with carbon are not
clear. Definitely, further study is needed to identify the influence of surface groups on the
reaction mechanism of NOX with carbon.
2.1.6 Structural effects on soot oxidation In the last few years, there has been a great deal of study on soot morphology and how it
affects the reaction rate of the carbon 10,19,36,112-121. Ishiguro et al 112 studied diesel soot
oxidized in air at various stages of burnoff. They found surface area increased with the
removal of SOF (soluble organic fraction) during early stages of burnoff. TEM images showed
the soot structure is turbostratic (i.e. has a wavy structure). The wavy structure is caused by
formation of 5 and 6 member rings and defects located within the layers during the soot
formation. Ishiguro et al 112 provides evidence of crystallites at the edges of the particles
flaking off instead of the particle becoming smaller. FTIR data of the different burnoff levels
20
show a decrease of the 1700 cm-1 (corresponds to the C=O group) with respect to the C=C
vibration at 1600 cm-1.
Vander Wal et al. 10,19,113 using HRTEM investigated oxygenated diesel fuel soots and found
that highly disordered carbon structures can be produced that are potentially higher in
reactivity. The highly disordered carbon structure exposes a greater number of edge carbon
atoms. As discussed earlier, these edge carbons are well established as the most reactive 52,54,122. HRTEM work has shown that thermal annealing (700°C) causes the outer layers of the
soot particle to become more graphitic 113. The images indicate the oxidation of the particle
proceeds from the inside to the outside. Su et al 119 and Muller et al 120 found that diesel soot
from a Euro IV heavy-duty diesel engine consisted of more fullerene type (onion-like)
structure and the soot started combusting at 573K. Further studies by Muller et al 120 compared
the reactivity of soots from four different sources in order of increasing graphitic nature: spark
discharged soot, Euro IV soot, soot from a smoking diesel engine and furnace soot. The soots
with lower graphitic content and thus more disorder in the carbon structure were more reactive.
Raman spectroscopy is used to show differences in the amorphous and graphitic behaviour of
carbons 123. The pioneering work of Tuinstra and Koenig 124 showed that variations in Raman
spectra were observed in different carbon materials. In this initial work, they observed a shift
of the intensities between the 1350cm-1 (D) peak and 1600cm-1 (G) wavenumber peaks. Of
these two main features, the 1350cm-1 is thought to relate to ”edge” carbons and the disorder of
the carbon structure, while the second feature at 1600 cm-1 is thought to relate to crystalline
graphite. The change of the D (1350cm-1) and G (1600cm-1) peaks gives an indication of the
graphitic nature of the carbon, while the D/G intensity ratio gives an indication of the degree of
disorder. Compagnini et al. and references therein 125 has provided evidence that the D peak
correlates with disordered carbons on graphite and is possibly related to edge carbons. The
application of Raman spectroscopy to graphite and amorphous carbon is available in review
articles by Ferrari 126 and Pimenta et al. 127. Also, the technique has been applied by a variety
of authors 118,128,129 to diesel soots. Further information on Raman can be found in Section 8.3,
Appendix C- Raman.
21
On a macroscopic scale, Higgins et al 130 developed an experimental method to extract surface
kinetic rates based on size-selected nanoparticles. A modified Arrhenius method gave
activation energy of 164 kJ/mol with different pre-exponential factors for each initial particle
size over the temperature range of 800 – 1200°C. They reported that the results agreed with
other published work.
2.1.6.1 Thermal aging Exposure of carbons to high temperatures is shown to negatively affect the carbons reactivity
on phenol formaldehyde resins 90,91 and coal 131-133 using high temperature anneals (> 900°C)
in an inert atmosphere. Suuberg et al. 90,91 show that the surface area of the carbon measured
using oxygen chemisorption decreases with increasing annealing temperature and with
exposure time. Possibly annealing of the carbon (DPM) in an exhaust environment may cause
loss of surface area and rearrangement of surface functional groups that could result in the loss
of active sites.
2.1.7 Catalyst effects Catalysts are effective in improving carbon reactivity. They work by creating new reaction
pathways that have rate limiting steps with lower activation energies and/or by the creation of
active sites. The review paper by Mims 21 documents that nearly all the elements on the
periodic table are effective at catalyzing the carbon oxidation reaction. Stanmore et al. 13
documents more recent catalysts used in DPF applications. Many catalysts 21,84,99,122,134-221 are
reported with a variety of structures and elemental compositions that have been used to
catalyze the carbon oxidation reaction.
Two factors that affect catalyzed carbon oxidation are the carbon catalyst contact and the type
of catalyst. Catalyst–carbon contact is a key element. Some catalytic elements are mobile
enough under reaction condition to ‘wet’ the carbon surface and effectively disperse their
activity and move to non-reacted carbons. Other catalysts remain as discrete particles but are
mobile enough to maintain contact with the carbon. More static catalytic elements require the
carbon to contact them. Moulijn et al. has described two modes of carbon catalyst contact,
22
loose and tight 201. Loose contact mode was found to best simulate contact of carbon and
catalyst in a DPF environment. Tight contact provides higher reaction rates and may
correspond closer to certain types of DPM oxidation catalyst, such as fuel borne catalysts 201.
Type of catalyst is important. The most active of these catalytic species are the oxides of the
alkali and alkaline earth group metals (Na, K, Ca) 84,167,168,183,184,187 and some transition group
metals such as V, Ce, Fe, etc. 163,188,204,208,211,213,214. The key to the reactivity of these elements
is their mobility (“wetting”) on the carbon structure allowing for excellent carbon-catalyst
interaction at the active site and providing intimate availability of oxygen (present in the
catalyst) at the carbon active site. Other catalysts (such as Pt) can catalyze the NO + O2
reaction to NO2. As discussed earlier the NO2 + C reaction is faster than the O2 + C reaction.
However, it is unclear if the presence of a catalyst improves the NO2 + C reaction or only
catalyzes the formation of additional NO2 through the NO + O2 reaction, thus indirectly
affecting the carbon reactivity.
Alkali metals have high mobility allowing for excellent ‘wetting’ of the carbon. They are
known to be effective coal gasification catalysts at high temperatures (>600°C) 84,167,168,170-
172,183,184,186,187,206,207. At temperatures greater than 300°C, the alkali metals become mobile and
are able to migrate across the surface and decorate the edge carbons. Once at the active site
they can directly supply oxygen to the active sites and initiate the formation of carbon
monoxide or dioxide. An intermediate in the reaction mechanism for K catalyzed carbons is a
phenoxide intermediate using a bridged K ion 21,206,207. Electron microscopy work by Yang et
al. 122, Baker et al. 208 and McKee et al. 170, show the migration of catalytic particles across
graphite crystals (‘wetting’) and the recession of the carbon edges. Bueno-Lopez et al. 103
report that NOX reduction increases from 3% on a non-catalyzed carbon sample to 63% with
4.2wt% K loaded bituminous coal using a gas stream of 0.5% NO + 5% O2 at 350°C. Van
Setten et al. 201 investigated alkali metals on diesel soot reactivity and its application to diesel
particulate filters. They report alkali metals, Cs and K based, have carbon ignition
temperatures of around 350°C. Unfortunately for DPM filters, alkali elements are very mobile
and tend to migrate into the cordierite filter structure and thus the catalytic material becomes
ineffective 220.
23
Noble metal catalysts are very active for the carbon oxidation reaction 209,214 and especially so
for nitrogen oxide containing streams. Pt based catalysts are known to catalyze the reaction of
NO + O2 to NO2 68,74,75,185. Tests with oxygen and a Pt catalyst report ignition temperatures of
400 – 425°C for the C + O2 reaction. As discussed earlier (Section 2.2.2), NO2 is able to
oxidize the carbon at a faster rate than oxygen and is used in current diesel particulate matter
filter technology for regeneration.
Inorganic metals are present in diesel soot. These may play a role in catalyzing the oxidation
of the carbon. However the presence of sulphur and phosphorus in the exhaust stream may
diminish their effectiveness by creating less catalytically active metal sulphates and/or
phosphates.
24
2.2 DPM filter technology and state of the art In this section, an update of the technological requirements is provided and links these needs to
kinetic requirements. Reduction of emissions of DPM (diesel particulate matter) is of prime
importance and has lead to many governmental agencies legislating stricter emissions
regulations. In order to meet these regulations, systems have been devised to trap the DPM
and limit its emission into the atmosphere 5,222,223. Techniques that are used to oxidize the
trapped solid DPM, primarily carbon into gaseous components, such as CO or CO2, include the
use of catalysts 5,224-228, creating oxidant (ozone 229, plasma 230,231, NO2 74,75,226,227) and the
addition of energy (fuel injection 232-234, electrical heating 235,236, microwave heating 237). All
of these systems have limitations and strongly depend on the duty cycle of the engine or
vehicle. Engines that spend a lot of their time at low speeds and loads have difficulty oxidizing
the DPM trapped on the filter. The underlying root of these systems is the oxidation kinetics of
the carbon and was reviewed in Section 2.1. In Section 2.2, a review is given of the
technology being implemented in real world applications.
2.2.1 Current legislation and technology
Diesel engines are important for industrial, commercial, and personal transportation because of
their excellent fuel economy and torque. Diesel particulate matter is a product of incomplete
combustion in diesel fuelled vehicles. It is also known that the DPM can cause environmental
and health effect problems 238-240. In 1996, the California Air Resources Board designated
DPM as a possible carcinogenic substance 241,242. The passing of tougher regulations and
lowering of fuel sulphur levels have provided favourable conditions for wider usage of diesel
particulate filters for the capture of the DPM for many years to come (Figure 2.2.1) 4,243-246.
Figure 2.2.1 shows current, past and future regulations for NOX and DPM emissions for on-
road engines for the European Union, Japan, and the US (Large squares). Anticipated engine
out emissions are shown using the dashed lines.
25
NOX and DPM emissions are inversely related. Engine manufacturers are able to influence the
exhaust emissions through ignition timing to cause either higher NOX outputs or DPM outputs
because engines can be tuned to meet the DPM requirement but would require SCR (selective
catalytic reduction) as per Euro IV regulations. Engines in the US have higher DPM emissions
and lower NOX emission and require DPFs to meet US 2007 regulations. Future regulations
will require some combination of both DPM filters and NOX reduction technology. Off road
regulations are similar to on-road regulations but have implementation dates two to three years
behind on-road regulations. Filters are complex to implement into vehicles. The filters must
be able to fit the limited space on the vehicle, have minimal fuel penalty, not create excess
backpressure on the engine and periodically must be cleaned or regenerated to remove the
DPM.
Figure 2.2.1: General comparison of on-road heavy-duty diesel (HDD) standards in the US, Japan, and Europe. Estimated engine-out emissions for 2007 and 2010 (range) are shown. Steady-state cycle. 243
Reprinted with permission from SAE Paper# 2006-01-0030 © 2006 SAE International.
26
There are two main types of filters available commercially, blocking and non-blocking filters.
Most commonly used are blocking filters that consist of wall flow or deep bed filters. These
types of filters force 100% of the exhaust flow through the filter media. Wall flow filters
(Figure 2.2.2) are highly effective and can filter 99% of the particulate matter 247. They are
made of cordierite (NGK 248, Corning 247) or silicon carbide (Ibiden 249, NoTox, Liqtech) or
metal fleece (Purem). They consist of parallel channels with alternating ends plugged, causing
the exhaust to flow through the porous wall and trapping the DPM. If the particulate matter is
not oxidized to remove the carbon (also known as filter regeneration), the accumulated DPM
on the wall flow filter can cause backpressure build-up on the engine causing engine stoppage,
failure of the engine or failure of the filter. Additionally increased backpressure can also be
caused by the accumulation of ash deposited on the filter from the lube oil. Non- blocking
filters (Figure 2.2.3) (Emitec 250, DCL 251) have an open structure and use exhaust flow
diversion and hydrodynamic pressure differentials across the filter media to force a portion of
the exhaust flow through the filter media. The open structure protects the engine by preventing
backpressure increases by bypassing the exhaust flow around the filter media if it becomes full.
The disadvantage is that the filtration efficiency is lower and can vary from 0 to 60%. For both
non-blocking and blocking filters the need to regenerate the DPM by oxidizing the carbon is
vital to maintain the operation of the device.
Figure 2.2.2: Example of exhaust flow through wall flow filter
27
Figure 2.2.3: Example of exhaust flow thorough (non-blocking) filter (DCL) 251 Reprinted with permission from SAE Paper# 2007-01-4025 © 2007 SAE International.
Diesel particulate matter is composed of three major components. The soluble organic fraction
consists of unburned diesel fuel and lube oil. Solid elemental carbon is the most significant
component by mass, and is contaminated with inorganics from lube oil blow-by and trace
amounts present in diesel fuel. Dihydrogen sulphate, made from sulphur in the fuel and lube
oil, makes up the rest of the particle. The proportions of these components have changed
greatly with engine improvements, changes in fuel sulphur and the use of ‘environmental
friendly’ fuels (biodiesel, Fischer Tropsch). The inorganics in the DPM may play a role in
catalyzing the oxidation of the carbon, however the presence of sulphur and phosphorus in the
exhaust stream may diminish catalytic effectiveness of these inorganic elements. A recent
review by Maricq 252 discusses chemical characterization and methods of analysis of diesel
particulate matter. Newer engines (>2007) are anticipated to produce a particulate with a
majority composition of elemental carbon in the form of soot due to the more efficient
combustion of the fuel. In addition, research has shown that morphology and microstructure of
the carbon changes with engine operating conditions 10,19,36,113,114,116,130 and the type of fuel
used such as biodiesel or Fischer Tropsch 115,118. Biodiesel was found to have greater surface
oxygen groups and was more reactive than Fischer Tropsch fuels 118. This makes the removal
and oxidation of soot at low temperatures one of the most difficult of the various emissions
regulations to meet.
28
Duty cycles and exhaust temperatures of diesel engines vary with application and engine
manufacturer 253. Normal operating temperatures of diesel engines typically range between
150°C (idle) to 600°C at full load depending on the engine and duty cycle of the equipment.
For example, a forklift might be operating 90% of its time at idle and 10% of its time at full
load where the temperature may not exceed 380°C. Similarly, a school bus picking up kids in
a neighbourhood may spend its entire time at idle or low load condition and never reach
sufficient temperatures for DPM burn off. Conversely, a mining hauler may operate 70% of its
time at idle and the remainder of the time the exhaust temperature may exceed 600°C because
it is transporting up a grade thus, giving an opportunity to regenerate the filter. If the DPM is
not removed from the filter, excess backpressure will develop and cause the engine to stall or
cause damage to the engine. In some cases, applying too much backpressure to the engine will
void the engine manufacturer’s warranty.
DPM can be collected using either a catalyzed or non-catalyzed diesel particulate filter (DPF).
Although DPF’s are able to solve many applications, the criteria of operating temperature and
duty cycle are applied to both catalyzed and non-catalyzed DPFs 253. DPM begins to burn in
diesel exhaust without a catalyst at around 600°C. Current, base metal catalyzed DPF’s have
been tested and found to give balance point temperatures of 380-420°C. Pt based DPF’s are in
the 350 to 400°C. Balance point temperature is a commonly used method of evaluation of
DPF regeneration. At this temperature the engine soot production rate is equal to the removal
rate of DPM or oxidation rate of the carbon. An example of a commercial DPF is the Johnson
Matthey (JM) patented catalyzed regeneration filter (CRT) 74,75,222. It uses an oxidation
catalyst (e.g. Pt/Al2O3) upstream of the DPF. The oxidation catalyst converts NO in the
exhaust to NO2. The NO2 enters the DPF and oxidizes the DPM. This has been reported to
remove DPM at a balance point temperature of 320°C, but is strongly dependent on the NO
content in the exhaust stream, the NO/NO2 thermal equilibrium, exhaust temperature and
sulphur content in the fuel. BASF’s (formerly Engelhard) DPX commercial filter is reported to
operate at temperatures of 225 °C under certain conditions 254. An added drawback for Pt
catalyzed technologies is the emission of high levels of NO2 at the tailpipe. Recent regulations
by California Air Resources Board (CARB) prevent increases of NO2 greater than 20% over
engine out emissions 255. Precious metal based filters are under scrutiny by the underground
29
mine community because of the higher NO2 emissions that reduce air quality. Both of these
devices (JM and BASF) have undergone extensive field trials. Although both systems can
operate well at times they have problems with low temperature/low engine load applications.
Under these conditions, the carbon overloads the filter and eventually ignites causing high
temperatures that melt the filter and cause failure. For the 2007 on-highway regulation, the
technology uses sophisticated methods to track the amount of soot in the filter and a burner or
engine modifications to cause regeneration of the filter that can lead to higher fuel
consumptions costs. Improved catalytic systems at these lower temperatures could potentially
solve this problem.
Solutions to low temperature applications involve complicated systems and controls, some
operator intervention, added energy and high maintenance (e.g. fuel burner, electrical heating,
air throttling, etc.) 256-261. The ultimate solution is a catalyzed DPF that needs minimal
maintenance, requires no additional power input, uses molecular oxygen, minimizes NO2
production, and is a “bolt and go” solution. Can catalyzed DPF’s be used for low
temperature/low load engine applications with the right catalyst? Extensive research has been
performed on different catalyst, but there have been no reports of a lower temperature limit on
catalyzed DPM oxidation. An important question to ask is whether or not the lower
temperature limit on catalyzed DPM oxidation has yet been reached.
30
2.2.2 Relating fundamental kinetics to engineering targets
2.2.2.1 Macroscopic reactivity requirements
Here the engineering target for a continuously operating filter is established. At this
macroscopic level, a gasification reactivity is required to continuously operate the filter.
Additionally, this reactivity is required at a target temperature. On this level, the macroscopic
gasification rate is defined as:
SG Cdt
dCR 1•−= (1/time = 1/h) {2-7}
Where dC = moles of carbon reacted, Cs = total quantity of carbon in the filter or reactor, t =
time, and dC/dt is the extensive gasification rate in moles per time. It must be noted that the
term (RG) has the units of a first order rate constant, but for solid reactants this specific
reactivity on a solid (not volumetric) basis is standard.
The value for the required macroscopic specific rate (RG) is based on soot emission outputs
from a modern diesel engine. The lowest raw emission rate from the 2007 engine output line
on Figure 2.2.1 was used as this value. This value is 0.02g DPM/bhp-hr (0.027g DPM/kWh).
An emission rate at this level would be considered an extremely clean engine based on the
2002 EPA standards (0.1g/bhp-hr for an on-road 2002 bus engine, off road about 10 times
higher). However, the standard was changed in 2007 to the current on-road emission
requirement of 0.01g/bhp-hr and thus would require at least a 50% reduction of particulates
and is achieved with the use of a filter device.
Figure 2.2.4 shows a 480 hp engine with this emission rate and thus gives 10g/h of carbon.
The emissions from this engine pass into a particulate filter where the carbon is trapped.
Another flow stream is shown leaving the filter. This stream contains gas phase carbon
oxidation products such as carbon monoxide and carbon dioxide and would be 10g/h on a
carbon basis under steady state conditions. The filter was sized to give a maximum
backpressure of 40” water on a 480 hp engine when filled with DPM (maximum of 60g). It is
31
assumed the DPM contains carbon only. However to allow for disturbances in emission rates
and duty cycle changes, the engineering target is based on 1/3 of this maximum filter hold up
of 60g, giving a value of 20g of carbon in the filter. Thus the gasification rate is 10g/h of
carbon (∆C/∆t) divided by filter hold up of 20g (1/Cs) to give a macroscopic gasification rate
of 0.5 h-1 under steady state conditions (RGo) and is the reactivity necessary for a low-
temperature passive system. It is approximately 50% of the instantaneous soot inventory
oxidized per hour (0.5 h-1)(Figure 2.2.4). This macroscopic gasification rate (0.5 h-1) assures
that the steady-state inventory of soot on the filter is at or below that required for continuous
operation.
Figure 2.2.4: Base case gasification rate criteria
A target temperature of 200°C is established as the temperature criteria for continuous steady
state regeneration of the filter. This temperature is higher than idle engine temperatures that
can be as low as 150°C. Under low load conditions it is assumed that the exhaust engine
temperature will meet this target temperature and is chosen to reflect low load, low temperature
applications discussed in Section 2.2.
Evaluation of experimental and literature information in light of the criterion above is done by
using a plot in Figure 2.2.5. The plot contains the gasification rate plotted on the y-axis and
inverse temperature on the x-axis. The horizontal and vertical lines represent the engineering
targets of the macroscopic gasification rate and target temperature. The shaded region in the
plot (Figure 2.2.5) indicates the target region for the carbon oxidation reaction to operate
within for typical diesel applications. Below 200°C (1.74e-3/K) is the desired operating target
Engine
DPF ~20g
Max: 60g
10.5” dia. x 12” DPF 480 hp engine 40” H2O ~3g soot/ litre filter
10g/h at steady state 10g/h at steady state
32
for low temperature diesel applications with a minimum steady state gasification rate (RGo) of
0.5 grams carbon oxidized/(grams of carbon initially in reactor or filter * hours) or (g/(g-h) or
(1/h). Later in Chapter 4, this plot is used to evaluate the reactivity of the carbon-oxidation
reaction with oxygen and nitrogen oxides with and without catalyst for both literature and
results acquired in this work. This plot is useful for both the overall gasification rate and also
to place the reactivity of surface functional groups in context of DPM technology.
Figure 2.2.5: Example of gasification rate chart indicating desired reaction region
33
2.2.2.2 Microscopic reactivity requirements
2.2.2.2.1 Definition of turnover frequency (TOF)
On a microscopic basis, the measure of site reactivity is the turnover frequency (TOF).
Turnover frequency is the rate of product molecules produced per reactive site and is defined
as:
SdtdCTOFG
1•−= (mol/time *1/site) (h-1) {2-8}
Here, S is the number of carbon reactive sites, and dC/dt is the extensive gasification rate.
If it is assumed that all the reactive sites on the carbon have the same reactivity the
macroscopic gasification rate is:
RG = TOFG * (S/C)
However, as discussed earlier in section 2.1 the edge carbons can be populated with a variety
of functional groups and other contaminants. Each of these can change the reactivity of the
carbon reactive sites and neighbouring carbon reactive sites and thus the carbon can contain a
number of reactive sites with differing reactivities. This makes the macroscopic gasification
rate a function of these multiple reactivities and is defined as:
RG = ∑(TOFG, i * Si/C)
Where, subscript, i, represents a single reactivity type
In the case of carbon these reactive sites are edge carbons located on the periphery of the
carbon sheets. Electron microscopy studies by Yang et al. 122, Baker et al. 208 and McKee et al. 170 use turnover frequency to report recession rates on carbon surfaces (i.e. carbon removal). It
was found that the recession rates of the basal plane atoms were significantly lower than the
edge carbons. The measurement of turnover frequency for disordered carbons is difficult and
34
are never reported in any application literature and rarely reported in laboratory bench scale
studies.
Additionally in section 2.1, it was discussed that the reaction of carbon can contain a large
number of individual steps that have various micro-kinetic parameters. This is not addressed
here, however ToFSIMS can provide information on individual surface reaction rate steps and
is discussed further in Chapter 5. Reactivity parameters are expressed as rate terms (ri, sims and
rg, sims) with units of h-1 and are based on methods discussed in Chapter 5.
35
3 Experimental procedure for reactivity studies
3.1 Soot characterization and catalyst impregnation
The chapter covers materials used and the method of preparation for the samples used to
address active site reactivity and surface functional group reactivity experiments. ToFSIMS
methods will be covered in Chapter 5 where these results are discussed.
3.1.1 Materials
Carbon samples used to study the reaction fundamentals of DPM gasification/oxidation are
shown in Table 3-1. They include a pure carbon made from sucrose char and two diesel soots.
Sample NIST, a National Institute of Science and Technology Standard reference material
(NIST SRM 2975), is diesel particulate matter collected from a diesel-fuelled forklift. Its
certification and specifications can be found in Appendix E. Sample CAT was collected at
DCL International Inc. on a Caterpillar 3306 diesel engine at an engine load of 200Nm, 1400
rpm and an exhaust temperature of 200°C
Sucrose char was prepared by slowly heating sucrose (Sigma S-9378 - Lot#: 22K0066). The
sucrose was heated at 1°C/min to 400°C and held for 2 hours under air in a muffle furnace.
Upon completion, the sucrose char produced was black and brittle. The char was ground using
a mortar and pestle to produce small particles of 300um.
Surface areas measured by BET N2 adsorption were 77m2/g and 12 m2/g for the NIST and
sucrose char respectively. SEM photos of the gross morphology can be found in Appendix B -
SEM. Sucrose char is macro-structurally smooth while possessing internal surface area. The
diesel soot image shows a lacy morphology from agglomeration of 20nm primary particles.
36
Table 3-1: Sample designation
Sample designation
Type of Sample
NIST NIST traceable diesel particulate matter SRM 2975 Sucrose Char (SC)
Sucrose char made by ramping temperature at 1°C/min to 400°C heating in air
CAT Engine soot collected on a CAT 3306 diesel engine at an exhaust temperature of 200°C, Engine Load 200 Nm, 1400 rpm
3.1.2 Impregnation of carbon samples with catalyst precursors Three standard catalytic elements were chosen from review of the relevant literature (see
Chapter 2). Intimate contact was the objective of the impregnation to achieve the maximum
possible reaction rate. The three catalysts are listed in Table 3-2. Through intimate contact
and high catalysts/carbon ratios a greater number of the carbon edge sites will be catalyzed.
Catalysts were prepared by impregnation of the carbons with soluble metal precursor solutions
and the preparation method is described in Appendix A.
Table 3-2: Prepared catalyst impregnated carbons
Sample Designation
Carbon Sample
Catalyst/Carbon mol ratio
Ion impregnated
Ion precursor
K-NIST NIST 1:50 K KOH V-NIST NIST 1:100 V Ammonium meta
vanadate Na-NIST NIST 1:50 Na NaNO3
3.1.3 Elemental characterization Elemental analysis was performed using PIXE, and carbon combustion at EAI – Elemental
Analysis Inc., Lexington, Kentucky. ICP analysis was performed by Chemisar Laboratories,
Guelph, Ontario. Carbon combustion results indicates carbon contents on the two samples
were similar between the NIST and the vanadium impregnated NIST with carbon content of
85-86% (Table 3-3). The inorganic component accounts for the remainder of the mass of the
soot. Carbon content of the CAT and K-NIST samples was not measured.
37
Table 3-3: Comparison of carbon content in NIST soot
Sample ID Carbon (wt%) NIST 85.3%
V-NIST 86.4%
The composition of the inorganic ash was measured using two methods: ICP (Inductively
Coupled Plasma Spectroscopy) and PIXE (Proton Induced X-ray Excitation). Samples for the
ICP were first combusted to remove the organic carbon and the ash was dissolved in aqua regia
for analysis. PIXE samples were examined as-is.
Table 3-4: ICP and PIXE analysis of catalyzed and non-catalyzed soots
CAT NIST K-NIST V-NIST NIST Element ICP, % ICP, % ICP, % PIXE, % PIXE, %
Na 2.20 14.40 2.34 0.00 0.00 Mg 0.78 6.00 0.48 0.00 0.00 Al 2.05 2.53 0.37 0.00 0.00 P 0.98 12.03 0.84 0.00 0.00 S N/A N/A N/A 62.60 30.63 K 0.27 1.56 93.17 0.00 0.00 Ca 8.26 10.90 0.92 3.42 14.31 V 0.00 0.00 0.00 4.93 0.00 Cr 2.29 0.00 0.20 1.55 4.74 Mn 0.80 0.00 0.00 0.00 0.00 Fe 75.82 11.10 0.84 20.03 23.85 Co 0.26 0.00 0.00 0.00 0.00 Ni 2.79 0.00 0.00 0.00 0.68 Cu 0.00 0.00 0.00 0.26 0.63 Zn 2.95 39.60 0.53 7.22 25.18 Ba 0.07 0.00 0.00 0.00 0.00 Ce 0.07 0.00 0.00 0.00 0.00 Pb 0.08 0.00 0.00 0.00 0.00 Tl 0.20 0.00 0.00 0.00 0.00 B 0.14 1.88 0.31 0.00 0.00
ICP results of the inorganic materials without S content are shown in Table 3-4 for the NIST
soot, CAT soot and NIST soot impregnated with K (K-NIST). The K-NIST sample contains
primarily K in the inorganic fraction. The major inorganic components found in the NIST and
CAT soot were Zn, Na, P and Ca. Ca, Zn and P are from lubricating oil 262,263. The CAT
38
diesel contains a high concentration of Fe (~ 50000 ppm). The presence of Fe in the CAT
diesel is due to the following: lube oil ash, wear in the engine cylinder or corrosion of the
exhaust pipe. The latter two choices are more likely due to the high concentration of Ni and Cr
present. Also, Ce was observed in trace amounts on the soot, the trace amounts could be from
the lube oil ash or a result of experiments performed with fuel additives on this test engine
about 10 years prior to the collection of the soot sample. All of these materials have elements
that have proven to have some catalytic activity towards carbon oxidation (see Chapter 2.4).
The above samples were investigated in the catalytic screening runs using the reactor.
3.1.4 Spectroscopic characterization of soot
Sample characterization was performed using various methods. Analysis was performed to
determine edge sites and functional groups found on the carbon surface. Raman spectroscopy
was used to determine qualitatively the presence of edge sites and the disorder of the carbon.
Time of flight secondary ion mass spectroscopy (ToFSIMS) was used to determine the
functional groups present on the carbon surface. The ToFSIMS experimental procedure is
described in detail in Chapter 5.
Raman spectroscopy was performed with mixed results using the micro-Raman instrument at
the University of Toronto’s Institute of Optical Science, laser spectroscopy department. The
technique, experimental procedure and results are discussed in Appendix C. Interestingly due
to the type of Raman instrument used in this experiment it was observed that the D/G peak
ratio (see Chapter 2.2.6) changed with laser exposure. This changing ratio is likely due to local
heating of the surface and possibly oxidation of the carbon creating edge sites. A search for a
spinning sample stage that would reduce the local heating effects on the carbon was
unsuccessful resulting in further experimentation being abandoned. In further chapters of this
document, results will be presented regarding thermal annealing of the carbons. An interesting
experiment would be to perform Raman under a He atmosphere and observe if the D/G peak
ratio changes with laser exposure. This could give interesting information toward surface
morphology changes on the carbon and possibly creation/destruction of edge sites.
39
3.2 Reactivity studies Brief screening studies were attempted to evaluate a larger suite of catalysts in parallel. These
are discussed in Appendix A, but are supplemental to the information in this thesis. Literature
indicates that the alkali metals and vanadium maintain higher catalyst-carbon contact by
“wetting” the carbon surface and being sufficiently mobile to maintain such contact during
reaction. Materials with “standard” catalytic elements were tested using the gas reactor shown
in Table 3-2. The methods used to prepare the samples and reactivity screening method using
image analyses are described in Appendix A.
3.2.1 Reactor system
The reactor, as shown in Figure 3.2.1, is composed of an up-flow reactor. Reaction products
from the reactor are monitored with a continuous FID (flame ionization detector) that is
discussed in detail later. Inlet gas reactants are introduced into the reactor system using two
mass flow controllers (Omega 5400) that feed 10% oxygen/He and He only respectively. The
mass flow controllers were calibrated using a Buck calibrator giving linear calibrations (Figure
3.2.4). A four-way valve is installed with inlets from the oxygen and He streams. One outlet
stream from the four-way valve goes to the reactor and the other to the CO oxidizer. The four
way valve allows for easy switching between the He and oxygen feed to the reactor. This has
two benefits. The first is having oxygen at the CO oxidizer. The second is having a constant
oxygen supply to the FID; this helps maintain constant flame chemistry and a constant
response during the switching experiments and minimizes flow disturbances. The CO oxidizer
contains a 5wt% Pt on Al2O3 catalyst that is held constant at 300°C. The catalyst is highly
active for CO oxidation with a light-off temperature of 160°C (light-off temperature is the
temperature where 50% conversion of the reactant occurs). This ensures complete conversion
of the CO to CO2. This is needed to ensure a constant response from the FID (Figure 3.2.2).
CO has a higher sensitivity factor on the FID detector than CO2. A mass flow controller is
installed to control the flow stream from the reactor to the GC sample inlet and a backpressure
valve is used to maintain a constant pressure in the reactor system.
40
Reactorwith Carbon andQuartz beads
Mass Flow ControllerTo FID
To Vent
CO Oxidizer/Pt catalyst10% O2/He
He
He
H2
Four-way valve
Relief valve
MethanizerPt Catalyst
NO/NO2
Figure 3.2.1: Reactor system setup
y = 44.378x + 8.68R2 = 0.9802
0
100
200
300
400
500
600
0 2 4 6 8 10 12 14
mV
ppm
C
Figure 3.2.2: Example of FID calibration
41
The SRI-GC (gas chromatograph) has been modified to act as an on-line continuous
measurement device. The GC column is removed and the reactor effluent is piped directly to
the FID after dilution with hydrogen and helium before the FID. Flows of gases to the FID are
matched closely to the original specifications for normal operation of the GC with columns
present. Two additional catalyst beds are present inside the GC oven; an oxidation catalyst (5-
wt% Pt/ Al2O3) is added and a methanizer (Ni/Al2O3) catalyst is part of the GC FID assembly.
An excess amount of hydrogen is fed to the GC stream as a source of reductant for the removal
of the oxygen over the 5-wt% Pt/ Al2O3 catalyst. Residual oxygen removal is needed to
maintain the methanizer catalyst (Ni/Al2O3) in the reduced state. The methanizer is used to
convert the CO2 to methane, which can be detected by the FID. In addition He is added to
dilute the stream and maintain flows to the FID similar to that when a column is installed in the
GC. The FID flame is maintained by using additional hydrogen and air feeds in the GC.
Flows to the FID and ratios of gases were set as close as possible to original flows with the GC
column installed in order to maintain equipment sensitivity. The gas ratios at the flame were
checked through calculation of various limiting conditions on the entire reactor system. The
graph indicates little variation in the gas ratios with the different gas concentrations and flows
used for all experiments (Figure 3.2.3).
Figure 3.2.3: Calculated FID flame chemistry limiting O2 cases
42
3.2.2 Reactor loading
Carbon samples diluted with Aldrich–325 mesh SiO2 were loaded into quartz reactors. Sample
carbon /diluents ratios were 0.04 to 0.08. Carbon samples of about 3 mg were measured on a
microbalance (Mettler Toledo Model #AJ100) with 0.0001 g resolution. The reactors were
washed with nitric acid and then rinsed with deionised water to remove any alkalis from the
surface before loading the carbon samples. Quartz wool is used on both ends of the sample to
prevent entrainment of the sample in the gas stream. A type K, 0.2mm O.D., thermocouple
was placed inside a quartz 1/8” O.D x 0.25mm I.D. sheath. The thermocouple was positioned
0.5 cm above the reactor bed. The reactor furnace was controlled using an OMEGA CNi3244-
C24 controller with temperature feedback from the thermocouple. Temperature data were
collected every 5 seconds using an Omega OM-CP-Quadtemp temperature data logger.
The reactions were run via one of the following experiments described below. All experiments
began by warming up the gas flow meters for a minimum of ten minutes and setting desired
flows. Typically, total gas flow through the reactor was held constant at 15 cc/min giving a
space velocity through the reactor bed of 7000h-1 (STP). Deviations from this flow are
described in the individual experiments. At the beginning of the experiment, helium flushed
residual air through the reactor system giving a peak on the detector attributed to atmospheric
CO2 levels of about 370ppm undiluted.
43
Figure 3.2.4: Example of flow controller calibration
3.2.3 Data analysis
Collected FID traces are calibrated by correcting the calibration factor to give a total carbon
conversion of 99%. All runs are to complete burn off of carbon. This is verified by increasing
the reactor temperature and monitoring when the FID signal returns to baseline at the end of
the experiment. Visual inspection of the reactor tube also confirms complete reaction of the
carbon samples. Calibration of the FID detector by diluting a 1%CO/1%CO2/balance He
mixture gives a linear curve for the analysis ranges of the experiments described here. At
maximum CO2 production the oxygen conversion is 10%; this maintains a differential type
reactor. At high carbon conversion (~90%) the calculated rate data (RG) are not very reliable
due to possible mass transfer effects and the small carbon quantities 264.
44
4 Reactivity Studies
In this section soot and carbon oxidation rates are measured and discussed to address two
questions:
1) Can low temperature oxidation occur at sufficient rates to satisfy the criteria for steady
state operation of DPM established in Chapter 2? In addition to published data,
experimental data are presented at selected conditions and compared with these
requirements. The experimental data cover steady state rates at isothermal conditions
as well as temperature programmed oxidation procedures. A brief catalyst-screening
program is described in Appendix A.
2) Are there time-dependent changes in the reactivity of soot? Here, information is
presented to address the effects of temperature and gas composition changes on the
reactivity of carbon, eventhough the reactivity of carbon is a function of many
parameters. In other words, does annealing affect the reactivity of carbon?
4.1 Introduction
In this study, two approaches were used to evaluate catalysis of the carbon oxidation reaction
to meet steady state gasification rates at 200°C. The first is a survey of published catalyst
reactivity and a comparison to the gasification criteria discussed in Chapter 2.7. The second
involves evaluating two catalysts for their maximum reactivity. The two catalysts (Na-NIST
and K-NIST) were chosen because of their high reactivity as displayed in screening
experiments and literature surveys. Early reports by various authors show that alkali metals are
more reactive than other base metals for the oxidation of carbon with oxygen feed streams 21,167,170,207. The most promising catalytic candidates for meeting the required reaction rates for
the criteria in this thesis are the alkali metals. Alkali metals have high mobility and tend to
decorate the edge carbons where the carbon is the most reactive 207. However, alkali metals are
not attractive for DPF type systems due to their mobility and tendency to migrate into the
cordierite material 201,220. Nevertheless, due to their high initial reactivity we have tested these
materials and others (see Chapter 3 and Section 8.1:Appendix A for preparation and rapid
screening experiments, respectively) for their ability to catalyze the O2- carbon and NO2 –
carbon reactions and to meet the required criteria levels using screening experiments. The
45
results of rapid screening evaluation experiments indicate that these two catalysts (Na-NIST
and K-NIST) show the best low temperature performance and are further discussed later in this
chapter.
Base case experiments were performed in oxygen and nitrogen oxide atmospheres with non-
catalyzed NIST and catalyzed NIST soot (K-NIST, Na-NIST) using temperature programmed
oxidation experiments. These types of experiments involve reacting the carbon sample with an
oxidant and ramping the temperature at a known controlled rate. The data collected in these
experiments were plotted on the previously described gasification plot (Figure 2.2.5) to
determine if they meet the criteria levels of normalized rate (RGo) of 0.5 at 200°C. As
discussed in Chapter 3, the catalysts are applied to the carbon by wet impregnation and
correspond to a tight contact 201. This gives maximum rates and allows the testing of the
criteria limit established in Chapter 2 of RGo = 0.5 at 200°C.
Furthermore selected experiments were performed to evaluate annealing effects on the carbon
samples. The thermal history of the carbon can influence the reactivity of carbon as shown in
Senneca et al. 131-133. Exposure of the carbon to high temperatures can cause loss of surface
area and loss of functional groups on the carbon surfaces and edges. In the oxidizing
atmosphere of a particulate filter the soot can be reacted with oxygen or possibly annealed to
remove sites for oxidation. Both of these processes could occur simultaneously and would
affect the reaction rate of the carbon. Information on low temperature thermal annealing of
carbon is difficult to find in the literature, however high temperature annealing (>900°C) has
been investigated on coal chars and formaldehyde resin chars 90,131-133. Thus, a preliminary
investigation to evaluate the influence on the reactivity of the carbon used in this study is
discussed here. These experiments include gas composition changes performed under
isothermal conditions as well as temperature excursions under non-reactive gas conditions.
46
4.2 Overview: This chapter is organized in the following sections:
4.1) A survey of published carbon reactivity measurements and a comparison of these
rates to the reactivity targets discussed in Chapter 2.
4.2) Temperature programmed oxidation studies of the reactivity of the thesis samples to
compare with published data in 4.2.
4.3) Temperature programmed oxidation studies of the thesis carbon materials under
various NO2 and O2 atmospheres. These conditions are chosen to achieve the
maximum rate expected in typical diesel exhaust.
4.4) Temperature programmed oxidation experiments with selected catalysts known to
maintain intimate contact with carbon. These catalytic rates are compared to the
criteria discussed in Chapter 2.
4.5) Thermal annealing experiments to test the effect of annealing period in inert gas on
the isothermal reactivity of the carbon materials.
4.6) Temperature programmed oxidation experiments to test the effect of annealing on
the reactivity of selected catalyzed carbon samples in a NOX gas atmosphere.
4.3 Published carbon reactivity studies
While the kinetics and mechanism of carbon oxidation have received a great deal of research
attention over the years, there are little relevant data for carbon reactivity under the conditions
of interest here - namely catalyzed oxidation at low temperatures near 200°C, particularly with
species other than molecular oxygen. The existing reports are diverse and include (1) ignition
temperature measurements where steady state rates are difficult to extract 265-267, (2)
temperature programmed reaction measurements which allow better inter-comparison of
oxidation catalysts and atmospheres, but absolute rates are not readily available from these
studies. Recently, a few papers have been published to address the kinetics of diesel soot: O2-
DPM 68-70, NO2, O2 + DPM 268,269 and NO2, O2 + DPM with catalyst 76,77,270. In addition, many
of the studies that use engine exhaust provide clear measurements of the efficacy of a given
system, but they are difficult to relate to other measurements because of incomplete
47
characterization of the carbon mass, the gas atmosphere, or other key reaction condition
parameters.
4.3.1 Survey of literature carbon oxidation rates with NOX
Table 4-1 collects the published studies used here. This literature can be classified in two
categories: 1) Measurements of gasification rates, RG, as described in Chapter 2.2) The
measure of site-specific rates or turnover frequencies (TOF). The latter include microscopic
studies of the edge recession rate on graphite. Here, gasification rates are calculated from
literature values and plotted on the gasification plot. Although the catalytic literature is
diverse, the study takes literature data that lend itself to this type of analysis 99,271-274 (Figure
4.3.1). Only data using NO + O2 and NO2 have been included in the analysis because NO2 has
been shown to be more active than molecular oxygen 74,75. The literature covers catalysts with
reported good contact 99, various base metals, precious metal catalysts, combinations of the
above, as well as two non-catalyzed NO2-carbon studies 271,272 (see Table 4.1). The table also
indicates the carbon sources that include carbon black, graphite and various sources of diesel
soot. Typical minimum data used to complete this analysis are rate data with partial pressures,
the catalyst type, oxidant types, flow rates and reaction temperatures, and carbon mass.
Analyses include interpolating rate data, extracting rate data from TPR (Temperature
Programmed Reaction) data through leading edge analysis, and determining rate data from
TGA (Thermal Gravimetric Analysis) data. Very rarely are actual rate expressions reported in
the literature 74,75,199,275 . Many papers report only peak temperatures or some do not indicate
the catalyst type 136,140,144,146,276. These incomplete studies are not included in this analysis. In
other papers, rates are calculated from the slopes of mass carbon removed per time at known
temperature or taken directly from rate versus temperature curves. These derived rates are
normalized to the starting carbon mass or area under the rate versus time curve. In order to
compare the literature values on the same basis, the derived rates are extrapolated to 200°C by
assuming Arrhenius temperature dependence and corrected for different NOX gas compositions
by normalizing the rate to 1000ppm total NOX (NO + NO2) assuming first order kinetics.
Whenever possible calculation of the steady state gasification rate (RGo) was performed at low
conversions of carbon or using initial rate data. In cases where data are expressed in arbitrary
48
units, for example Matyshuk et al. 277, for temperature programmed oxidation (TPO), rates are
calculated at certain temperatures and then normalized using the calculated area under the
curve. These data are plotted in Figure 4.3.1.
The shaded region in Figure 4.3.1 indicates the target reactivity region established in Chapter
2. Below 200°C (1.74 e-3 /K) is the desired operating target for low temperature diesel
applications with a steady state gasification rate of 0.5 (RGo). The criterion used here was
presented in Chapter 2. This plot indicates that few of the catalysts used in this analysis meet
this low temperature requirement though some are close. Interpretation of this figure is
complicated by the differing reactant feed streams that contain varying concentrations of NO,
O2 and NO2. It is unclear whether the presence of the catalyst is catalyzing the NO + O2
reaction to NO2 and then the NO2 is reacting with the carbon or if the catalyst is helping the
NOX - carbon reaction.
What is certain is that the reactivities reported here are insufficient for continuous low
temperature operation. The reason is not clear - is the catalyst poorly distributed onto the
active carbon sites, or is the inherent reactivity of the catalyzed sites insufficient? Two
exceptions are the data labelled C and F in Figure 4.3.1. Both of these use Pt based catalysts
that are likely catalyzing the reaction of the NO + O2 reaction to NO2 and thus make the
concentration of NO2 present in the reactor uncertain. Chu and Schmidt (labelled A in Figure
4.1)271 have measured the reaction rate in nitrogen oxides of active edge carbons in graphite,
using microscopic examination of the edge recession rates or turnover frequencies. These
values were not converted into gasification rates, RG, but can be roughly estimated using
site/carbon ratio of 10% 61. Thus, the gasification rates for Schmidt’s work would be about 10
times less than the turnover frequencies presented on Figure 4.3.1. This microscopic work
follows earlier work of Thomas et al. 208, Yang et al. 122,204,213 and others 163,209,214-216,278,279 at
higher temperature conditions and with other oxidants. These rates are difficult to extrapolate
to soot carbons, but are instructive and can be used to evaluate various catalysts 216.
49
Table 4-1: Information on literature data sources used in Figure 4.3.1 below
Label Catalyst Oxidant Carbon Source Analysis Reference
A None NO2 Highly oriented crystalline
graphite
STM and isothermal
heating Ind. Eng. Chem. Res. 32 271
B None NO2 Printex U TGA and Gas analysis
J. Chem. Tech. Biotec. 75 213 272
C Ce/1wt%Pt/Si-Al 250ppm NO + 10%O2 Lister Petter LPW2 NA diesel
with fuel additives Flow reactor Cat. Tod. 53 623 99
D Fe/1wt%Pt/Si-Al 250ppm NO + 10% O2Lister Petter LPW2 NA diesel
with fuel additives Flow reactor Cat. Tod. 53 623 99
E Pt/SiO2 1000ppm NO2 + 10%
O2 + 7% H2O Carbon Black TPR App. Cat. B 30 259 273
F MoO3/SiO2 1000ppm NO2 + 10%
O2 + 7% H2O Carbon Black TPR App. Cat. B 30 259 273
G V2O5/SiO2 1000ppm NO2 + 10%
O2 + 7% H2O Carbon Black TPR App. Cat. B 30 259 273
H La1.9K0.1Cu0.95V0.05O4 0.5% NO, 5% O2, He Diesel soot TPR Cat. Tod. 27 107 274
I Cu/V/K/Cl/Ti 1000ppm NO Soot from burner TPO Cat Tod. 60 43 280
J Cu/ beta 600ppm NO, 1500ppm C3H6 + 5% O2
Charcoal TPR Cat Tod. 119 262 177
K Pt/ beta 600ppm NO, 1500ppm C3H6 + 5% O2
Charcoal TPR Cat Tod. 119 262 177
L KCu/ beta 600ppm NO, 1500ppm C3H6 + 5% O2
Charcoal TPR Cat Tod. 119 262 177
N CePrOx 600ppm NO + 10% O2 Printex U TGA and flow reactor Top. Cat. (42-43) 221 281
O Pt/Al2O3 600ppm NO + 10% O2 Printex U TGA and flow reactor App. Cat. B 72 299 282
P Ru/NaY 600ppm NO + 10% O2 Printex U TGA and flow reactor App. Cat. B 72 299 282
Q KNO3 (11)/ZrO2 1500ppm NO + 8% O2 Soot (Type not indicated) TPO Cat. Comm. 4 124 193
R La0.8Bi0.2MnOx 0.5% NO2 + 10.5%O2 Diesel soot TPO Kin. Cat. 47 400 277
50
50
B
C
D
E
F
G
H
I
M
O
P
Q
A
J K
N
L
0.0000001
0.000001
0.00001
0.0001
0.001
0.01
0.1
1
10
100
1000
10000
1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 2.60 2.80 3.00
Inverse Temperature (1/K)
RGo
(mol
C/(h
*Co)
)
Target ReactionRegion
Figure 4.3.1: Literature survey of gasification rate data for catalyzed carbon reaction with NOX. Data normalized to total NOX values of 1000 ppm
50
51
4.3.2 Temperature Programmed Oxidation Experiments in O2 atmosphere (with and without catalyst)
Experiments using temperature-programmed oxidation (TPO) were performed to obtain initial
gasification (oxidation) rates of the thesis carbons and provide a baseline for comparison with
subsequent data. The samples were temperature ramped at a rate of 5.8°C/min from room
temperature to the final reaction temperature of 650°C in a 10% oxygen gas stream. The high
oxygen content (10%) is near the upper limit of oxygen concentration in diesel exhaust and
provides an upper limit for the carbon oxidation rate. TPO was used primarily as a screening
tool. The parameters of sample size and ramp rate were kept constant. The quantative rate
data used later were taken from the initial low conversion part of the curve that has minimal
mass transfer effects. For information on parameters that affect TPO see Querini and Fung283
and Redhead284. Figure 4.3.2 shows a typical temperature programmed oxidation experimental
run, rate evolution and temperature with time. From this data the gasification rate (RG) at each
time is determined. Data are plotted on an Arrhenius type plot (gasification plot) (Figure 4.3.3)
to compare to the target criteria.
-2.00E-06
0.00E+00
2.00E-06
4.00E-06
6.00E-06
8.00E-06
1.00E-05
1.20E-05
1.40E-05
1.60E-05
1.80E-05
0 20 40 60 80 100 120 140 160
Time (min)
dC/d
t (m
oles
car
bon
evol
ved
per m
inut
e)
-100
0
100
200
300
400
500
600
700
Tem
pera
ture
(°C)
Figure 4.3.2: Example of temperature programmed oxidation experiment. Sample NIST, 10% O2
52
The non-catalyzed NIST diesel soot–oxygen reaction rate data are compared to selected
literature data 68-70 in Figure 4.3.3. The curves (heavy lines) shown on the gasification plot are
from the present study while the literature values (symbols with thin lines) do not fall within
the target region for low temperature operation. The individual runs for NIST diesel soot
reacted with 10% O2 are very similar with an average temperature of 505°C at RG = 0.5. In
addition, the data collected in this study show good agreement with the reaction data collected
by Yezerets et al 69,70 in the temperature ranges that they studied. Yezerets et al. studied three
different engine soots with temperatures varying from 410°C to 530°C for RG = 0.5 and
varying oxygen conversions (3 to 25 vol%). Their data show a wide variation in soot reactivity
in their materials. A second feature of the curve for the NIST, O2 data is the slight plateau in
slope at 1.8 x 10-3 1/K. This curve is observed in the carbon oxidation literature and in this
case may be attributed to the soluble organic fraction of the soot 285, which contributes to a
faster rate at lower T but which is no longer present at the higher temperatures
The addition of a potassium catalyst to the soot (K-NIST) causes the C + O2 reaction to occur
at lower temperatures (~200°C lower than non-catalyzed soot (~600°C)) (see Figure 4.3.3).
An improvement in rate of a maximum of 120 times over the non-catalyzed soot sample is
observed. The slopes of the catalyzed curve and the non-catalyzed samples are similar
indicating little change in the apparent activation energy. Despite this increase in activity, the
rates with K catalyst are still too slow. These rates from the tight contact K catalyst in this
thesis compare well with other tight contact K supported materials with a carbon/catalyst
weight ratio of 1/10 (labelled S, T, U, Table 4-2), their rates are also too slow. Comparison of
this thesis’ tight contact K-catalyst to literature tight contact K2O (carbon/catalyst weight ratio
of 1/10, labelled W, Table 4-2) shows that better contact improves activity.
53
Figure 4.3.3: Gasification rate plot with TPO O2 data: NIST soot and K-NIST (K/C mol ratio = 1/50) compared to Yezerets et al. 2003 70, 2005 69 data; reaction conditions: 10% O2, 7000 h-1, ramp rate 5.8°C/min, Symbols with thin lines are literature values. Heavy lines represent thesis experimental data.
Table 4-2: Information on literature data sources in Figure 4.3.3
Label Catalyst Oxidant Carbon Source Analysis Reference
R KNO3/MgO 21% O2 Carbon Black (Monarch 430) TGA and Gas analysis App. Cat. A (2006) 314 81 155
S K/CeO2 10%O2 Printex U TPO Cat Comm 8 1274 219
T K/Ce0.5Zr0.5O2 10%O2 Printex U TPO Cat Comm 8 1274 219
U K/ZrO2 10%O2 Printex U TPO Cat Comm 8 1274 219
V CeO2 10%O2 Printex U TPO Cat Comm 8 1274 219
W K2O 10%O2 Printex U TPO Cat Comm 8 1274 219
54
4.3.3 Reaction testing of soot and catalyzed soot in NO2 atmosphere
Similar experiments were performed with NOX present in the gas stream. As a limiting case, a
high concentration of NO2 is used to test the criteria needed for steady state conversion at
200°C. A gas stream containing 5% NO is mixed with 10%O2/He to give a gas mixture of
2.5% NO, and 5% O2. The NO2 concentration is given by the reaction of NO + O2 NO2.
NO2 compositions are calculated using the equations of Glasson and Tuesday 286 and
information from the review by Tsukahara et al. 287. The concentration at the entrance of the
reactor bed is estimated based on the residence time from the mixing point of the reactant gas
streams to the soot bed (~ 10 seconds) but is uncertain. It is estimated that the concentration of
NO2 was a minimum of 10000 ppm. Due to the large concentrations and the uncertainty of
time the concentration of NO2 is estimated at 8000 to 12000ppm. This NO2 concentration is
obviously not a realistic NO2 value in engine exhaust (typically 1000 ppm) but is used here as a
limiting case to help establish if soot oxidation can meet the criteria.
Figure 4.3.4 compares the reaction rate in NOX to the O2 results. Addition of NO2 causes an
increase in the rate of carbon oxidation and shifts the burn-off rate curve to much lower
temperatures. Based on the criteria for steady state operation the gasification rate (RGo) of 0.5
is at 315°C. This implies that using a high concentration of NO2 cannot meet the criteria level,
although this gasification rate temperature is very high compared to others. Other workers
have reported filter balance point tests on engines as low as 250°C with NO2 223 . This could
be a result of lower engine-out carbon emissions.engine or a higher carbon oxidation rate, but
since these tests do not report engine-out carbon emissions it is impossible to compare to our
results. One possible reason for a different oxidation rate is the presence of water in the
exhaust gas. Others 74,76,288 have shown that the presence of water can improve reaction rates
of the C- NO2 reaction. Or these differences in reactivity could be due to the carbon samples.
The addition of the K or Na catalyst to the NIST sample (catalyst/carbon = 1:50 mol ratio) and
using the same NO2 gas composition gives an improvement in the rate of carbon oxidation. At
similar temperature, the rates are increased by 2 to 3 times with Na and K catalysts,
respectively, over the non-catalyzed soot. This boost in reactivity is smaller than that seen for
O2 reactions. However, these data do not clarify whether this effect is due to the catalysis of
55
the NO2 carbon reaction or from the catalysis of oxidation of NO to NO2 reaction. Although
there was considerable improvement in the carbon oxidation rate, the addition of the catalyst
did not meet the targeted criteria. The best catalyst with NO2 was able to only achieve at RG of
0.5 a temperature of 250°C.
An interesting observation is that the curves from these TPO experiments are concave for all
cases studied for non-catalyzed and catalyzed soot with oxygen and NO2. This indicates that
the activation energy, frequency factor or both are changing with extent of reaction. A similar
observation is made by Yezerets et al. on O2 only experiments 70,289. In all cases, it appears
that carbon reaction kinetics is changing. The double plateau observed in the non-catalyzed O2
experiments does not exist with the NO2 and catalyzed soot experiments. This may be due to
the NO2 and catalyst improving the reaction of the adsorbed hydrocarbons on the soot.
The most favourable rate in this study, achieved at 200°C, is RGo = 0.1. This would allow
continuous operation at an engine emission rate of 0.004g/bhp-h. The data show that under the
most favourable conditions of high catalyst to carbon ratio, intimate (tight) catalyst/carbon
contact, and highly favourable gas compositions that the carbon reaction rate does not achieve
the steady state carbon oxidation rates needed for 200°C operation at the soot emission rate of
0.02 g/bhp-h. The lower emission rate of 0.004 may be achievable under some low
temperature engine operating modes such as idle or low load, low speed conditions.
56
Figure 4.3.4: Gasification rate plot with non-catalyzed and catalyzed NIST samples in O2 and NO2 atmospheres: Reaction conditions: Ramp rate: 5.8°C/min, 7000 h-1, O2 runs: 10% O2, NO2 runs: 4.5% O2, 1 % NO2, 4 % NO, K-NIST sample: K/C : 1/50 mol ratio, Na-NIST sample: Na/C : 1:50 mol ratio Symbols with thin line represent literature data. Heavy lines represent thesis experimental data.
4.3.4 Thermal annealing (Isothermal experiments) O2 atmosphere
Aging of soot filter applications is mentioned often in the literature and can describe many
situations. In some cases, aging can be referring to the catalyst 3, the cordierite filter 11 and in
some cases the soot 68,70,290. As discussed previously, carbon oxidation can be affected by a
variety of factors such as contaminants, oxidant used and its thermal and chemical history, all
of which affect the active sites on the carbon. This set of experiments investigates two of the
factors that affect soot aging, thermal and gas composition effects using limiting conditions.
TPO annealing experiments were attempted but are difficult to interpret because of the
simultaneous change of temperature and carbon conversion during the experiment. Addition of
K catalyst further complicates the analysis due to decomposition of catalyst precursors and the
57
mobility of the catalyst. TPO experiments are briefly discussed here but are difficult to
interpret due to the above-mentioned reasons.
4.3.4.1 Experimental procedure for annealing experiments
1) He at room temperature (~25°C) was used to purge the reactor for a minimum of 10
minutes.
2) The sample is ramped to the reaction temperature of 550°C in 15 minutes under He
and allowed to stabilize for 5 minutes.
3) Reaction
a. Base case: (B1, B2):
i. When the reactor temperature is stabilized, 10% O2/He
(15 cc/min) is switched to the reactor.
ii. The two base cases were run at a temperature of 550°C until
completion (B1 and B2). Sample B1 and B2 were both pre annealed
in He at 550°C for 30 minutes. Sample B1 is ramped at ~ 39°C/min
to 700°C and then cooled immediately to 550°C. Sample B2 is
ramped at ~ 30°C/min to 550°C similar to Sample T2 below. The
observed rise in temperature at the end of the run was intended to
burnoff any residual carbon in the reactor and complete the mass
balance.
b. Treatment 2: T2 - In-situ constant Temperature anneal (Composition
change):
i. When the reactor temperature is stabilized, 10% O2/He
(15 cc/min) is switched to the reactor.
ii. After 5 minutes, the helium is switched back to the reactor.
iii. The sample is heated for 1 hour under helium. 10% O2/He
(15 cc/min) is introduced for 5 minutes and Helium switched to the
reactor for 30 minutes.
58
iv. At the end of this period, 10% O2/He is introduced to the sample and
allowed to completely react the remaining soot.
c. Treatment 3: T3 - Pre-anneal low temperature anneal with oxygen
i. Anneal carbon at 700°C.
ii. Low temperature anneal at 200°C in 10% O2 for 1 hour
iii. Isothermal burnoff at 550°C
d. Treatment 4: T4 - In-situ High Temperature Anneal
i. Same as Treatment 2: T2 with the exception at the second Helium
switch (iii) the temperature is raised to 700°C in 15 minutes and held
for 1 hour. At the end of the thermal treatment, the sample is cooled
back to the reaction temperature before switching the oxygen stream
to the reactor.
4.3.4.2 Analysis The rate is calculated at each sampling point during the burn-off curve (moles carbon
consumed with time) and normalized with respect to the initial mass of carbon loaded into the
reactor. Variability due to the sample mass loaded, sample composition variability and its
history give rate curves that vary greatly in initial reactivity. This variability in sample
reactivity makes it difficult to directly compare sample-to-sample runs. One solution is to
normalize the rate curves to the same initial conditions and plot this rate versus the fractional
conversion (fc) of the carbon. This is done in the following manner.
The ratio (A/Ao) is used here to describe the availability of the active sites by manipulation of
equation {4-1a}. A few assumptions are needed to get this ratio. First, it is assumed that the
oxygen concentration is in excess throughout the experiment. Second, it is assumed that the
carbon reaction order is pseudo first order. These assumptions reduce equation {4.1a} to
equation {4.1b} and rearrangement gives equation {4.1c}. Third, the rate constant, k, is
assumed to follow Arrhenius type kinetics {4.1d} that can be used in equation {4.1c} to give
{4.1e}. The value R/[C] represents the rate at a given fraction conversion of carbon and is
59
defined as Rfc. The (R/[C]) @fc=10% is defined as a basis point for comparison and is defined
as Rfco. Cancelling the exponential terms creates the ratio of A/Ao, equation {4.2}. This is
possible because of the constant reaction temperature (isothermal conditions) and the
assumption that the activation energy is constant with carbon conversion. By using this
method of analysis, the carbon is used as an internal standard and allows for comparison
against other sample runs by reducing the effects of sample weights, sample homogeneity,
reactor effects, and differences in thermal history.
R= k [C][O2] {4-1a}
Assuming pseudo first order kinetics, and excess oxygen, Equation {4-1a} can be reduced.
R= k [C], {4-1b}
R/[C]= k, {4-1c}
k= A exp (-Ea/RT) {4-1d}
R/[C] = Rfc= A exp (-Ea/RT) {4-1e}
(R/[C])fc=10% = Rfco= Ao exp (-Ea/RT) {4-1e}
Rfc / Rfco = A/Ao {4-2}
Where fc = fractional conversion,
Rfco is the rate of carbon gasification at a fractional conversion of 10%
Ao is the frequency factor at a fractional conversion of 10%
In the results below, the initial soot amount [Co] replaces [C] above to simplify the
calculations and does not affect the comparisons made or conclusions. Plots of A/Ao versus
fractional conversion of carbon for each of the experiments are shown below.
60
4.3.4.3 Results
Figure 4.3.5A gives an example of the oxidation rate of the carbon with changing time,
temperature history and oxygen pulses during the experiments. Arrows on the figure indicate
the corresponding y-axis. The square wave line indicates whether the oxygen pulse is on or
off. The gasification rate (RGo) curve indicates the evolution of carbon oxides from the carbon
surface. At the start of the experiment, the sample is temperature ramped in helium to the
reaction temperature (~550°C) and held to remove any volatiles of reactive oxygen on the
carbon surface. The reaction temperature of 550°C on this reactor set-up was chosen so that a
measurable rate of oxidation could be measured and that the reaction would be completed in a
reasonable time of 1 to 2 hours. During this temperature ramp it was observed that a peak is
present that may be attributed to the desorption of the volatile organic fraction of the soot or
reaction of oxygen containing surface functional groups and this is observed in the increase
above zero of the RGo curve at ~ 40 minutes. This indicates that oxygen-containing functional
groups on the carbon surface are reacting to release carbon oxides. After this peak is complete,
an oxygen pulse was sent to the reactor for five minutes followed by a temperature anneal in
He. Although the figure shows the oxygen is shutoff, there are still some carbon oxides
evolving. This is due to available oxygen for oxidation present in the reactor due to residence
time effects and possibly consumption of adsorbed oxygen on the carbon surface. The oxygen
pulse is repeated a second time followed by a second thermal anneal and finally a complete
carbon burn-off in oxygen. The analysis proceeds by determining the Rfc for a fraction
conversion of 10% (Rfco) and then normalizing as described above to calculate A/Ao values.
Figure 4.3.5B is created from this information and is described in detail below.
61
Figure 4.3.5A
Figure 4.3.5B
Figure 4.3.5: Effect of isothermal thermal annealing: Reaction Temperature = 550°C, B1, B2: Base case- burnoff curves with 10% O2/He. T2: Step change in Oxygen concentration from 10% to 0%, Thermal annealed for 1 hour at 550°C with He only.
62
4.3.4.3.1 Base case (B1, B2)
The two base cases follow the same general trend of a gradual decrease in the frequency factor
(A/Ao) with fractional conversion (fc) (Figure 4.3.5B). For these two curves, the initial 10% to
15% of conversion have similar normalized frequency factors (A/Ao). The two curves diverge
and have a maximum difference in A/Ao of 0.1. At a fractional conversion of 0.5 the base
cases have dropped on average by 25% relative to A/Ao=1 at 10% fc. This difference is
unclear and may be experimental error or possibly attributed to the pre-annealing affecting the
internal morphology and/or functional groups. In addition the 700°C treatment on sample B1
may reactivate sites on the carbon giving it better activity than sample B2.
4.3.4.3.2 Treatment T2: In-situ constant temperature anneal (Composition change)
Experiment T2 shows the same initial A/Ao for the first 10% to 15% of conversion as the base
case (Figure 4.3.5B). After the first thermal treatment, the A/Ao drops by about 40% relative
to base cases (B1 and B2). At a fractional conversion of 0.5 and higher, A/Ao is 60% less than
the initial A/Ao of 1 at 10% fc. This observation indicates that under an oxygen deficient
environment that the number of active sites on the carbon is reduced and would affect the rate
of soot oxidation with respect to the initial carbon inventory.
The above experiment would simulate a worst-case situation of an engine operating under high
loads and medium speeds where soot emissions are high and the oxygen concentration is
extremely low. Extended low oxygen environments over long periods are rare and would
never extend for 1 hour on most engines. Examples of such a low oxygen situation are a bus
going up a steep hill or a forklift lifting a heavy load. This data indicate that repeated exposure
to oxygen deficient environments could lower the reactivity by a value of 40% and would
represent an upper limit to active site loss.
63
4.3.4.3.3 Treatment T4: In-situ high T anneal
This experiment is a repeat of the gas composition change experiment above with the second
anneal in He at a temperature of 700°C (Figure 4.3.6A). The ramp to higher temperature was
intended to cause further sintering and removal of adsorbed oxygen on the soot. A peak
indicating desorption of carbon oxides is observed at ~75 minutes. It was expected that a
higher temperature would cause more rapid loss of active sites and show a larger activity loss.
This was not the case. Instead the normalized A/Ao exhibits a similar change after the 700°C
anneal as seen with a 550°C anneal under He (Figure 4.3.6B). It is suspected that the higher
temperatures may have desorbed contaminants on the surface exposing active sites.
64
Figure 4.3.6A
Figure 4.3.6B
Figure 4.3.6: Effect of in-situ high temperature annealing: Reaction Temperature = 550°C, B1, B2: Base case- burnoff curves with 10% O2/He. T2: Step change in Oxygen concentration from 10% to 0%, 1st Thermal anneal at 550°C for 1 hour and 2nd Thermal anneal at 700°C with He only.
65
4.3.4.3.4 Treatment T3: Pre anneal at 200°C with O2: complete burnoff
In this treatment, the sample was ramped to 200°C in He and then reacted with 10% oxygen for
1hr (Figure 4.3.7). The results show no significant difference from the baseline rate curves.
This experiment was performed to simulate an engine idling for 1 hour at 200°C which then
experiences a temperature increase. What is observed is that the 200°C pre-anneal does not
appear to change the rate at higher temperatures and closely resembles the burn-off curves of
the base case burn-off curves. The short exposure to He during the purging and ramp up in
temperature to 550°C does not affect the shape and trend of the curve. The amount of burn-off
during this step change from 200°C to 550°C is about 1% of the soot mass. This low mass
burn-off is too low to cause a noticeable difference in A/Ao from the 200°C pre-anneal on the
oxidation rate of the carbon.
Figure 4.3.7: Effect of 200°C pre anneal and reaction with oxygen (T3): Temp2 = 550°C, B1, B2: Base case- burnoff curves with 10% O2/He. T2: Step change in Oxygen concentration from 10% to 0%, Thermal annealed for 1 hour at 700°C with He only. T3: Pre anneal at 200°C in He and then oxidation in O2.
66
All of the above experiments show that there is an effect on carbon reactivity with changing
temperature and gas atmospheres under extreme cases. What this implies is that possible
engine load condition changes may affect the carbon reactivity during steady state operation.
Soot resident in the filter would constantly be reacting at a slower rate, as it is burned off.
During transients in engine operation the carbon reactivity could be reduced quicker by low
oxygen concentrations and high temperature excursions. In effect, the filter would eventually
accumulate low reactivity soot if a regeneration event did not occur. Although, in an actual
engine application the effects of these changes may not be clearly obvious from filter testing
due to few reports on the subject, making it likely a minor contributor to filter device
deactivation, or due to the difficulty in measuring the effect.
4.3.5 Temperature Programmed Oxidation - Thermal Annealing Experiments
4.3.5.1 NO2 atmosphere- non-catalyzed conditions (Annealing Treatments) The experiment was performed by initially annealing the carbon sample under He at the
specified annealing temperature, cooling in He and then performing a TPO experiment in a
NOX gas stream as described previously in Section 4.3.2. The annealing time (tann) was
varied to determine the effect on the reactivity. Annealing times of tann= 0, 2.5, 4, and 8 hours
were tested. The analysis of the data was performed by extracting from each individual TPO
experiment the temperatures for each fractional conversion of carbon (0, 0.1, 0.2,…1) at
similar gas composition and temperature ramp rate. A plot of the temperature at the specified
fractional conversion (Tfc) versus annealing time is shown in Figure 4.3.8A. These Tfc values
were normalized to Tfc at annealing time of 0 hours to give the ratio Tfc/(Tfc @tann=0)
(Figure 4.3.8B). The ratio gives the effect of annealing time by comparing the change in
temperature needed to give a certain fractional conversion at the same temperature ramping
rate. At a fractional conversion of 0.1, the temperature increases by greater than 1.15 times
after four hours of annealing. Further increases in annealing time give minimal changes in the
ratio. A similar flat profile is observed for fc= 0.2, 0.25, and 0.4 after 6 hours of annealing. At
high fc’s greater than 0.5 the temperature ratio is observed to trend upward while fc=0.9 trends
67
linearly with temperature and does not appear to have hit a plateau. One possible explanation
is that the longer annealing exposure times at high temperature are needed to cause changes in
the active sites of the carbon. These changes could manifest as reordering of the graphene
sheets within the carbon particle reducing the surface area and thus the number of active sites.
The first 10% of fractional conversion of the carbon is likely represented by the outer layer of
the carbon particles and would be subject to initial reordering of the graphite sheets and edge
functional group reorganization. Exterior carbon lamellae of collected DPF soot particles have
greater graphitisation as observed by microstructural examination of soot particles using
HRTEM 19,113,118. Vander Wal and others also report for carbon exposed to oxygen and
temperatures present in DPF’s (200-500°C) that the interior of the particles is hollow
indicating faster oxidation of the disordered interior carbon nanostructure 19,118. These may
explain the observations seen here, that at high fractional conversions the interior of the
particle may be reordering with increasing exposure time under annealing conditions. Oxygen
concentrations at the interior of a carbon particle are uncertain and may play a role in loss of
reactivity. The oxygen-free annealing experiments performed here are limiting cases that may
reflect what is occurring during long anneal times within a carbon particle in a particulate
filter. The reordering of the interior of the particle to less reactive material or loss of reactive
sites may explain the increase in the Tfc/Tfc@tann=0 ratio at long anneal times and high
fractional conversions.
68
Figure 4.3.8A
Figure 4.3.8B
Figure 4.3.8: Effect of Annealing time on Temperature for a specified fractional conversion, 5% NO2, 10% O2, Annealing Temperature: 700°C, Ramp rate: 5.8°C/min
69
4.3.5.2 Annealing treatments (K catalyzed soot) A TPO annealing experiment was performed to determine the effect of annealing on the K
impregnated samples (K-NIST (K/C = 1/50)). In Figure 4.3.9, a plot of the native soot, NIST
is shown with catalyzed samples with and without thermal annealing. It is shown that all of the
catalyzed samples are reactive at much lower temperatures than the non-catalyzed soot.
Longer thermal treatments at 680°C cause a shift in the reactivity curves to higher
temperatures. A curious feature is also seen in these thermal treatments. It appears that the K-
NIST samples with thermal treatment have two reaction regimes. Although, the cause of these
two regions was not determined the following questions can be asked. Is the potassium being
encapsulated during the annealing process? Is potassium carbonate or other less reactive
species being formed at the high temperatures? Is potassium migrating to the silica diluents or
is it evaporating and being re-deposited in the cool zone of the reactor? The likely explanation
for the shift in reactivity may be gas phase mobility but further tests are required. Jelles et al.
showed at >1000K that K compounds are mobile in the gas phase, but does not indicate the
rate of gas phase mobility 291. The temperature used for annealing in this experiment is below
this temperature but some migration could have occurred either to the diluents, reactor wall, or
quartz wool. Furthermore graphitisation on the exterior of the particle could encapsulate the
mobile catalyst rendering it inactive or possibly accelerating internal particle oxidation. This
would make the reactivity measured in this experiment a function of two factors: active
catalyst concentration change and thermal annealing. Further investigations are needed to
decouple these two effects.
70
Figure 4.3.9: K impregnated NIST soot (Thermal Annealing) TPO Annealing T= 680°C, K/C mol ratio = 1:50
A reactor that was used for a K catalyzed reaction was reloaded and tested for effects of any
residual K on the reactor walls. For this test the reactor contents were removed and the used
reactor (contains residual K on walls) was loaded with the non-catalyzed soot (NIST). It is
seen that at low temperature the reactivity of the soot in the used reactor is initially higher than
the NIST soot in a clean reactor but at higher temperatures it matches the clean reactor NIST
soot reactivity. The low quantity of K may have “wet” some of the carbon and this could
explain the higher low temperature reactivity. As the carbon is converted, the K was likely
unable to wet the remaining carbon and reactivity drops to that of the native soot.
The RGo versus conversion plot (Figure 4.3.10) shows that the rate is increasing with
conversion indicating that possibly more sites are catalyzed or sites being created. The valleys
are also observed in this plot. The 8h anneal under He shows a drop in reactivity at low
fractional conversions (0.4) and the 2h anneal in He having a drop in reactivity at higher
fractional conversions (0.7). This may indicate during the long annealing time that the catalyst
has migrated from the carbon and thus reduces the amount of catalyzed active sites.
71
Figure 4.3.10: K impregnated Carbon (Thermal Annealing) TPO Annealing T= 680°C. Fractional Conversion versus RGo Plot
Additionally, assuming Arrhenius type behaviour, the slopes of the rate curves for the different
anneal samples containing K catalysts vary in the same manner indicating that the activation
energies are similar (Figure 4.3.11). The observed shift of the curves to higher temperatures is
due to the change in the frequency factor that indicates possible change in the number of active
sites.
72
Figure 4.3.11: Comparison of slopes of rate curves. Same conditions as Figure 4.3.9
4.4 Conclusion/Summary
The data collected in both the isothermal and temperature programmed experiments support
that there is an observable change in the reactivity of carbon after exposure to high
temperatures in the absence of oxygen. Isothermal experiments indicate that the rate drops by
a maximum of 40% while TPO shows a shift in the reaction temperature to reach a specified
carbon fractional conversion. Under these conditions, it is proposed that the carbon active sites
are decreasing by either morphological changes of the carbon particle through graphite sheet
reordering and/or loss of oxygen functional groups at the carbon edges. The observed changes
in the carbon oxidation rate were low under these drastic conditions. It is practically
impossible for the collected diesel soot to not be exposed to oxygen unlike here where
extended periods of oxygen-less exposure are seen. The rate changes reported here could be
regarded as upper limits for carbon rate changes. These results show that the contribution of
thermal annealing to changes in carbon reactivity may be a minor contributor in the observed
rate of carbon trapped on real life diesel particulate filters. However under NOX gas
73
conditions, a method is needed to measure in-situ NO2 creation to answer if the catalyst is
accelerating the formation of NO2 or catalyzing the C + NO2 reaction.
4.5 Future Work/Suggestions
Work here is inconclusive for determining if morphology changes and/or functional group
availability is the primary cause of rate limitations on carbon reactivity. One potential
experiment to help clarify this question is to perform longer pre-anneals and/or in situ anneals
at high temperature (700°C or higher). This would cause the surface to be cleaned of all
oxygen functional groups that would be desorbed during carbon burn-off. Extended exposure
at high temperature anneals would allow the carbon structure more time to change and stabilize
in a final structure. Each of the samples with varying degrees of anneal on the carbons would
be cooled in helium and then reoxidized in a known concentration of O2 for constant length of
time followed by a burn-off. The above experiment would indicate that the carbons reactivity
could be changed and possibly renewed. It would help confirm or deny that reactivity changes
are primarily due to the presence of surface functional groups and/or morphology changes of
the carbon.
74
5 ToFSIMS study of surface functional group reactivity
5.1 Introduction
This chapter covers the study of the reactivity of the functional groups on the carbon surface
using secondary ion mass spectroscopy. As discussed in Chapter 2, the reaction mechanism of
carbon oxidation consists of a series of smaller elementary steps. These small steps in the
mechanism consist of the adsorption of the oxidant, the interaction of the oxidant with the
carbon to form surface intermediates, the formation of the product molecule and the desorption
of the product molecule 13. As described in Chapter 2, the mechanism of carbon oxidation
involves functional groups on the edge carbons of the graphite carbon sheets. These functional
groups serve as reaction intermediates and in the generic mechanism step
O2 + C (site) C(O) COx + C
C(O) represents one or more surface intermediates, i.e. functional groups on the carbon
surface. Details of these crucial steps, the formation and reaction of surface intermediates, are
not clearly understood. Additional understanding of these surface intermediates and their
reactivities may hold the key to better understanding of the carbon oxidation reaction
mechanism and possibly provide insight into how to improve the reaction. For this study, the
reactivities of the surface functional groups are measured directly and not on the basis of total
carbon.
Early reports have established that edge carbons on the polyaromatic sheets of the carbon
structure are the most reactive 61,122,213. These edge carbons react to form a variety of
functional groups that may serve as “active sites”, some playing a role as intermediates in the
gasification mechanism of the carbon. They are also responsible for the adsorption properties
of activated carbon materials. Early reports identified that active sites (discussed in detail in
Chapter 2) on the carbon surface are important to the carbon reaction mechanism 52,59,61,62.
Studies on various carbon materials (activated carbon 26,86,292, graphite 293, carbon black 18,23,30,293, soot 31,35,78, chars 30,31) have proposed that the carbon surface is populated with
75
functional groups such as lactones, carboxylic acids, carboxylic anhydrides, lactols, pyrone,
and pyridine groups 16,24,293. Figure 5.1.1 shows these possible functional groups formed by
reaction with oxygen and/or water vapour and their respective decomposition temperature
ranges. These functional groups may control reactivity of the carbon sheets and are likely the
active sites. Many surface reaction schemes have been postulated and have been described in
greater detail in Chapter 2.
Figure 5.1.1: Functional groups on soot surface listed according to their thermal stability. Acidity represents only the general trend. (Muckenhuber et al.35 )
As discussed in Chapter 2, Section 2.1.3, reaction mechanisms involving such surface
intermediates have been postulated for the C + O2 and C + NO2 reactions. Nevertheless, the
reaction mechanism for carbon oxidation is uncertain. Furthermore, the species involved in the
more rapid NO2 – carbon reaction, utilized in the soot filter technology, have received much
less attention and are relatively unknown. Published reactor studies provide information on
primary and secondary product 13,61. These studies report that the reaction of carbon and
oxygen forms primarily carbon oxides above 500°C 13,61 and the reaction of C with NO2 forms
CO and NO at temperatures as low as 250°C 74,75. Isotopic labelling studies were able to
identify that carbon dioxide and carbon monoxide are both produced directly by desorption as
gaseous products from carbon-oxygen complexes on the surface 64. In all cases the oxidant
molecule is proposed to interact with a carbon site to form surface intermediates that react and
76
rearrange on the surface and eventually desorb as CO or CO2. These reactor analysis
techniques, although informative, shed only limited light on the surface reaction mechanism.
The introduction of sophisticated analysis techniques that investigate the surface reactions
allowed greater insight into the reaction steps by the identification of surface groups. Early
attempts were made using surface titration experiments to identify functional groups on the
carbon surface such as lactones, carboxylic acids, anhydrides, etc 16,24. More recently, in-situ
infrared (IR) techniques give additional insight into adsorbed species on the carbon surface 26,28
and give real time information of reaction products on surfaces. DRIFTS has been used by
Fanning 30 to examine the reaction of oxygen with carbon black. More recently, Muckenhuber
et al. 35,78,79 used DRIFTS and TPD –MS to study the interaction of NO2 and commercial soots
(Printek U and Monarch 120) for the identification and reaction of surface groups. XPS was
used to give chemical oxidation states of the carbon surface and possible surface group
identification by derivatization reactions 17. Raman spectroscopy gives information on the
morphology of the carbon 123,125, but is insensitive to functional groups because of selection
rules.
A surface technique that has not been extensively studied with respect to carbon oxidation is
SIMS (Secondary Ion Mass Spectroscopy). It is a surface sensitive technique that uses ion
bombardment to release mass fragments from the sample surface and is thus capable of giving
molecular information. Despite the fact that a small fraction of the desorbed fragments are
ionized and detected, it has a high sensitivity, down to ppm levels 49,50, much greater than other
surface techniques such as XPS, which is limited to the 0.1% range. SIMS is also very surface
specific, Briggs has shown that SIMS data examine ~ 2 monolayers (10 angstroms) or less of
the surface 294. Both negative and positive ions can be measured by changing the polarity of
the detector. New TOF technology has increased the sensitivity of this technique, by detecting
all of the ions desorbed by brief pulses of primary ions. Modern ToFSIMS machines providing
high mass resolution (m/Δm of ~ 10,000) allowing for the separation of similar mass
fragments, such as S (m/z=31.9716) and O2 (m/z = 31.9898). Liquid metal ion guns allow
spatial resolution to 50 nm and new cluster ion beams provide higher yield of high molecular
weight fragments. Detailed information on the ToFSIMS technique and instrumentation can be
found in the recent book by Briggs 295 and references therein. Despite the advantages, the
77
technique is difficult to quantify, due to the highly variable yields of the various secondary
ions. Furthermore, due to absence of reliable fragmentation patterns from known surface
species, the assignment of a fragment or group of fragments to a given precursor is not always
easy or possible. Nevertheless, SIMS is capable of providing molecular information with a
much higher degree of sensitivity. When combined with TPD (Temperature Programmed
Desorption) it allows relative changes of the surface composition to be followed in great detail.
The combination of SIMS and TPD was first used to study the dehydrogenation reaction of
ethylene on Pt (111) and the isotopic exchange between hydrogen and deuterium in adsorbed
ethylidyne on Pt (111) surface 296. This technique thus offers an opportunity to evaluate the
types and reactivity of the surface intermediates on the carbon that may control the oxidation
rate at different temperatures, like other ‘single step’ investigations of heterogeneous
reactions.
SIMS analyses of soot and hydrocarbons containing a few polycyclic rings have been reported 37,297-302. Simple aromatic molecules related to soot such as 1,2,3,4- tetraphenyl naphthalene
have been used to study and simulate desorption and ionization processes during SIMS 302.
Albers et al 37 studied carbon black and ‘as collected’ diesel soot before and after exposure to a
Pt catalyst using SIMS. They report the ion/C2- ratio for CH-, C2H-, C2H2
- and O-. The CH-
and C2H- are ions reported to provide differentiation between hydrogen containing species
associated with poorly crystalline and highly graphitic structures. C2-/CH- ratio was used as a
probe to investigate the efficiency of the catalyst to remove low crystalline carbon species with
high H/C ratios. C2-/C2H- ratio was used as a measure of bound hydrogen associated with bulk
carbon. An erosion test was performed for up to 10000 seconds (i.e. continuous ion
bombardment of the carbon surface); they suggest that the increasing ratio of C2- /C2H2
- with
erosion time indicates that upper layers of the carbon surface layers are being removed and the
more graphitic layers are being exposed. This interpretation is suspect, since continued ion
bombardment rearranges and decomposes the underlying layers 49,50. Large changes in all the
ratios occurred after approximately 250 seconds. Kirchner et al 297 examined diesel soot using
single particle mass spectrometry and ToFSIMS with a m/Δm of 5000. The diesel soot was
examined ‘as is’ and after exposure to ozone and alpha–pinene. They report strong positive
ion intensities for N+, K+, Fe+ and Ca+. Negative ions were composed of carbon - oxygen
78
fragments, NOX products (NO2-, NO3
-) and sulphate fragments (HSO4-). Carbon core products
such as C5, C6, and C7 were less abundant.
Here we attempt to add to the current knowledge base by using the technique of TP –
ToFSIMS (Temperature Programmed Time of Flight Secondary Ion Mass Spectroscopy) to
provide molecular surface information. It is used to identify the type of surface molecular
fragments, their parent surface functional groups that may contribute to the reactivity of the
carbon and their reactivity to rearrangement and eventual gasification. In following the surface
composition with time and temperature, the reactivities of these surface functional groups can
be measured and compared with the gasification rates of soot oxidation.
5.2 Experimental Procedure
5.2.1 Sample preparation and pre-treatment Samples investigated and their treatments for this experiment are shown in Table 5-1. These
were obtained by pretreatment of the three carbon samples described in Chapter 3. Five
samples were chosen for analysis. Two of these samples are ‘as-is’ diesel soots, NIST-0
(forklift soot) and CAT-1 (engine soot from a CAT 3306). The third sample labelled NIST-
ANN is a sample of NIST-0 that received a pre-treatment of 8 hours in a He atmosphere at
700°C. These three diesel soot samples form a subset that allows the study of temperature
change of the functional groups found on the two different engine diesel soots (NIST-0 and
CAT-0). In addition, the NIST-ANN sample provides functional group information of the
cleaned soot surface after 700°C exposure.
Model char carbons, one exposed to NO and NO2 (SC_NOX) and one exposed to air
(SC_AIR), were made from pure sucrose starting material and comprise the remaining two
samples of the five samples analyzed. The sucrose char was created by heating sucrose at a
rate of 1°C/min to 400°C and held for 2 hours under air in a muffle furnace. Then, using the
reactor set-up described in Chapter 3, individual samples were initially annealed in a He
atmosphere for 8 hours at 700°C and then cooled in He. Sample SC_NOX was then prepared
79
by exposing one such sample to flowing NOX (4000ppm NO, 1000 ppm NO2, 4.5% O2) at
200°C for 20 minutes. SC_NOX and SC_AIR were both exposed (max. 30 minutes) to air
(79% N2, 21% O2) during the sample mounting. The pure carbon chars allow the investigation
of the N-containing intermediates formed from NOX without interference by fragments
produced from inherent nitrogen in the soot samples. Unlike these pure carbons, the diesel
soots were exposed to NO and NO2 during their formation in the combustion chamber and
subsequent storage in the diesel particulate filter.
Table 5-1: List of Samples used in TPD-ToFSIMS analysis
Sample #: Description NIST-0 NIST as-is
CAT-0 CAT 3306 Diesel soot as-is
NIST-ANN NIST soot annealed in He at 700°C for 8 hrs, air exposure for 30 minutes at 25°C
SC_NOX
Sucrose char annealed in He at 700°C for 8 hrs, dosed with NOX for 30 minutes at 200°C, Air exposure for 30 minutes at 25°C
SC_AIR Sucrose char annealed in He at 700°C for 8 hrs, air exposure for 30 minutes at 25°C
After pre-treatment of the samples, they were mounted for analysis by pressing the carbon
powders into copper foils. The copper foil was pre-rinsed with methanol, acetone and de-
ionized water to remove contaminants and allowed to air dry prior to depositing the carbon. A
pressure of 2000 psi was used to press and immobilize the carbon particles into the ductile
copper foil substrate. The loaded foil sample was promptly placed in the vacuum chamber of
the ToFSIMS to minimize air exposure. Exposure to room temperature air was a maximum of
30 minutes. Although chemistry can occur during this exposure time the data show that
adsorbed molecules desorb during the first temperature ramping step.
5.2.2 TP ToFSIMS experiment description The TP ToFSIMS experiment is comprised of the collection of the SIMS mass spectra at
various times during a temperature program profile. ToFSIMS analysis was performed on
each sample at seven temperatures. These temperatures are room temperature (25°C), 100°C,
200°C, 300°C, 400°C, 500°C and maximum stage temperature (~550°C). A Type K
80
thermocouple was mounted to the sample stage for temperature measurement. The
temperature was ramped slowly for each temperature increment, and the pressure inside the
vacuum chamber was monitored to ensure that desorbing gas did not exceed the pressure limits
of the analysis equipment. If pressures inside the chamber were found to rise too rapidly
temperature ramping was stopped until vacuum pressures dropped to acceptable levels.
Temperatures greater than 550°C were unstable due to the heated platform approaching its
maximum power output. A qualitative profile of one step of the typical experimental profile is
shown in Figure 5.2.1. The ToFSIMS spectra are acquired during periods denoted (ta).
Acquisition times (ta) were about 2 minutes in length and total acquisition time for both
positive and negative ions was about 5 minutes. The intervening temperature ramps to the next
temperature are denoted (tr). Occasionally, during the ramp to the new temperature, the
ramping had to be stopped to allow the vacuum to stabilize (ts) and for other operator
interruptions (th). Once the heated stage is at the required temperature further time may be
needed for vacuum stabilization and other operator interruptions. The heating and stabilization
cycle is repeated for all the remaining temperatures.
81
Figure 5.2.1: Timing events during typical TP ToFSIMS experiments, ta= acquisition time, tr= ramp time, th = operator interruptions, ts= vacuum stabilization time
It was found that the NIST soot (NIST-0) took a long time (hours) to degas in the vacuum
chamber (i.e. vacuum stabilization) while the sucrose char time was shorter. The shorter
degassing times, the absence of organic adsorbed species and the lower initial N content levels
in the sucrose chars are the primary reasons why NOX dosing was performed on these pure
carbons instead of the diesel soot.
ToFSIMS spectrum collection was performed using an ION-TOF ToF-SIMS IV instrument
(ION-TOF, Munster, Germany) equipped with a 25 keV Ga liquid metal ion gun. Positive and
negative ion spectra were acquired from 0 to 200 m/z over a rastered area of 100 um x 100 um
while maintaining a primary ion dose below the static limit of 1013 ions/cm2. Staying below
the static limit minimizes the chance of analyzing the same region twice so that the measured
ions arise from surface undamaged by the SIMS process.
tr ts + th
ta
ts + th
tr
Tem
pera
ture
Time
ta tr ta
82
5.2.3 ToFSIMS spectra, data calibration and peak assignment
All mass fragments are collected simultaneously during ToFSIMS analysis, thus creating a
large data set at each condition. Figure 5.2.2 shows a typical negative ion spectrum observed
at the indicated temperatures for sample NIST-0. Each vertical line in the Figure represents the
signal intensity of one of the masses measured by the ToFSIMS analyzer (note the logarithmic
intensity scale). Distinct fragments up to 200 amu were observed. Small (low m/z) fragments
were the most intense and the intensity decreased for heavy/more complex molecular
fragments (see Figure 5.2.2).
The processing of each individual temperature data set requires scale calibration, selection of a
spatial region of interest, subtraction of the substrate signal (Cu), recalibration of the spectra,
and assignment of the peaks. Mass calibrations are performed by identifying many known, or
expected fragments (e.g. CN-, C-, C2-, C3
-, C4- ), and assigning appropriate elemental
compositions to these fragments. The ToFSIMS software uses these values to calibrate the
entire mass scale. Calibrations for these sets of experiments were stopped at 100 mass units.
Beyond 100 mass units it was very difficult to narrow down the possible molecular formula
assignments. After calibration and with the assistance of the ToFSIMS analysis software
(IONSPEC version 4.5.0.0), each mass peak was assigned a molecular formula that best
describes its peak position and elemental composition of the sample. A spatial region of
interest was selected for an ion image of the data. The region was selected that had the highest
ion intensity and a minimum of Cu signal. The Cu signal was subtracted from the data set and
the data recalibrated with extra care taken to identify all ions containing C, H, O and N only.
In addition sulphur containing carbon compounds and metal containing fragments were
identified. A peak list was generated for each individual temperature from a single sample.
These individual peak lists were then combined to create a master peak list that was applied to
spectra for a single sample. The master peak list was truncated to contain only ions (~67 ions)
containing C, H, O, and N; this was done to make data analysis simpler.
83
Figure 5.2.2: Example of TP-ToFSIMS spectra for Negative Ions – Sample NIST-0, Temperatures of spectra displayed: room temperature (~25 °C), 100 °C, 200 °C, 400 °C, and 550 °C. Y-axis: Intensity (log scale), X-axis: mass units (m/z, linear scale)
Room Temperature (~25°C)
200°C
400°C
100°C
550°C
84
5.2.4 Data Analysis
An example of signal intensities for the negative ions containing C, H, O, and N of SC_NOX is
shown in Figure 5.2.3 with respect to the analysis temperatures. The labelled ions are those
that represent the highest intensities during the experiment. In this case, the light ions, H-, OH-,
C2-, C2H-, CN-, give the strongest signal intensity and are labelled in Figure 5.2.3, Panel A.
The rest are included, unlabeled, in Figure 5.2.3, Panel B, to illustrate the richness of the data
set. Some ions are observed to increase while others decrease, reflecting the formation and
decomposition of the precursor surface groups. The C2- intensity has the greatest value with
the exception of H- ion and is used as a carbon substrate reference ion for intensity changes
(discussed below).
85
10000
100000
1000000
10000000
0 100 200 300 400 500 600
Temperature (°C)
log
Inte
nsity
H-
C2-
C2H-O-
OH- CN-
C-
CH-
Panel A
100
1000
10000
0 100 200 300 400 500 600Temperature (°C)
log
Inte
nsity
Panel B
Figure 5.2.3: Effect of temperature on the intensity of each individual ion for sample SC_NOX negative ions. Panel A = high intensity, low molecular weight ions: Panel B = lower intensity, higher molecular weight ion intensities (unlabeled) to show variety of temperature dependent behaviour.
86
5.2.5 Reference ions and relative intensities
SIMS is inherently non-quantitative and ion yield is sensitive to surface composition. A
reference peak is used to normalize the rest of the ion intensities. Ion yields are used to
describe the number of secondary ions generated by a primary ion impact (see reference 295 for
ToFSIMS operation). Secondary ion yields can vary by several orders of magnitude across the
periodic table and are dependent on the chemical state of the surface 303. For example, a
sample could have trace amounts of Na and have high surface coverage of another element
such as Pt; the intensity of Na would be higher because of its greater tendency to create ions.
However, relative compositional information can be extracted by taking the ratio of the
intensity of the component signal with respect to the signal intensity of a chosen standard peak
(i.e. an internal standard) within the spectra. For the negative ion spectra, the C2- intensity is
the highest (with the exception of H- ion). It also is the most prominent ion in pure carbons
and graphite and clearly arises from the basic carbon structure in the absence of functional
groups on the edges of the paragraphitic carbon sheets. The C2- ion intensity is therefore used
as a carbon substrate reference ion for intensity changes of the other mass fragments.
Similarly, the C+ ion intensity is used for an internal standard for the positive ion spectra. This
method of normalization is useful to compare spectra collected at different dates. This is
discussed in more detail in Section 5.3.4.
5.2.6 Plan of data analysis
The data collected using TP-ToFSIMS are extensive and a multifaceted plan was used to
extract the information. In the results section below the data are presented firstly to illustrate
changes in overall surface composition as a function of temperature. Following this, more
detail of the speciation during thermal treatment is presented. In particular, the detailed
changes in composition of the N containing functional groups on the NOX exposed sucrose
char (SC_NOX) are examined. Finally, general data mining of the spectra using principal
component analysis (PCA) to associate groups of ions with each other was used to extract
correlations which might not be obvious to the eye, but which could reveal unexpected trends.
87
5.3 Results
5.3.1 SIMS atomic composition change with temperature The atomic composition of the surface was examined to determine any trends in the surface
composition of the carbon. Atomic compositional change in the sample was determined for the
negative ions for each of the samples. First the mole fraction of C, H, O, N was calculated for
each ion fragment in the master peak list (Equation {5-1}). For example, yxj for the H atoms
(x) in ion CH3O2- (j), would be equal to 3/6.
yxj = nxj/ntj {5-1}
where ntj = total of all atoms in the ion j, nxj represents the number of atoms of element x in ion j (i.e. C, H,O, or N)
Following this, the mole fraction of a given element (x) in the complete spectrum at a single
temperature is calculated using the following:
yx sample @ T = [Σ (Ij @ T * yxj)]/ I total @ T {5-2}
Itotal @ T = Σ Ij @ T {5-3}
Where Ij @T is the ion intensity of ion j at the specified temperature, T, and I total is the sum of all ion intensities at temperature T.
Figure 5.3.1 and Figure 5.3.2 show these elemental compositions (C, H, O, and N) as a
function of temperature for SC_NOX and SC_AIR. The reader is reminded that this
information is not a quantitative measure of the atomic composition, but shows clear trends in
the atomic composition relative to temperature. Focusing on sample 3, SC_NOX, mole
fraction changes with increasing temperature, it is observed that C triples from 0.18 to 0.65, H
decreases by a two thirds from 0.6 to about 0.2, O decreases by one-half from 0.2 to ~0.08,
while N content increases ten times from ~0.01 to 0.1. Similar observations can be made for
sample 4, SC_AIR, where C triples from 0.25 to 0.68, H decreases by about one half from 0.5
to about 0.2, O decreases by a half from 0.2 to ~0.1, and N content increase five times from
~0.01 to 0.05. In both cases the amount of C and N increase while H and O decrease with
increasing temperature from 25°C to 550°C.
88
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
25 100 200 300 400 500 550
Temperature (°C)
SIM
S At
omic
Com
posi
tion
C
H
ON
Figure 5.3.1: Elemental (C, H, O, N) SIMS spectral composition as a function of temperature for SC_NOX negative Ions.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
25 100 200 300 400 500 550
Temperature (°C)
SIM
S At
omic
Com
posi
tion
C
H
O
N
Figure 5.3.2: Elemental (C, H, O, N) SIMS spectral composition as a function of temperature for SC_AIR Negative Ions.
89
As can be seen from these figures, the SIMS ion spectra lose H and O and retain C and N
during the temperature program. The differences between these samples is subtle and is more
easily observed in Figure 5.3.3, which plots the relative difference (SC_AIR - SC_NOX) for
each element as a function of annealing temperature. Negative values on the plot indicate that
the element has a greater mole fraction on the non- NOX treated sample (SC_AIR) and positive
values indicate that a greater mole fraction is present on NOX -treated sample SC_NOX. We
observe that the initial mole fraction of carbon is higher on the non-treated sample (SC_AIR)
than on the NOX treated sample (SC_NOX), reflecting a higher concentration of “hetero-
atoms”. At 25°C, carbon content is ~0.35 higher on SC_AIR than on SC_NOX. As
temperature increases the carbon mole fractions approach the same value indicating no
difference in carbon content on the two samples after the surface functional groups have
decomposed. O content is higher initially on SC_AIR and at 300°C, SC_AIR appears to retain
a higher fraction. At higher temperatures, however, the SC_NOX sample appears to retain
more oxygen and overtakes SC_AIR, reaching a maximum of 0.18 at greater than 500°C.
Turning to the hydrogen content, SC_NOX exhibits a larger H fraction throughout though
there are changes in the relative composition with temperature. H content at 25°C is about
0.18 on SC_NOX and drops linearly to 0 at 200°C. It then rise to a maximum value of ~0.3 at
a temperature of 300°C followed by a second linear decrease in the ratio. The H datum point
at 300°C deviates from this linear line and shows a higher H content on SC_NOX of ~ 0.3
higher than SC_AIR. Interestingly, the N content is higher on SC_AIR at 25°C. It indicates
that these N - containing species on SC_AIR are possibly weakly adsorbed, possibly formed
during air exposure, and are readily desorbed upon temperature increase. Above 100°C, large
amounts of N are retained on SC_NOX as seen by the increase in the N mole fraction to
difference values of 0.5 at temperatures of 400°C and higher. The nitrogen content, though a
minor component of the SIMS spectra, could contain valuable information about the
mechanism of NO2 oxidation of carbon, particularly in view of the contrast between the
behaviour of these two “pure” carbons. The data indicate that N from NO2 is retained in the
char during oxidation. This observation is supported by previous reports 45,304 that have
reported that N accumulates in carbon chars as it oxidizes. The overall atomic composition is
not markedly different between NO2 and O2 exposure, however examination of the molecular
intensities show a strong difference between the two. In the upcoming sections, we will
identify the ions that are responsible for the elemental compositional changes described here.
90
-0.4-0.3-0.2-0.1
00.10.20.30.40.50.6
0 100 200 300 400 500 600
Temperature (°C)
(SC_
NOX
- SC_
AIR)
/ SC
_NO
X
C
H
O
N
Figure 5.3.3: Effect of NOX treatment on sucrose char. Difference in elemental mole fractions between NOX treated sucrose char (SC_NOX) and non-treated sucrose char (SC_AIR). Positive values indicate higher mole fractions in SC_NOX. Negative values indicate higher mole fractions in SC_AIR.
5.3.2 SIMS elemental compositions of diesel soots
The elemental composition changes on the diesel soot samples NIST-0, CAT-0, and NIST-
ANN are similar to the sucrose char samples. Mole fraction changes of NIST-0 with
increasing temperature from 25°C to 550°C show that C increases from 0.22 to 0.62, a change
of three times. H decreases from 0.4 to about 0.1. O decreases similarly from 0.35 to ~0.08.
N content increases by three times from ~0.05 to 0.15 at 500°C and then remains constant
(Figure 5.3.4).
91
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
25 100 200 300 400 500 550
Tem perature (°C)
SIM
S A
tom
ic C
ompo
sitio
n
C
N
O
N
Figure 5.3.4: Elemental Composition Change (C, H, O, N) with Temperature of NIST-0 Negative Ions.
Similarly, CAT-0 mole fraction changes with increasing temperature from 25°C to 550°C
show C increases about 3 times from 0.22 to 0.62. H decreases from 0.48 to about 0.15, about
1/3. O decreases from 0.28 to ~0.1, and N content increases five times from ~0.02 to ~ 0.1
(Figure 5.3.5).
92
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
25 100 200 300 400 500 550
Tem perature (°C )
SIM
S A
tom
ic C
ompo
sitio
n
C
H
O
N
Figure 5.3.5: Elemental Composition Change (C, H, O, N) with Temperature of CAT-0 Negative Ions.
NIST-ANN mole fraction changes with increasing temperature (Figure 5.3.6) from 25°C to
550°C show C increases approximately three times from 0.22 to 0.7 at 500°C and remains
constant. H decreases by four times from 0.55 to about 0.15, O decreases four times from 0.18
to ~0.05, and N content increases by six times from ~0.02 to ~ 0.12.
For each of these samples, relative changes in the elemental compositions are subtle. The
relative change in carbon composition is the same from sample to sample. H and O show
slight changes with the greatest variation observed in the nitrogen content. These changes
between samples are examined more closely below.
93
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
25 100 200 300 400 500 550
Temperature (°C)
SIM
S A
tom
ic C
ompo
sitio
n
C
H
ON
Figure 5.3.6: Elemental Composition Change (C, H, O, N) with Temperature of NIST-ANN Negative Ions.
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
0 100 200 300 400 500 600
Temperature (°C)
(NIS
T-0
- NIS
T-A
NN
)/NIS
T-A
NN
CHON
Figure 5.3.7: Effect of thermal annealing at 700 °C in He on NIST diesel soot. Difference in elemental mole fractions between non-treated NIST diesel soot (NIST-0) and thermally annealed NIST soot (NIST-ANN). Positive values indicate higher mole fractions in NIST-0.
94
The effect of annealing on the NIST diesel soot can be observed by examining the relative
difference in mole fraction between the NIST as-is sample (NIST-0) and the annealed NIST
sample (NIST-ANN) (Figure 5.3.7). Positive values indicate higher fractions of that element
in sample NIST-0 than in NIST-ANN. The opposite observation is seen with negative values.
C has a higher mole fraction in NIST-0 at low temperatures up to 300°C, hitting a maximum of
0.2 at 100°C. From 300 °C to 500 °C, NIST-ANN has a 0.1 higher fraction of C. At 550 °C
the two samples are equal. O mole fraction is constantly 0.4 to 0.6 higher on the NIST-0
sample from 25°C to 500 °C. At 550 °C the O content drops to 0.2. N content is higher on the
NIST-0 sample and drops with temperature from 0.6 to 0.2 between 100°C to 300°C. N
content on the NIST-0 sample remains constant until 550 °C where the N content is 0.18 more
than the annealed sample. H content is higher on the annealed sample, NIST-ANN, for all
temperatures. It increases on the annealed sample from 0.4 to 0.8 (25°C to 200°C), and goes
through a minimum of 0.4 at 300 °C. It increases to 0.6 and gradually decreases to zero
difference in H content between the two samples at 550 °C. By contrast with the sucrose char
behaviour, and though the two NIST soots begin the experiment with a different surface
composition, there is no strong contrast between the thermal behaviour of elemental species,
particularly N, on the two samples.
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
0 100 200 300 400 500 600
Temperature (°C)
(SC
_NO
X - N
IST-
0)/S
C_N
OX
CHON
Figure 5.3.8: Difference in elemental mole fractions between NOX -treated sucrose char (SC_NOX) and non-treated NIST diesel soot (NIST-0). Positive values indicate higher mole fractions in SC_NOX. Negative values indicate higher mole fractions in NIST-0.
95
A comparison between the ‘as-is’ diesel sample NIST-0 sample and NOX - treated sucrose char
sample, SC_NOX, shows a contrast, however. The relative differences are plotted with
increasing temperature (Figure 5.3.8), similarly to Figure 5.3.7. Again positive values indicate
a higher relative fraction on the SC_NOX sample. C has a value of –0.25 from 25 °C to 200°C
and then shifts to zero from 400 to 500 °C. O is at –0.6 at 25°C and increases to about zero
from 200 to 500°C indicating no relative difference. At 500°C the value becomes +0.25
indicating that the oxygen content is higher on the SC_NOX. H content is constantly higher on
SC_NOX at a value of 0.4 from 25°C to 500°C and drops slightly to 0.25 at 550°C. The
relative retention of H and O with increasing temperature is similar on the two materials. A
large contrast is seen in the N content, however. The initial N contents are very different,
owing to the native N in the engine-produced soot, with a difference value of –3 at 25°C. This
drops to –1 at 400°C and higher. As mentioned previously, the absolute fraction changes of N
in the ions are small, however, in one NOX exposure cycle the sucrose was able to retain N
contents similar to those in the engine produced carbon.
-1.2-1
-0.8-0.6-0.4-0.2
00.20.40.60.8
0 100 200 300 400 500 600
Temperature (°C)
(NIS
T- 0
- C
AT-
0)/N
IST-
0
CHON
Figure 5.3.9: Difference in elemental mole fractions between non-treated NIST diesel soot (NIST-0) and CAT 3306 diesel soot (CAT-0). Positive values indicate higher mole fractions in NIST-0. Negative values indicate higher mole fractions in CAT-0.
96
The relative difference between mole fractions of the two engine soots (NIST-0 minus CAT-0)
is shown in Figure 5.3.9. The initial SIMS-based compositions are very similar for the two
samples; however as temperature increases some differences emerge, particularly in their H
content. The difference in H content is –0.1 and continues to decrease to a minimum of –1 at
500°C, but recovers at 550°C to nearly zero. This is odd behaviour and deviates from the trend
of decreasing H – content and may be due in part to temperature instability of the heated stage
used in the ToFSIMS analysis. This indicates that H content is higher on the CAT-0 sample
during the middle temperature range. O content is retained less well by the NIST soot as
shown by the persistent decrease in O content difference with temperature and the O content on
the CAT-0 soot eventually exceeding that on the NIST soot. Again, these two soots have
higher initial N contents, with the NIST-0 soot being consistently greater than the CAT-0.
These differences persist as temperature is raised, indicating little or no contrast in the retention
of N in these two materials.
5.3.3 General SIMS atomic change observations
A few general features are observed in the atomic changes of all the samples. The results are
no surprise, O and H content in all cases decreases with temperature while C and maybe N
content increase with temperature. The retention of N content fraction supports the
observation of Ashman and others 45,304. Interestingly, the direct involvement of NOX exposure
in the incorporation of N in sucrose char is a new finding. The two diesel soots have different
atomic compositions: CAT-0 has higher H content while NIST-0 has consistently higher N
content. However, the N retention differences are minor in the materials, which did not see an
incremental NOX exposure.
The atomic compositional data provide information during the TP experiments that real
changes are occurring to the carbon surface. The next step involves narrowing down possible
reactive groups on the carbon surface by identifying the molecular ion fragments that
contribute to these atomic changes in the carbon with temperature.
97
5.3.4 SIMS molecular fragment changes
The distribution and identity of the molecular ion fragments and their individual behaviour
with temperature reveals much richer information about the identity of the functional groups on
the carbon surface and their reactivity. This additional information reveals a strong contrast
between the NO2 and O2 samples. Although ToFSIMS spectra were collected for both positive
and negative ions, the negative ion spectra gave greater molecular information. Although
metals, which are measured more easily in positive SIMS, are known to influence the oxidation
of carbon, the primary focus here will be on the C, H, O, N containing organic ions revealed in
negative ion spectra.
To begin, examples of calibrated, assigned non-normalized peaks behaviour are shown for
sample NIST-0 (Figure 5.3.10). Ions selected for this overview are based on (1) the magnitude
of their intensity changes, (2) ions that can be associated with surface functional groups
identified in the literature by other techniques (eg. CN-, OH-, O2-, CO2
-) 16,86,292,293, and (3)
possible molecular surface groups (NO-, NO2-, CO-) 27,28,35,78 that could affect carbon
reactivity. The C2- ion was used as a reference for the intensity measurements since C2
- ions
are the highest intensity peak for all temperatures (with the exception of H) and has been
shown to arise from graphite and the basic graphitic carbon structure in the soot. The raw C2-
intensity is observed to increase with temperature (see Figure 5.3.10 panel a). Conversely, OH-
ions decrease with temperature (see Figure 5.3.10 panel b). NO2- levels (Figure 5.3.10 panel c)
drop rapidly with temperature and are undetectable at 200°C and higher. The maximum
intensity of the NO2- ion is one order of magnitude less than the C2 - ion. CO2
- ions also
decrease with temperature and are two orders of magnitude less than C2- (Figure 5.3.10 panel
d). The CO- ion intensity (Figure 5.3.10 panel e) is very low and gives an example of an ion
near background levels. The CO- ion decreases with temperature and disappears into the
background at higher temperatures. O2- intensity is similar to OH- in intensity and is observed
to decrease with temperature. S- is shown because of its close proximity in mass. Although it
initially has a similar magnitude with the O2- ion, it is observed to remain relatively constant
with temperature, exhibiting a small maximum near 300°C.
98
mass / u23.95 24.00 24.05
4x10
2.04.06.0
Inte
nsity
4x10
2.04.06.0
Inte
nsity
4x10
2.04.06.0
Inte
nsity
4x10
2.04.06.0
Inte
nsity
4x10
2.04.06.0
Inte
nsity
4x10
2.04.06.0
Inte
nsity
4x10
2.04.06.0
Inte
nsity
Spectrum ParameterSample ParameterSample:
Comments: ; ;
Origin:
File: PI dose:
Area / µm²: 371 x 371 um„
1.0x10ƒ… ions/cm„
Polarity:
Time / s:
negative
150 TOF-SIMS IV
File:NIST_B1.dat
File:NIST_D.dat
File:NIST_F.dat
File:NIST_H.dat
File:NIST_J1.dat
File:NIST_L2.dat
File:NIST_N1.dat
mass / u16.95 17.00 17.05
4x10
1.0
2.0
3.0
Inte
nsity
4x10
1.0
2.0
3.0
Inte
nsity
4x10
1.0
2.0
3.0
Inte
nsity
4x10
1.0
2.0
3.0
Inte
nsity
4x10
1.0
2.0
3.0
Inte
nsity
4x10
1.0
2.0
3.0
Inte
nsity
4x10
1.0
2.0
3.0
Inte
nsity
Spectrum ParameterSample ParameterSample:
Comments: ; ;
Origin:
File: PI dose:
Area / µm²: 371 x 371 um„
1.0x10ƒ… ions/cm„
Polarity:
Time / s:
negative
135 TOF-SIMS IV
File:NIST_B1.dat
File:NIST_D.dat
File:NIST_F.dat
File:NIST_H.dat
File:NIST_J1.dat
File:NIST_L2.dat
File:NIST_N1.dat
(a) Sample NIST-0: C2
- (b) Sample NIST-0: OH-
mass / u45.95 46.00 46.05
3x10
1.02.03.04.0
Inte
nsity
3x10
1.02.03.04.0
Inte
nsity
3x10
1.02.03.04.0
Inte
nsity
3x10
1.02.03.04.0
Inte
nsity
3x10
1.02.03.04.0
Inte
nsity
3x10
1.02.03.04.0
Inte
nsity
3x10
1.02.03.04.0
Inte
nsity
Spectrum ParameterSample ParameterSample:
Comments: ; ;
Origin:
File: PI dose:
Area / µm²: 371 x 371 um„
1.0x10ƒ… ions/cm„
Polarity:
Time / s:
negative
150 TOF-SIMS IV
File:NIST_B1.dat
File:NIST_D.dat
File:NIST_F.dat
File:NIST_H.dat
File:NIST_J1.dat
File:NIST_L2.dat
File:NIST_N1.dat
mass / u43.95 44.00 44.05
2x10
0.5
1.0
1.5
Inte
nsity
2x10
0.5
1.0
1.5
Inte
nsity
2x10
0.5
1.0
1.5
Inte
nsity
2x10
0.5
1.0
1.5
Inte
nsity
2x10
0.5
1.0
1.5
Inte
nsity
2x10
0.5
1.0
1.5
Inte
nsity
2x10
0.5
1.0
1.5
Inte
nsity
Spectrum ParameterSample ParameterSample:
Comments: ; ;
Origin:
File: PI dose:
Area / µm²: 371 x 371 um„
1.0x10ƒ… ions/cm„
Polarity:
Time / s:
negative
150 TOF-SIMS IV
File:NIST_B1.dat
File:NIST_D.dat
File:NIST_F.dat
File:NIST_H.dat
File:NIST_J1.dat
File:NIST_L2.dat
File:NIST_N1.dat
(c) Sample NIST-0: NO2
- (d) Sample NIST-0: CO2-
CO2
25°C
100°C
200°C
300°C
400°C
500°C
550°C
25°C
100°C
200°C
300°C
400°C
500°C
550°C
99
mass / u27.96 27.98 28.00 28.02 28.04
1x10
1.02.03.04.0
Inte
nsity
1x10
1.02.03.04.0
Inte
nsity
1x10
1.02.03.04.0
Inte
nsity
1x10
1.02.03.04.0
Inte
nsity
1x10
1.02.03.04.0
Inte
nsity
1x10
1.02.03.04.0
Inte
nsity
1x10
1.02.03.04.0
Inte
nsity
Spectrum ParameterSample ParameterSample:
Comments: ; ;
Origin:
File: PI dose:
Area / µm²: 371 x 371 um„
1.0x10ƒ… ions/cm„
Polarity:
Time / s:
negative
150 TOF-SIMS IV
File:NIST_B1.dat
File:NIST_D.dat
File:NIST_F.dat
File:NIST_H.dat
File:NIST_J1.dat
File:NIST_L2.dat
File:NIST_N1.dat
mass / u31.92 31.94 31.96 31.98 32.00 32.02 32.04
3x10
0.5
1.0
1.5In
tens
ity
3x10
0.5
1.0
1.5
Inte
nsity
3x10
0.5
1.0
1.5
Inte
nsity
3x10
0.5
1.0
1.5
Inte
nsity
3x10
0.5
1.0
1.5
Inte
nsity
3x10
0.5
1.0
1.5
Inte
nsity
3x10
0.5
1.0
1.5
Inte
nsity
Spectrum ParameterSample ParameterSample:
Comments: ; ;
Origin:
File: PI dose:
Area / µm²: 371 x 371 um„
1.0x10ƒ… ions/cm„
Polarity:
Time / s:
negative
150 TOF-SIMS IV
File:NIST_B1.dat
File:NIST_D.dat
File:NIST_F.dat
File:NIST_H.dat
File:NIST_J1.dat
File:NIST_L2.dat
File:NIST_N1.dat
(e) Sample NIST-0: CO- (f) Sample NIST-0: S- and O2
- Figure 5.3.10: Example of identified peaks from TP- ToFSIMS data
Absolute intensities in SIMS often vary substantially (1) from sample to sample, (2) with the
overall composition of the sample due to so-called “matrix” effects and (3) over time due to
subtle variations in the spectrometer settings. In order to “normalize” the data for proper
comparison between different spectra and samples, the C2- ion from the basic carbon structure
was used as an internal reference to scale the intensities of the negative ions arising from the
attached functional groups. Such use of internal standards is common in SIMS investigations 49. Thus a normalized value N(x) for negative ion species x is given by
)()(
2)( −=
CIxIxN T
{5-4}
I(x) = intensity of component x at T
I(C2-) = intensity of C2
- ion at T N(x)T = Normalized intensity of ion x at temperature T
The normalized intensity ratios (N(x)T) are shown as a function of temperature for several key
ions in the panels of Figure 5.3.11. Each panel includes the results from all of the carbon
CO
O2
S
25°C
100°C
200°C
300°C
400°C
500°C
550°C
100
samples. A general trend for all samples and most ions is that the normalized intensity of these
ions associated with surface functional groups decrease with temperature. This indicates that
surface coverage of functional groups responsible for these ions is decreasing on the surface.
The exception to this trend is the CN- ion. Examining samples SC_NOX and SC_AIR,
annealed char dosed with NO2 and annealed char respectively, it is observed that CN- intensity
is similar at low temperature (25°C). CN- intensity for sample SC_AIR declines gradually
with temperature as the samples are heated. Singular behaviour is shown for the NO2 dosed
sample (SC_NOX) where the CN- intensity instead goes through a peak (400°C) before
declining. The ultimate CN- intensity of the NO2 dosed sample (SC_NOX) is about double
that of the CN- intensity on the non-dosed sample (SC_AIR).
The CNO- curve for SC_NOX is also distinct. The intensity for CNO- for SC_NOX gradually
rises and reaches a maximum at 300°C. For all the other samples, the CNO- peak decreases
linearly with increasing temperature. This result indicates that there is some kind of chemistry
occurring on the surface of the SC_NOX sample.
With these principal ions, we observe ion intensity decreasing with increasing temperature with
some of the ions showing some variability between samples. It is observed that some of the
ions have varying slopes with temperature. Possibly these changes may lead to information on
the rate of change of functional groups on the surface. Some of the functional groups on the
surface could kinetically control the reaction of the carbon and would be represented by the
molecular fragments (ions) generated in the ToFSIMS analysis. This kinetic rate change of the
molecular fragments on the surface is further discussed in Section 5.3.5.
101
Figure 5.3.11: Effect of temperature on Individual Ion intensity ratio for each sample. (Intensity scale x100) where 0= NIST-0, 1= CAT-0, 2= NIST-ANN, 3= SC_NOX, 4= SC_AIR
Earlier it is shown that atomic nitrogen is present in the samples of SC_AIR and SC_NOX
making it difficult to determine the source of the nitrogen. Despite this uncertainty, there is
substantial contrast between the behaviour of the N-containing ions on SC_AIR versus
SC_NOX. Figure 5.3.12 illustrates this for the prominent ion (CN-) ion. The ratio of I(CN-
)/I(C2-) for the SC_AIR sample is constant for all temperatures and may slightly decrease at
temperatures greater than 300°C. By contrast, SC_NOX shows the opposite behaviour for the
ratio and increases with temperature up to a ratio of I(CN-)/I(C2-) of ~ 3 , an increase of ~
12 times over SC_AIR at 550°C. Similar comparisons of the carbon – NO type ions (C3NO-
102
and CHNO-) for SC_NOX and SC_AIR (Figure 5.3.13) show further contrasting behaviour.
The N = I/C2- ratio for these ions from SC_NOX goes through a maximum at intermediate
temperatures while the ratio for the same ions from SC_AIR decreases. The data would
suggest that there is an effect of NOX dosing on the sucrose char leading to nitrogen fixation on
the carbon surface possibly through a carbon - NO type intermediate.
Figure 5.3.12: Effect of NOX exposure on sucrose char. Comparison of the CN- ion between SC_NOX and SC_AIR.
103
Figure 5.3.13: Effect of NOX exposure on sucrose char. Comparison of the CHNO- and C3NO- ion between SC_NOX and SC_AIR
The temperature dependences of the various N-containing ion fragments from the NOX treated
sample (SC_NOX) show a rich variety of behaviour. This is illustrated in Figure 5.3.14, where
the normalized intensity values N(x)T/N(x)25°C where N(x)T is I(x)T /I(C2-) T are grouped from
top to bottom in order of increasing oxidized state. The value of N(x)T can be viewed as the
pseudo concentration of the ion on the surface at that temperature and the ratio N/No (short-
hand for N(x)T/N(x)25°C) gives an indication of the amount of change in concentration of the
ion fragment precursor observed on the surface as the temperature is increased from 25°C. The
concentration of highly oxidized carbon-free ions disappears quickly before 300°C (NO-, NO2-,
NO3-) while the concentration of CHNO- containing ions remains constant or goes through a
maximum in intensity between 200 and 400°C indicating some type of chemistry occurring on
the surface in this temperature range. All of the CHN- type ions with the exception of the
C6H3N- ion increase with temperature while only ions containing NH- decrease. This
observation of the loss of oxygen containing ions and the increase in CHN- type ions gives
excellent evidence towards the surface enrichment of nitrogen on the carbon surface possibly
104
caused by the reaction of NOX with carbon leaving behind N and liberating or forming a C-O
grouping on the carbon surface, as discussed in Section 5.3.8.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
25 100 200 300 400 500 550
Temperature (°C)
N/N
o
NH_3
NH_2
NH
C_6H_3N
C_3N
CHN
CN
C_3NO
CNO
CHNO
CH_2NO
C_2HNO_3
NO
NO_2
NO_3
CxHyN
CxHyOzN
NHy
Figure 5.3.14: Change in nitrogen containing ions during temperature ramping, Nitrogen, oxygen, hydrogen and carbon containing ions only shown, where N=I(x)/I(C2
-), Sample: SC_NOX. The order of the ions in the figure is identical to the list in the right hand margin.
A similar treatment for the C, H and O – containing (no N) is shown in Figure 5.3.15. Also
similar to the presentation of N-containing ions in Figure 5.18, the ion ratios N/No are ordered
from top to bottom in order of reduced to more oxidizing type of ions. The N/NO ratios for the
oxygen rich ions in the lower region of the figure decrease with increasing temperature
indicating that the carbon surface is depleting the precursors for these ions. The top region
contains ions with high H/C ratio (>0.6); these are also observed to decrease. In the middle the
opposite is observed for reduced ions with lower H/C ratio (<0.6) such as C4-, C3
-, C8-, C4H2
-,
C5H3- and others where the N/No ratios generally increase with temperature. In the
temperature range of this study, the oxygen containing ions being removed from the carbon
surface likely represent the functional groups of a carboxylic acid, lactone or carboxylic
105
anhydride as identified by DRIFTS and TPD 35. At temperatures greater than 300°C, the ratio
of N/No for the ion fragments of CO2-, CHO2
-, CH3O-, and CHO- is negligible indicating that
these ion fragments may represent less stable functional groups such as carboxylic acid or
lactones.
Figure 5.3.15: O depletion of C, H, O only containing ions during temperature ramping. Sample:(SC_NOX) The order of the ions in the figure is identical to the list in the right hand margin.
This observation of increasing N content with higher temperatures suggests that N bonding
with the carbon structure is very stable and thus, suggests that the N is being incorporated into
the heterocyclic structure of the carbon. The mechanism of how this is happening is not clear.
One notional mechanism is that at low temperatures, the NO2 molecule can bond to the carbon
surface through either the oxygen atom or the nitrogen atom and then through some unknown
106
rearrangement pathway the N is being incorporated into the carbon structure. Further analysis
of the ion fragments may reveal a potential reaction mechanism. Thus, the next step is to
identify the rate that the parent functional groups are leaving the surface and to identify if any
similarities exist that can help identify the surface groups and a possible reaction pathway.
5.3.5 Rate analyses of ion fragment data TP-ToFSIMS produces a rich source of data that are abundant with information. Through
further analysis it is possible to extract from this data quantitative measures of reactivity for
each of the individual ion fragments. The reader should be aware that these ion fragments can
be contributed from more than one surface functional group and thus the individual ion
reactivity can represent a number of surface functional groups with similar behaviour. By
understanding the rate of disappearance from the surface of these ion fragments, it may be
possible to (1) group ions together and to deduce the structure of the surface groups involved
and (2) evaluate the individual reactivities with respect to the specific gasification rates
required for low-temperature soot applications. Three methods were employed to extract
kinetic information from the large data sets:
A. Kinetic values are determined from spectra at each individual temperature; time of
reaction is assumed to be equal to the overall acquisition time of 5 minutes (referred
to as the “Integral Method”).
B. Kinetic values are derived from data collected during the acquisition time of a
single polarity (the “Differential method”).
C. Kinetic values from B are analyzed using a mathematical method called PCA
(principal component analysis) (the “Alternative Method”).
5.3.5.1 Method A - Integral method of rate analysis In this method, destruction rates of the individual ions were calculated from the total intensity
change between two temperatures. Specifically, the rate of change for an ion was calculated by
the following:
107
Ro,SIMS25
21
N(x) *t N(x) - N(x)
=Δ=
T
TT {5-5}
where Ro,SIMS is the integral rate of ion x , ∆t is the difference between the time of the start of
the acquisition and the end of the acquisition (~5 minutes). The choice of this value is
discussed below. Both the positive and negative ions were evaluated in this manner.
Various values of the reaction time ∆t can be chosen from the data. Referring to the
experimental diagram in Figure 5.2.1, the ramp from one temperature to another, two time
values are recorded. The first is the time from the last data acquisition at temperature T1 to the
time of next data acquisition at temperature T2. This time increment includes the times
required for ramp, hold, stabilization, and acquisition periods (Figure 5.2.1). This value can be
estimated using the difference in the time stamp for when the spectra were saved for each
temperature (~ 50 minutes). This time value represents a minimum rate of the removal of
surface groups from the carbon over the interval at the temperature T2. An alternative value of
the rate can be estimated by assuming that all the reaction occurs at the higher temperature and
during the acquisition time of both the positive and negative spectra (~ 5 minutes). This
provides the best available lower limit to the time value and thus provides a maximum rate or
turnover frequency. This “maximum rate” method is easy to calculate and was used to obtain
an overall picture of the types of rate behaviour.
The shapes of the integral rate (Ro,SIMS) versus T plots enable a visual method (correlation-
inspection technique) to compare the rate behaviour of the ions containing C, H, O, and N.
Examples are shown of plots comparing rates for pairs of ions (Figure 5.3.16); in three of the
panels (Figure 5.3.16, a-c), the ions proportionally correlate. In one plot the behaviours of the
two ions are anti-correlated (the rate of one goes up while the other goes down) (Figure 5.3.16,
d). In the three plots with similar shaped curves the majority of the data are proportional. As
can be appreciated attempting to compare each individual ion against another ion would be
very tedious by graphical methods. The notion of correlation was used to automate the
comparisons of the entire data set, as described below.
108
-0.0060
-0.0050
-0.0040
-0.0030
-0.0020
-0.0010
0.00000 100 200 300 400 500 600
Temperature (°C )
[Ro,
SIM
S] I
on1
(dia
mon
d)
-0.0040
-0.0035
-0.0030
-0.0025
-0.0020
-0.0015
-0.0010
-0.0005
0.0000
[Ro,
SIM
S] I
on2
(squ
are)
CHO_2
C_3H_2
a-0.0045-0.0040-0.0035-0.0030-0.0025-0.0020-0.0015-0.0010-0.00050.0000
0 100 200 300 400 500 600
Temperature (°C )
[Ro,
SIM
S] I
on1
(dia
mon
d)
-0.0060
-0.0050
-0.0040
-0.0030
-0.0020
-0.0010
0.0000
[Ro,
SIM
S] Io
n2 (s
quar
e)
C_4H_3
C_2H_2O_2
b
-0.0060
-0.0040
-0.0020
0.0000
0.0020
0.0040
0.0060
0 100 200 300 400 500 600
Temperature (°C )
[Ro,
SIM
S] I
on1
(dia
mon
d)
-0.0040
-0.0035
-0.0030
-0.0025
-0.0020
-0.0015
-0.0010
-0.0005
0.0000
[Ro,
SIM
S] Io
n2 (s
quar
e)
CHN
O
d-0.0100
-0.0080
-0.0060
-0.0040
-0.0020
0.0000
0.0020
0.0040
0 100 200 300 400 500 600
Temperature (°C )
[Ro,
SIM
S] I
on1
(dia
mon
d)
-0.0050-0.0040-0.0030-0.0020-0.00100.00000.00100.00200.00300.0040
[Ro,
SIM
S] I
on2
(squ
are)
CNO
C_5H_3
c
Figure 5.3.16 (a-d): Examples of positive and negative correlations for ion pairs of Integral rate versus Temperature. Units: h-1 panels (a,b): positive, panels (c,d): negative
In this automated procedure, the correlation coefficient (ρ) between each of the ions for the
integral rate Ro,SIMS as a function of T was calculated. The correlation function in Excel was
used, which is based on Equation {5-6 (a-c)}. It provides a mathematical method of
comparing the curve shapes regardless of magnitude. These values are collated in a correlation
matrix of all the ions. The matrix is n X n (n = 67, the number of identified ions) and half of
these values are used (~4500). The data set is reduced by extracting all the correlation
coefficients that fall within a certain range, for example between 0.9 <ρ<1 and –1<ρ<-0.9.
Further reduction of the data set was achieved by considering only the ions containing C, H, O,
and N resulting in a total of ~90 ion comparisons. Positive values of the correlation coefficient
indicate that two ions have similar proportional curves while negative correlations indicate
proportionally opposite curves with respect to T. An example of positive and negatively
correlating ions was shown in Figure 5.3.16. Note the primary and secondary axis scales are
not the same.
109
yxyx
YXCovσσ
ρ•
=),(
, {5-6a}
where: 11 , ≤≤− yxρ {5-6b}
∑ −−−=
n
iyixi yx
nYXCov
1))((1),( μμ {5-6c}
Equation 5-6: Equation for correlation coefficient (ρ) used to determine similar curve shapes
A combination of the calculated correlation coefficients and visual inspection is used to group
the ions into similar kinetic characteristics as given by their rate versus temperature curves.
Mathematical manipulations of the matrix could have been used to associate subgroups for this
identification step, but this was not done here. A rigorous independent mathematical
evaluation of the entire data set by principal component analysis, (PCA), was performed and is
described in section 5.3.4.5. Here, however, a single ion curve was chosen and all ion curves
with correlation coefficients greater than 0.7 were grouped with that ion. Each curve shape
was visually compared as a check to ensure that the curves were similar. In cases where the
curve shape would fit another grouping better the ion was moved.
All of the 67 CHON ions were thus divided into five different groups based on their curve
shapes (Figure 5.3.17). For clarity, these ion groupings are referred to as “Sets” to avoid
confusion with the word “group” used often in this document. The Sets do not take into
account if the Ro,SIMS value is a positive or negative. The integral rate measure is admittedly
complex and is only one method to search common surface precursors for the various SIMS
ions. The integrated rate measure, Ro,SIMS, measures the net destruction rate of the yield-
weighted suite of precursors for that particular ion. In other words, changes to the rate at which
the surface ion precursors are formed from surface reactions contribute in combination with the
destruction of these same surface species. A correlation therefore only signifies that there is a
similar temperature dependence of this net destruction rate, which could appear in addition to a
constant formation or destruction rate. Hence, negative values of Ro,SIMS indicate accumulation
of the precursor for that ion and positive values imply net destruction. Since the Ro,SIMS values
are approximate derivatives of the ion intensities, an ion with positive values of Ro,SIMS which
shows a maximum in its Ro,SIMS (T) (destruction rate) curve is correlated in this procedure with
an ion with negative values whose accumulation rate is minimized at the same temperature.
110
This procedure can identify possible correlation of part of an ion’s reaction pathways with
another, even if their intensities versus temperature is different. For example, if the Ro,SIMS
value is negative but is becoming less negative with temperature because of a greater
destruction rate, this component is correlated with an ion with positive Ro,SIMS values (net
destruction rates) which are increasing with temperature. Referring to Figure 5.3.17, Sets V, I
and II are similar in that they have a tendency towards increasing Ro,SIMS values with increasing
temperature, indicating an overall lower net rate of accumulation on the surface (negative
value) or higher rate of removal (positive value). The variations in this overall behaviour are
distinctive enough to be grouped separately, with Set I showing a two-stage behaviour with an
early maximum, while Sets II and V show monotonic increases, but with the increase in Set V
taking place at significantly higher temperatures. The remaining classes show more distinct
contrast. Set III curve goes through a maximum at intermediate temperatures, while Set IV
shows the opposite effect and goes through a minimum at intermediate temperatures. The ions
are assigned to their Sets in Table 5-2. An additional column is added here that shows ions
that do not fit any of the above sets. The ions in each column are sorted in order of decreasing
intensity.
Figure 5.3.17: Identified curve shapes for rate versus T plots grouped into Sets for sample SC_NOX. y-axis is rate, x-axis is temperature
V
I II III
IV
111
Table 5-2: Integral rate sets, SC_NOX, Ions in bold are suspect.
CHON Total Ions 67 Curve Type I II III IV V No trend
Total 5 41 16 4 0 1 Oxidized 4 25 1 3 0 1 Reduced 1 16 15 1 0 0
Ion CNO H CN C_2O CO C_4HO O C_4 C_3HO C_3NO C_2H C_4H C_3O C_5H_3 OH C_3 C_7H_2 C_4O CH C_3H C C_3N CH_2 C_5 C_2HO CHN O_2 C_6H CHO_2 C_6 C_3H_2 C_4H_2 C_2H_2O_2 C_5H C_2H_3O_2 C_7H C_2H_3O CHNO C_3H_3O_2 C_8H C_3H_3 C8 C_2H_3 NO_3 NO_2 NH_2 CH_3 CO_2 C_4H_3 CH_3O C_3H_5O_3 C_6H_3N O_2H C_5H_2 NH_3 C_3HO_2 NH C_3H_3O C_3H_5O CHO C_2HNO_3 C_4H_3O NO CH_2NO
C_4H_5O C_2HO_2 C_4H_5
112
Set II has the majority of the ions showing the greatest tendency towards lower accumulations
on the surface. It contains many of the negative ions with the highest intensities such as C, O,
and H. The set has 25 oxidized species and 16 reduced ion species indicating a possible net
loss of oxygen containing ions with temperature. Set III contains primarily reduced species
(16) with only one oxidized species (CHNO) showing similar behaviour. Set IV has ions with
decreasing rates with temperature although two of the ions (C2O- and C3O-) could be classified
to have a minimum in rate (not shown).
The contribution of each of the sets to the total intensity with temperature was determined
(Figure 5.3.18). For each set, the total set ion intensity was calculated by summing the
intensities of each ion contained in the set. Set II contributes more than 90% of the intensity at
room temperature and drops with increasing temperature to a final contribution of ~70% at
550°C. Set III contributes only 5% to the total intensity at 25°C but increases to 30% at
550°C. Sets I and IV, both go through maximums of less than 2% at intermediate
temperatures.
00.10.20.30.40.50.60.70.80.9
1
25 100 200 300 400 500 550
Temperature (°C)
Set
frac
tion
inte
gral
VIVIIIIII
II
III
I0.95
0.96
0.97
0.98
0.99
1
25 100 200 300 400 500 550
Temperature (°C)
Set
frac
tion
inte
gral
VIVIIIIII
II
III
IV
00.010.020.030.040.050.060.070.080.090.1
25 100 200 300 400 500 550
Temperature (°C)
Set
frac
tion
inte
gral
VIVIIIIII
I
II
Figure 5.3.18: Contribution of each integral rate set with Temperature, Top left: All Sets, Top right: Magnification of top fraction showing Sets II, III, and IV. No contribution from Set V. Bottom centre: Magnification of bottom fraction showing Sets I and II. Sample: SC_NOX
113
5.3.5.2 Method B - Kinetic values derived from time-dependent isothermal acquisition.
The data files accumulated on the ToFSIMS apparatus preserve the time of each ion pulse
during the analysis. Thus, the ion intensity changes with time at constant temperature can be
reconstructed from the data set. While this is normally used to verify that the surface has not
been damaged and the acquisition time remained within the so-called “static limit” 295, this also
allows measurement of isothermal kinetics of the change in surface functional group. This is
similar, from a reaction kinetics point of view, to an isothermal batch reactor with data
collection over an accurately known time. We used this aspect of the data to measure
isothermal kinetics for ions with sufficient intensity. This direct measure gives relative rates at
specific surface compositions and temperatures and provides a method for further analysis.
Data from the entire sample were used in order to increase the signal / noise ratio and further
averaging over ten-primary ion pulses was performed to improve the count statistics. Finally,
the ions’ intensities were normalized to the C2- ion as before. Typical results are shown in
Figure 5.3.19 for CH–. A linear fit equation and its statistics are calculated for each of the ions
using the LINEST function in Excel. Statistics for the linear regression correlation coefficient
(r^2), standard errors for y (s (y)), slope (sem), and intercept (sb) are calculated. This
information is used to estimate a first order rate constant or turnover frequency (k) for an ion
from the ratio of the slope and y-intercept of the linear Equation {5-7}.
k (1/h) = Δ N(x)/Δ t *1/N(x)o = slope/y-intercept {5-7}
k0.1(1/h) = k * sites/C where sites/C = 0.1 {5-8}
Where N(x)o is the normalized intensity at time = 0, k represents the TOF of an ion under
differential conditions, and k0.1 represents the gasification rate of a single ion based on TOF
values calculated under differential conditions.
Equation {5-8} is used to convert the value to a bulk reactivity that can be related to the
gasification rate (RG) presented earlier in Chapter 4. Error bars are based on the calculated
standard error of y from the linear regression. Again, here the rates are net destruction rates
114
and the k values (or TOF) can have both positive and negative values that reflect the dynamic
balance between the formation and destruction of the ion fragment precursors.
CH
1.541.561.581.6
1.621.64
0 20 40 60 80 100 120
Time (s)
Ra
tio
(I/
C2
-)
Figure 5.3.19: Time-dependent isothermal intensity changes in normalized intensity (CH- /C2-) for CH- at
25°C for sample SC_NOX negative ions. Top (a): All data during data collection, Bottom (b): The same date averaged over 10 primary ion pulses.
115
Finally, a plot of the k values against temperature is created. Examples of these plots are
shown for CH-, CN-, CHN- and NO-, for SC_NOX negative ions. CH- ions (Figure 5.3.20a)
have negative values and indicate that surface concentrations of this type of ion are increasing
with temperature. The maximum rate of increase is seen at 200°C. Above this temperature the
CH- ions accumulate on the surface at a lower rate.
The NO- data show the opposite behaviour to the CH- ions. The k values are positive and pass
through a maximum at 300°C indicating a net loss of NO- molecule precursors on the surface
of the carbon (Figure 5.3.20b). Above 300°C the rate of loss from the surface is decreasing
until at 500°C there appears to be a net gain on the surface. CHN- and CN- ions have similar
patterns (Figure 5.3.20c,d). Their positive k values go through a maximum at 100°C and then
gradually decrease in a similar manner but with different magnitudes. This similarity between
the curve shapes is interesting and can provide information on ion fragments that are being
removed in a similar way.
CH
-0.25
-0.20
-0.15
-0.10
-0.05
0.000 100 200 300 400 500 600
Temperature (°C)
k 0.
1 (1
/h)
Figure 5.3.20a
116
NO
-2.00-1.50-1.00-0.500.000.501.001.502.00
0 100 200 300 400 500 600
Temperature (°C)
k 0.
1 (1
/h)
Figure 5.3.20b
CHN
-0.40
-0.20
0.00
0.20
0.40
0.60
0 100 200 300 400 500 600
Temperature (°C)
k 0.
1 (1
/h)
Figure 5.3.20c
117
CN
-0.10
-0.05
0.00
0.05
0.10
0.15
0 100 200 300 400 500 600
Temperature (°C)
k 0.
1 (1
/h)
Figure 5.3.20d
Figure 5.3.20: Effect of temperature on differential rates for CH-, NO-, CHN-, and CN- ions (Sample SC_NOX)
A similar analysis is performed as per the “Integral Method”. Curve shapes of rate versus
temperature are grouped using both the correlation coefficient and visual comparisons. The
ions are sorted into the same curve shape groups as in the “Integral Method” (Figure 5.3.21)
and shown in Table 5-3. Generally, it is difficult to identify simple universal trends in the
differential data. In all of the differential groups there is a mix of species that have different
degrees of oxidation. As discussed later, this method was compared with the integral method
used for the analysis with the PCA method as well statistical relevance of the ion.
118
Table 5-3: Differential rate sets, SC_NOX. Ions in italics are suspect.
CHON Total Ions 67 Curve Type I II III IV V No trend
Count 9 22 9 9 13 5 Oxidized 6 11 4 5 7 1 Reduced 3 11 5 4 6 4
Ion C_3H_3 H C_4 CN C_3H_3O_2 C_6 CO_2 O C_4H CNO NO_2 C_2H_3 C_3O C_2H C_3 C_2HO NH_2 C_5H C_5H_2 OH C_3N C_3H_2 CH_3 C_3NO C_4O CH NO_3 C_2H_3O CH_3O C_7H_2 C_3H_5O C C_8 CHN C_3HO C_2HO_2 CH_2 C_2HNO_3 C_4H_2 C_3H_5O_3 C_4H_5 C_2O NO C_3HO_2 C_6H_3N CO O_2 CH_2NO C_4H_5O NH CHO_2 C_7H C_3H CHO C_2H_2O_2 C_4H_3O C_2H_3O_2 C_8H C_6H C_4HO C_4H_3 O_2H NH_3 C_5H_3 CHNO C_3H_3_O C_5
119
-3.5E-02-3.0E-02-2.5E-02-2.0E-02-1.5E-02-1.0E-02-5.0E-030.0E+005.0E-03
0 100 200 300 400 500 600
T (°C)
k0.1
Ion1
(dia
mon
d)
-4.0E-01-3.5E-01-3.0E-01-2.5E-01-2.0E-01-1.5E-01-1.0E-01-5.0E-020.0E+00
k0.1
Ion2
(squ
are)
C_2H O_2
-0.20
-0.15
-0.10
-0.05
0.000 100 200 300 400 500 600
T (°C)
k0.1
Ion1
(dia
mon
d)
-0.10
-0.05
0.00
0.05
0.10
0.15
k0.1
Ion2
(squ
are)
CH CN
Figure 5.3.21: Examples of positive (top panel) and negative (bottom panel) correlations for Sample (SC_NOX) negative ions.
5.3.5.3 Comparison between integral and differential method
A comparison between the ions found in the integral and differential rate sets revealed that
very few of the ions correlated between the two methods. The sets created using the integral
rate data tend to be more chemically distinct than those from the differential rate sets. Only 20
of the 67 analyzed ions (34%) were in the same sets when comparing the integral and
differential methods. Combining Sets V and I from the differential method and comparing this
to Set I in the integral method raises the percentage of matching ions to 51%. The greatest
number of matches was seen with ions with the highest absolute intensity. A possible
explanation is that the samples are not changing quickly enough during the isothermal testing
period (differential rates method) to observe any measurable changes in the kinetic behaviour.
120
Perhaps longer acquisition times might make it possible to measure these rates more precisely.
Of the two methods it appears that the integral method is more precise in giving a separation in
kinetically and chemically distinct ions, a possible result of the larger measurable differences
in the intensities between the temperatures during the analysis. In order to determine which
functional groups are accumulating on the surface of the carbon further analysis is needed.
An additional sorting of the ions in the integral method into categories with net positive only,
negative only, positive and negative rates was performed (Table 5-4). All ions with a positive
rate are reported in the table. Ions in this category have a majority of the rate values greater
than zero. In the positive rate category, 6 out of the 8 ions are from Set III where the ion
intensity goes through a maximum in rate with temperature. The six ions are CN-, CHN-, C4-,
C4H-, C4H2-, and C3N-. The other two ions (CNO-, C3NO-) are from Set I, where the rate
increases, and then drops and increases again.
The second category shows a near 50/50 distribution of both negative and positive rates with
increasing temperature. Two ions, C5H3- and C4O-, belong to Set I, C6
- and C8- belong to Set
III and the remaining ion C7H2- to Set III.
In the case of the negative rates, only the ions with the highest negative rates, but not
necessarily the highest absolute intensity are shown. The ions with the highest negative rate
values are primarily oxygen containing species at 200°C and all of these ions belong to Set II
(C4H5O-, C3HO2-, C3H3O2
-, C2HNO3-, C3H5O3
-, and C6H3N-). Since these ions have their
highest rate at this low temperature these ions might be weakly adsorbed species. In the case
of C2HNO3- the rate is highly negative (high rate of removal) at 200°C and drops to nearly zero
for the higher temperatures. Between the temperatures of 100 to 200°C, the parent group of
the ion may be removed or the surface reorganized. This ion may represent the base fragment
of NO2- and/or NO3
- weakly adsorbed onto a carbon site. The majority of the remaining ions
not discussed would primarily have negative rates and be comprised primarily of ions from Set
II.
121
Table 5-4: Max negative and positive rate categories, SC_NOX
Ion [Temperature of
highest rate ((+) or (-))
rate)]
Positive Negative (high) Positive and negative
CN (300) C4H5O (200) C5H3 (300)
CHN (300) C3HO2 (200) C4O (300)
CNO (300) (I) C3H3O2 (200) C6 (300)
C4 (300) C2HNO3 (200) C7H2 (300)
C4H (300) C3H5O3 (200) C8 (300)
C3N (300) C6H3N (200)
C4H2 (300)
C3NO (300) (I)
5.3.5.4 Statistical relevance of ions used in sets.
The inclusion of ion fragments into an ion set for both the integral and differential methods
was determined statistically. In the case of the integral method, the error of each ions intensity
was calculated using counting statistics (i.e 1/C1/2, where C is the number of counts or
intensity). Errors that would propagate into the ion rate values were determined using standard
error calculations for addition/subtraction and multiplication/division operations
(i.e. )222 ssss cbay++= and )()()(
222
cs
bs
ass cba
yy ++= ).
A visual inspection of the ion rate versus temperature curves with error bars indicated that 8
ions were suspect. These ions were removed from the ion sets and the average rates of the sets
calculated (section 5.3.7). It was found that inclusion of these suspect ions did not change the
shape of the rate curves or their magnitudes.
Rates in the differential method use isothermal data. Linear regression of the isothermal data
gives a goodness of fit. The value r2 gives the confidence that the straight line represents the
data. An error estimate on the slope (rate of change) gives directly the uncertainty in the rate
122
of change in intensity. Dividing this uncertainty by the average value gives the uncertainty in
dN/N*dt. Similarly the uncertainty in dN/No*dt is the uncertainty in the rate of change of the
intensity divided by the original N. The F-statistic was used to test if the isothermal data for an
ion was a random scatter of points with zero slopes. The F-statistic is a ratio of the variance
explained and the variance unexplained. An ion was included, if an ion for any temperature
was found to have the F statistic greater than the F critical value for an alpha of 0.1. This gives
90% confidence that the data are not a random scatter of points with zero slopes. Ten ions
were found suspect. However removal of these ions does not change the randomness of the
chemical species within the sets of the differential method. Why this difference exists is
unclear. This difference could be attributed to the time span being evaluated. The differential
method gives a snapshot of the surface reaction at a constant temperature while the integral
method consists of the reaction history during the ramp between analysis temperatures. This
would suggest that continuous ToFSIMS measurements may help clarify this difference
between the two analysis methods.
5.3.5.5 Method C - PCA method of analysis of data
Principal component analysis (PCA) uses multi-variant statistical techniques to help reduce and
simplify large data sets. PCA has recently been used to analyze ToFSIMS data, for example
protein analysis 305-307, forensic work 301 and has been used in fields of chemistry 308,309,
genomics 310 and others to interpret datasets. Large datasets such as TOF-SIMS spectrum can
be visualized as a multi-dimensional space. Comprehension of dimensions greater than three is
difficult and thus it is useful to reduce the dimensionality. This reduction can be accomplished
with the use of multi-variant techniques such as PCA that can retain a large amount of the
original information of the data set. PCA is an excellent tool for evaluating combinations of
variables that highlight possible trends in the data that may not be obvious by visual
examination.
Descriptions on the mathematics behind PCA can be found in the following references: 305,306,311-313. The input into PCA is a matrix (X) where the rows are samples (i.e. spectra at
temperature = T) and the columns are variables (i.e. ions). The inputs into each cell in the
matrix are the calculated rates for each ion at the given temperature. Prior to analysis, the data
123
must be pre-processed to allow for comparisons. A few types of pre-processing are described
in the literature: mean centering, auto-scaling and others 310-313. Mean centering involves
subtracting the mean value for the columns x-bar from original data set X to create the mean
centred data set. The data set is thus centred on the origin allowing for differences in the
sample variances to be more prominent than the sample means. Mean-centering was found by
Wagner et al. 306 to be the most informative method to analyze their data to determine protein
structures.
Autoscaling means that the columns of X are adjusted to zero mean and unit variance by
dividing each column by its standard deviation. Here, the rates of ions leaving the surface are
being investigated. Recall that the ion rate is normalized with respect to the initial ion intensity
at T= 25°C. Ion intensities are a function of the ionization potential in SIMS (i.e. some ions
are more easily ionized and detected). Thus the ion rate’s magnitude will also be affected by
the ionization potential of the ion (recall the rate is normalized to T= 25°C). By auto-scaling
all of the ion rate magnitudes to similar levels the chance of biasing one ion over another is
reduced. In other words, some ions may leave the surface in the same manner (i.e. curve shape
with temperature may be similar (correlate)) but may have different magnitudes of rate. These
ions leaving the surface in the same manner could potentially be from the same surface groups
on the carbon.
The PCA method involves statistically determining the variance within the input matrix by
determining the direction of the greatest variation within the data set. This can be visualized
graphically as an axis rotation to capture the direction of greatest spread within the dataset 311,313, (see figure in Graham et al. 311). The singular value decomposition (details can be found
in 306,313 and references therein) of the resulting variance covariance matrix is determined and
used to determine scores (describes relationship between samples in the new axis system) and
loadings (describes relationship between principal components associated with these sample
scores) as shown in Equation {5-9}:
X =PTT + E {5-9}
124
Where X is the auto-scaled data matrix, P is the matrix of scores and T is the matrix of
eigenvectors called loadings. The cross product of PTT contains most of the original variance
in X with the remaining variance (mostly noise) relegated to the residual matrix E.
Typically, 1 to 5 or higher principal components can be identified that will describe 100% of
the variance in the data set. Score plots can be made that describe the relationship between the
samples in the new coordinate system. Loading plots are used to describe the effect of the
principal component with the original peak data (variables). The first PC (principal
component) describes the direction of the greatest variation in the data set. The second PC
describes the direction of the second greatest variation.
5.3.5.5.1 PCA Results SC_NOX negative ions:
PCA was applied to the integral rate values of sample SC_NOX negative ions. About 60% of
the variance can be explained using a single PC, 80% with 2PC’s and 95% with 3PC’s (Figure
5.3.22, c). The biplot (Figure 5.3.22,b) gives information on the relationship of the scores and
loadings with PC1 plotted on the x-axis and PC2 located on the y-axis. The plot shows that the
variables tend to positively correlate with the high temperatures of 400 °C, 500 °C and 550 °C.
The 400 °C score datum point is not seen and is located in the centre of the loading data
variables. The 500 °C and 550 °C data points are located in the centre far right quadrant. The
loading plot (Figure 5.3.22,a) has PC1 on the x-axis with PC2 on the y-axis. Ellipses are
drawn that represent the sets discussed above in the integral rate section. Set I type ions are
found in the centre of the plot. Set II type ions are found spread in a curved band from the
upper right quadrant to the lower right quadrant. It contains the majority of the ions. Set III
type ions are found in a curved band spreading from the upper left quadrant to the upper right
quadrant. The majority of the ions in the upper left quadrant contain ions found in Set III. It
would appear that this Set could be split into two subgroups. This is shown by the addition of
dashed circles in Set III region. Set IV type ions are found from the centre of the graph to the
lower right quadrant. Set IV and Set III are located in opposite sides of the plot suggesting that
these two rate curves are anti-correlated. This is confirmed by re-examining the integral rate
125
curves (Figure 5.3.21) shown above where the rate curves versus temperatures are mirror
images. The ions tend to group in the same regions as the correlation-inspection method with
the following exceptions. C5H3- and C7H2
- found in Set I and IV respectively using the
correlation inspection method are moved to Set III. These ions fit this Set better chemically
(i.e. reduced ions). CO- is found to be in the same region as Set IV ions, which are chemically
very similar. Although the agreement is excellent between the two grouping techniques, the
task of assigning boundaries to the Sets would be difficult without the initial correlation
inspection groupings.
126
Figure 5.3.22: PCA Loading Plots for Sample SC_NOX negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified sets within the ToFSIMS data.
a
b c
I II
IV
III
127
Positive ions (~ 240 ions) for sample SC_NOX were examined using PCA. Three PC’s were
found to account for nearly 92% of the variance (Figure 5.3.23, c). The biplot shows that the
variables (loadings) are strongly correlated to the temperature from 300 to 500°C. The loading
plot of PC1 versus PC2 (Figure 5.3.23,a) shows the groupings (ellipses) of ions for the positive
ions. Sets were based on similar kinetic behaviour (i.e. same region of the loadings plot) and
attempting to group chemically similar ions in the same region. Seven groups are identified
with the set to right side of the plot containing the majority of the ions.
PCA was performed for the C, H, O and N containing ions for all samples and polarity of ions
(positive and negative). For these samples, loading plots, biplots and variance explained
versus PC plots can be found in the Appendix with plots for SC_AIR negative ions (Figure
5.3.24) shown below for reference. For all cases (positive and negative ions), there is a clear
trend of the majority of the ions grouping in a curved band on the right hand side of the PC1
versus PC2 loading plot. The ions in this set contain a mixture of reduced and oxidized ions
and tend to correlate with the higher temperatures (400°C and 500°C) used in the experiment.
As this is a first attempt to use this analysis method, the technique was not applied to all of the
PCA plots. However the PCA plots are included in the Appendix to show the variation of ions.
Additional analysis would require the intensity information collected during the ToFSIMS
experiments.
PCA alone is not capable of classifying ions into sets. It is a powerful tool to help group like
ions but does not provide enough information to determine the location of the boundaries of the
group making the assignment of these group boundaries a difficult task that requires further
assistance. Additional identification is needed along with the investigators judgement to
classify the boundaries in these regions where the group is not well defined. In this case, the
correlation inspection technique (used in Method A) was employed to examine the ion curves
in these undefined regions of the loading plot. Individual ions near an anticipated Set
boundary were compared and sorted based on their curve shapes, chemical states and the
investigators judgement. This method was found to be effective in the reduction and grouping
of the ion kinetic behaviour.
128
Figure 5.3.23: PCA Loading Plots for Sample SC_NOX positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the ToFSIMS data.
129
Figure 5.3.24: PCA Loading Plots for Sample SC-AIR negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the ToFSIMS data.
II
III III
I
IV
130
5.3.6 Effect of temperature on individual ion sets, SC_NOX For each group of ions for SC_NOX, the individual raw ion intensities at a single temperature
(T) were summed to get the total raw ion intensity for the Set at that temperature (T) (Figure
5.3.25a). These raw total ion intensities are further normalized to the C2- reference ion to give
the normalized Set ion intensity at T and are shown in Figure 5.3.25b. Set II starts at its
maximum intensity and gradually drops until it reaches its minimum intensity at 550°C. Set III
starts at its minimum intensity and increases to its maximum intensity at 550°C. Set I intensity
rises from 25 °C until it reaches its maximum intensity at 300°C, where it then decreases until
it reaches its minimum value at 550°C. Set IV rises from 25°C to it maximum intensity at
100°C and then gradually declines to its minimum intensity at 550°C. Each of the normalized
Set ion intensities is related to the pseudo concentration of the ions in that group. This makes
it possible to relate each Set to a possible concentration of the surface functional groups on the
carbon surface that are a probable source of the Set ions.
The change in surface functional groups (normalized surface conversion) represented by each
Set of ions with temperature is shown in Figure 5.3.25c. It shows that Set II increases the
fastest and its maximum surface conversion reaches 0.96. Positive conversion values indicate
depletion on the carbon surface while negative conversion values indicate accumulation on the
surface. Set IV conversion is initially negative from 25°C to 200°C indicating creation of
parent surface functional groups of these ions. It increases in conversion at a slower rate and in
a similar manner to Set II. Set I decreases to its minimum at 300°C and a negative value of ~
0.39 and then increases to positive conversion to a maximum of 0.95 at 550°C. Set III
decreases in conversion and mirrors the decrease in conversion of Set I to 300°C. It decreases
to 0.4 at 400°C and then increases to 0 at 550°C.
131
0
500000
1000000
1500000
2000000
2500000
3000000
0 100 200 300 400 500 600
Temperature (°C)
Raw
Inte
nsity
(II,
III)
0
5000
10000
15000
20000
25000
Raw
Inte
nsity
(I, I
V)
Set II
Set III
Set I
Set IV
0
5
10
15
20
25
0 100 200 300 400 500 600
Temperature (°C)
Nor
mal
ized
Inte
nsity
, (N
(x) (
II)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
N(x
) (I,
III, I
V)Set II
Set I
Set III
Set IV
-2
-1.5
-1
-0.5
0
0.5
1
1.5
0 100 200 300 400 500 600
Temperature (°C)
Surf
ace
Con
vers
ion
base
d on
N(x
)
Set I
Set II
Set III
Set IV
Figure 5.3.25: Top (a): Effect of temperature on the non-normalized intensity of each individual Set, Centre (b): Effect of temperature on the normalized Set intensity, Bottom (c): Surface conversion of each Set with temperature
132
Based on these results the following observations can be made.
1) Set II ions dominant the surface of the carbon due its high magnitude of
intensity. This set is a mixture of reduced and predominantly oxidized ions
(C*-O ions).
2) Set II ions drop in relative intensity faster than the other groups and reach a
stable surface concentration at temperatures above 400°C. This suggests the
surface oxygen level drops to a stable concentration during the temperature
programming. Works by Fanning30, Jeguirim288, Muckenhuber35,78 indicate that
between temperatures of 100 – 400°C carboxylic acid groups are unstable and
react or desorb. This suggests that the carbon surface is covered with oxygen
containing parent surface groups, possibly carboxylic acid groups.
3) Set IV ions have the lowest surface concentration and decline in surface
concentration in a similar way as Set II ions. Set IV ions consist primarily of C-
O ions. The slower rise in conversion of these surface species suggests that the
parent group containing the C-O group is more strongly bonded than the Set II
oxygen containing parent surface group. From previous work of others, the
temperature range of this desorption suggests that these ions may represent
lactones 16,24,30,35,78.
4) Set III ions increase in surface concentration with temperature up to 400°C.
Above 400°C the surface concentration decreases back to the same level as the
initial concentration at 550°C. The creation of reduced surface groups is
expected with the removal of oxygen containing groups and greater exposure of
the base carbon structure. This set contains CN- type groups. Chu and Schmidt
(1993) 271 report that during the NOX - carbon reaction CNx polymers are
formed on the surface of the carbon. Results from this work suggest that their
observation may be correct due to the increase of CN- and atomic nitrogen
increase in the surface region of the carbon.
5) Set I ions go through a maximum in surface concentration at a temperature of
300°C and then decline. This indicates the formation of surface intermediates
up to 300°C and their reaction or desorption.
133
These observations suggest that during TPD under vacuum there is rapid desorption of weakly
bonded oxygen - containing molecules at low temperatures between 100 to 300°C (Set II,
possibly carboxylic acid groups). A second slightly stronger bonded oxygen group (Set IV)
desorbs from 200 to 400°C (Set IV, possibly lactones). Set I ions suggest that an intermediate
is formed on the surface of the carbon (CNO- type surface groups). Above 300°C these CNO-
groups react or desorb from the surface of the carbon. Set III parent surface groups have two
distinct regions with different rises in temperature. The first region mirrors the rise seen in Set
I from 100 to 300°C. This could be due to the formation of the reduced ions being produced
from the same reaction pathway. Above 300°C the ion surface concentration rises linearly
with temperature primarily due to the decomposition of Set I type parent surface groups.
200-300°C
Set II carbon oxygen products (CO)
Set II and Set IV C-O products (desorbed) + Set III and Set I (adsorbed)
Above 300°C
Set I desorbs rapidly (~340°C)
Set III desorbs slowly (~400°C)
5.3.7 Reactivities of surface ion precursors
5.3.7.1 TOF of ion sets leaving carbon surface In this section, the ToFSIMS data are used to derive net reactivities of the surface species
involved. These reactivities are then compared with the reactivity requirements required of a
low temperature particulate filter. The reactivity values are estimated by using the ToFSIMS
data to determine a site-specific rate of decomposition. This value is translated into an
equivalent carbon oxidation rate under the assumption of a site density on the carbon, as
discussed in Chapter 2. The assumption is made that the ions leaving the surface during
ToFSIMS analysis are a surrogate for the functional groups on the surface. Furthermore, the
functional groups desorbing from the surface during temperature ramping give possible
information on the carbon site reactivity under these analysis conditions. By using the grouped
134
sets, the site specific or “instantaneous” rate (ri, sims) of the ion sets that have sufficient
reactivity to meet the required reactivity of particulate filters are determined.
ri, sims = a
i
NdtdN 1
∗ {5-10}
where dt = 5 minutes, the time of acquisition
and Na = (Ni(t) + Ni(t+1))/2
ri, sims (average) = set
ions
ionsimsi
n
r∑ ,
{5-11}
rg, sims = - simsir , (average) * fs {5-12}
where fs = carbonsites (site density)
Note, ri, sims is an instantaneous rate or TOF. It is not the same as RO,SIMS (normalized to a
constant temperature) that was used earlier for comparison purposes with other ToFSIMS data.
By knowing the specific rate of the functional groups on the carbon surface we can compare it
to the gasification rate needed for filter regeneration and determine if any of the functional
groups (ion sets) have a sufficient TOF.
Earlier in Chapter 4 and Chapter 2 we presented the gasification rate for particulate filter in the
gasification rate plot (Figure 4.3.3, Figure 2.2.5). Figure 5.3.26 presents a similar gasification
plot based on the values of rg, sims. The ri, sims values for the individual Sets are multiplied by an
assumed number of reactive sites per carbon to provide a carbon based reaction rate. The
figure shows that the gasification rate of Sets II and IV increase with temperature while Sets I
and III show a minimum in rate at 300°C. Above 300°C, Set I has a higher rate than Set III.
135
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0 100 200 300 400 500 600
Temperature (°C)
rg,s
ims
(1/h
) (av
erag
e)
Set I Set II Set III Set IV
Figure 5.3.26: Effect of Temperature on the gasification rate for each ion Set leaving the carbon surface. Sample SC_NOX: NOX dosed sucrose char negative ions
Figure 5.3.26 includes all ions that fit into the Set. No ions were excluded because sufficient
information to set criteria for elimination of an ion from a group is not known. The error bars
represent one standard deviation of the ion rates at a specified temperature for the ions in that
ion group (Set). All ions were included for the following reasons:
1) The ions yields are uncertain. As discussed earlier, some ions can create higher
intensities in SIMS even though the surface group concentrations are low. For example
if we consider the ion intensities of the two ions in Set II (H- and C4H5-), the intensity
of the H- ion is 1300000 at 25°C and 130000 at 550°C. The intensity of the C4H5- is
130 at 25°C and 4 at 550°C. The difference in magnitude between intensities of the
two ions is great. However, the change in intensity with temperature is similar in
magnitude. The library for mass fragments from mass spectroscopy of solid surfaces is
small unlike the available large database for gas phase molecules and thus information
on ion yields is small. Additionally the effect of temperature on ion yields is not
known for all compounds.
2) All individual ion rates have a percent deviation greater than 30% indicating that the
ion rate is changing with temperature. This was found by calculating the amount of
136
change in rate with temperature by taking a single ion and averaging the rates over the
temperature range and then calculating the %deviation = stdev/average. For example,
if the % deviation is small (e.g. 10%), there is no change in rate of the ion with
temperature. However, what is considered a small deviation is difficult to determine
and thus an arbitrary number of 20% was chosen. All ions are greater than 30%
deviation suggesting changes in rate with temperature. The maximum deviation is
840%. CN- and CHN- have deviations of 550% and 500%, respectively.
Earlier we defined the steady state rate needed to achieve steady state conversion of the soot in
the exhaust stream at 200°C. The criterion used in early chapters to define an acceptable
carbon gasification rate, RGo, was 0.5 h-1 at a temperature of 200°C. Temperature programmed
oxidation reactivity data shown earlier (Chapter 4) indicate that the RGo of 0.5 at 200°C is
difficult to achieve even under highly favourable conditions such as high NO2 reactant
concentrations and the use of catalysts with intimate contact with carbon. Application of the
same criterion to the Set rates (rg, sims) shows that none of the Sets show sufficiently high rates
to meet the required carbon gasification rate. Similarly to the observed carbon gasification
rates these surface functional group reactivities only show sufficient reactivity at temperatures
greater than 400°C. The highest gasification rate at 200°C is 0.04 by Set II, this is only 2% of
the required reactivity. For most sets the specific rate reaches a maximum of 0.04 only at
temperatures greater than 400°C. All of the sets are below the criteria level and likely are
contributing to a similar reaction pathway.
137
5.3.7.2 Individual ion reactivities The specific rates of selected ions were examined in a similar manner, i.e. the “gasification
rate” of a specific ion is calculated using the individual ion specific rate and correcting for site
density. The calculated gasification rates of the ions are plotted on the gasification plot using a
linear scale for the y-axis (Figure 5.3.27). As seen for the Set results above, the gasification
rates are well below the criteria turnover frequency of 0.5 for all temperatures. The SIMS
reactivities are in substantial agreement with these measurements.
For all temperatures, the Rg, sims of NO-, NO2
-, OH-, CO2-, CO- and CN- type functional groups
do not have rates that are sufficient to meet the criteria level; this indicates that these ions may
not limit the reaction rate. The sum of the rates of CO- + CO2- rises with temperature linearly.
The data indicate that all surface groups with sufficient reactivity have been removed from the
carbon leaving the less reactive groups.
Figure 5.3.27: Specific gasification reaction rate of individual ions for sample SC_NOX. The lines are included to make it easier for the reader to locate the data points and are not intended to show trends.
138
5.3.8 Surface mechanistic considerations The surface reaction of carbon with oxidants as discussed earlier is not clear. In addition,
predicting molecular reactions using ToFSIMS is difficult. The technique is highly destructive
and the creation of the fragmentation pattern is not fully understood. Models using computer
simulation are in their early stages and are breaking new ground into the understanding of the
fragmentation patterns 314,315. Although it is difficult to identify the parent surface groups, here
an attempt is made to postulate surface reaction mechanisms that are supported by observations
in the literature. Surface reaction mechanisms have been proposed based on in-situ FTIR,
DRIFTS measurements 78, TPD-MS 35,79,316, reactor measurements 45,97,288 and have been used
to deduce surface reaction mechanisms. In this section we utilize the ToFSIMS data along
with PCA analysis to deduce the surface reactions occurring during the TP–ToFSIMS
experiment.
Although, the SIMS process is highly complex, there are clearly trends in the TP-ToFSIMS
data that can provide information on surface species and reaction sequences. For example, one
possible reaction sequence involves three possible surface states (Figure 5.3.28). This includes
an initial surface composition containing a number of functional groups (initial surface state -
S1, S2, S3…) that can decompose or react to form a variety of functional groups at
intermediate temperatures (intermediate surface state - I1, I2, I3…). These intermediate
functional groups can follow other reaction pathways to form a number of functional groups at
the final treatment temperatures (final surface state - F1, F2, F3…). In addition, gas phase
products (G1, G2) can be produced by reaction or desorption (e.g. carbon oxides, nitrogen
oxides, water and others). A notional surface composition for each of these states can be
surmised based on the general observations of the TP-ToFSIMS data and is summarized
below:
139
Figure 5.3.28: Reaction Scheme 1
a) Low temperatures: (25°C to 200°C) (Initial Surface Composition) (S1,S2,…)
a. Surface consists of mainly oxygen containing functional groups
b. Oxidized nitrogen species are present on the surface
c. Atomic nitrogen composition is lowest at these temperatures
d. Greater number of oxidized ion species than reduced ions
b) Intermediate temperatures (Intermediate Surface Composition) (I1, I2,…)
a. Reduction in oxidized species indicating loss of oxygen containing functional
groups
b. Increase in NO ion fragments relative to starting state indicating formation of
NO containing surface functional groups
c) Higher temperatures (Final Surface Composition) (F1, F2,..)
a. Further reduction in oxidized species indicating loss of oxygen containing
functional groups and surface containing primarily reduced species.
b. Increase in atomic nitrogen composition indicating surface enrichment of
nitrogen.
c. Decrease in NO containing ion fragments relative to intermediate temperatures
indicating loss of NO containing surface functional groups
G2G1
S2
S3
…
I1
I2
I3
…
F1
F2
F3
…
Increasing Temperature
S1
140
Using information from detailed rate analysis and published literature, the number of possible
surface functional groups can be deduced for the oxygen and nitrogen reaction pathways. This
information is discussed below and used to postulate a reaction mechanism.
5.3.8.1 Summary of oxygen dosed sample observations The oxygen reaction pathway is found from examining the SC_AIR sample TP_ToFSIMS
data. This sample was annealed at 700°C for 8 hours and cooled under a helium atmosphere.
The sample was exposed to air (20% oxygen) at room temperature (~25°C). For this sample,
PCA was used to segregate the ion fragments into groups with similar rate behaviour.
However, detailed rate analysis was not performed (i.e. segregation of ion fragments into
similar rate behaviour groups).
A cursory comparison of the PCA biplots for the negative ions of the sucrose char samples
(SC_AIR and SC_NOX (Figure 5.3.24b, and Figure 5.3.22b, respectively)) shows that the
curved ion band found on the right of the plot is shifted relative to temperature. This curved
ion band was attributed to Set II in the SC_NOX detailed rate analysis and was found to
contain primarily oxygen containing-ion fragments. While this ion band on SC_NOX is
centered over 400°C, it is shifted on the SC_AIR sample to a region between 400°C and
500°C. This would indicate a temperature delay in the evolution of the oxygen –containing
functional groups on the air-exposed sample and that the presence of NOX or different
intermediates may improve the evolution temperature. Further experimentation and analysis is
needed to confirm this observation.
The literature is populated with many studies on possible oxygen containing species on carbon.
Muckenhuber et al. 35 and references therein discuss the decomposition temperatures of various
oxygen containing species found on carbon. From this information the oxygen species being
desorbed from the SC_AIR sample between the temperatures of 400°C and 500°C may
correspond to oxygen containing functional groups such as carboxylic acids, lactones, and
carboxylic anhydrides. These surface species are formed during the air exposure or are formed
by reaction of the oxygen with carbon during the temperature ramping. Similarly these surface
141
groups may be formed during the reaction of NOX with carbon. This reaction is discussed
below.
A review of the literature for SIMS fragmentation information on model compounds found
mostly positive ion spectra 301,309,317. Many of the positive ions from these model compounds
are similar to those measured on the SC_AIR sample. An attempt to relate the ToFSIMS data
to model compounds was not successful but some ion fragments for the positive ions were
similar to dibutyl phthalate 301, nitro-cellulose 301, polysaccharide coatings 317, and bisphenol-A
polycarbonate 318 and polyethyleneterephthalate (PET) blends 319. However, this comparison is
not perfect and is difficult due to the large number of ions; in addition the molecular
assignments on the SC_NOX sample may not be entirely correct at the molecular weights
greater than 100amu due to the numerous ion possibilities. Further experimentation is needed
on model compounds and analyzed using PCA to determine the source of the ions on the
carbon surface.
5.3.8.2 Summary of NO2 dosed sample observations, SC_NOX
The most interesting information is from the NOX dosed sample (SC_NOX sample). This data
were analyzed using PCA and detailed rate analysis to identify similarly desorbing ion
fragments. The TP-ToFSIMS data clearly show the carbon surface composition is changing
with temperature and can be classified into at least three observed surface states as in Scheme 1
(Figure 5.3.28) (initial surface composition-low temperature, intermediate surface
composition-intermediate temperatures and final surface composition - high temperature.).
5.3.8.2.1 Initial surface composition, SC_NOX
In the starting state (i.e. low temperatures), the SIMS indicates primarily oxygen containing
groups and oxygen containing nitrogen groups. With the application of the temperature ramp
between 25°C and 200°C, ToFSIMS data show an evolution of the oxygen containing groups.
Also in this temperature range some ions, such as the NO2- ion, show a large desorption spike.
Looking at the NO2- ion gasification rate with temperature, it is observed that the NO2
- rate is a
142
maximum at 100°C and then drops rapidly to zero at 200°C. Similarly, the ion C2HNO3- is
observed to have a low gasification rate at 100°C and a high gasification rate at 200°C and then
drops to zero at 300°C. This would indicate that the ion is reducing its population on the
surface of the carbon at a desorption temperature between 100°C and 200°C. To determine the
exact temperature of desorption the TP-ToFSIMS experiment would have to be performed at
shorter temperature intervals. For these cases, the NO2- ion fragment and possibly the NO3
- ion
fragment may represent molecular NO2 adsorbed to the carbon. A related observation was
made by Azambre et al 316, who examined the reactivity of diesel soot with flowing 1000ppm
NO2/bal Ar (60mL/min) at temperatures between 25°C and 200°C using TGA, FTIR and TPD-
MS. The TPD-MS results showed that molecular NO2 is weakly bonded to the carbon surface
and is desorbed at ~60°C, while a weak desorption signal was also seen around 200°C.
Likewise, Jegurim et al 288 report that a sharp desorption peak of NO2 is observed with a
maximum at 110°C indicating that a significant quantity of NO2 was sorbed on the carbon
surface during the adsorption step and was completely desorbed by 200°C. An additional two
peaks attributed to NO and CO2 are observed simultaneously and have maximum desorption
peaks at 150°C. Additionally, Muckenhuber et al. 78 and Azambre et al. 316 suggest NO2 forms
an acidic functional group on the soot samples, which decomposes at about 140°C into CO2
and NO 78. This evidence supports the observation that the oxygen containing nitrogen ions
released during TP-ToFSIMS below 200°C are likely to arise from weakly bonded nitrogen
oxides. Based on prior IR assignments, the initial surface composition of the carbon is likely
to contain adsorbed NO2 and NO with oxygen species such as lactones, carboxylic acids, and
carboxylic anhydrides. These NO containing functional groups would need to react with the
carbon to account for the increase in N content in the final state.
5.3.8.2.2 Final surface composition, SC_NOX
The TP_SIMS experiment suggests that the surface of the carbon is enriched with nitrogen
after exposure to nitrogen oxides however the mechanism for this enrichment is uncertain with
little evidence in the literature. There are reports that can help identify the carbon – nitrogen
surface functionalities 45,86,97,304. Biniak et al. 86 used XPS to identify the surface functional
143
groups of a commercial activated carbon (D43/1) after each individual treatment. The
treatments included de-ashing and high temperature annealing (1000K) under vacuum (10-2 Pa)
followed by oxidation with nitric acid at 353K or ammonia treatments at >1000K. The surface
is reported to contain primarily pyridinic structures after the initial high temperature anneal and
subsequent low temperature nitric acid treatment. This suggests that this low temperature acid
treatment was inadequate to modify the surface and possibly high temperatures are needed to
cause N to be incorporated into the ring. In addition, Kaptejin et al 304 used XPS to study
nitrogen functionality development during burnoff on model chars (sucrose and
phenolformaldehyde) after high temperature (1373K) exposure in N2 followed by CO2 (900°C)
and O2 (580°C) exposure. After the high temperature exposure in N2, they report the presence
of pyridinic nitrogen, pyridones, and oxidic nitrogen species on the edge of the graphene
structure with quaternary nitrogen incorporated in the structure. Suzuki et al. 97 also observed
the incorporation of N during the reaction of C + NO at 600°C by XPS and reaction studies.
However, XPS results on the initial surface composition of the SC_NOX (pre-dosed NOX
samples) were below the detection limit (>0.1%) of the XPS. This indicates that, at least in the
initial surface state, the N- containing ions measured by ToFSIMS in this study were primarily
from the carbon surface. The inability to measure N using XPS is interesting. This may be
due to the TOFSIMS experiment only examining a single turnover of the carbon surface during
a single adsorption step. While in other studies 45,97,304 the carbon surface experiences several
turnovers and may have accumulated sufficient N on the surface to be detectable by XPS. An
interesting experiment would be to perform successive ToFSIMS, NO2 treatment, ToFSIMS
cycles followed by XPS to track the N enrichment on the carbon surface.
Additionally during the O2 exposure of these chars, Kapteijn observed that N accumulates with
burnoff 304. A similar observation was reported by Ashman et al. 45 for coal char oxidation in
2%O2/bal He at 873K with N retention as primarily pyridinic N. These reports indicate that the
N containing ions detected by the ToFSIMS in the final state are likely from nitrogen
incorporated into the carbon ring and are probably retained as pyridinic N or pyrollic N. In
order to better understand how the N is incorporated into the carbon structure an understanding
of the intermediate state is also needed.
144
5.3.8.2.3 Intermediate surface composition, SC_NOX
The intermediate state holds the key to understanding how nitrogen is retained into the carbon
surface. The TP-ToFSIMS rate grouping data show that NO containing ion fragments are
being formed around 400°C with a shift to N-containing ions with increasing temperatures
along with a reduction of NO2 ions. This implies that the NO2 observed at the initial state is
also decomposing leaving behind C-NO type functional groups. These C-NO functional
groups eventually decompose/react to create CN type functional groups that may represent N
attached to the edges of or incorporated into the graphene structure.
IR based temperature studies report the presence of C-NO2, C-NO, C-NCO and anhydride type
structures around 400°C 31,78. Muckenhuber et al. 78 have interpreted DRIFTS bands at
1610cm-1 and 1230cm-1 as characteristic of N=O and C-O vibrations in C-ON=O at 400°C.
They also suggest that at 400°C, an acidic functional group is the transition state (C(=O)ONO).
From this group, NO is split and the C(=O)O functionality remains on the surface.
Jeguirim et al using reactor studies observed the rapid formation of NO 288. They postulate the
C(NO2) complex from their proposed mechanism (Equation 5-13 (a-c)) is probably unstable
and rapidly decomposes. They also suggest that a relationship exists between excess CO2
released at 300°C and additional NO2 adsorbed on the carbon surface may indicate that NO2
interacts with oxygen complexes formed during O2 pretreatment. This data supports the
observation of lower temperature loss of oxygen functionality from the PCA TP-ToFSIMS
data discussed earlier (O2 summary above). However, Jeguirim et al also report that
pretreating the carbon with O2 prior to NO2 exposure results in earlier desorption of the carbon
oxygen complexes 288. Recall that the SC_NOX sample was thermally annealed in He prior to
NO2/NO/O2 exposure and was further exposed to air during the transfer to the vacuum
chamber. However, the TP-ToFSIMS data suggest that oxygen need only be present on the
surface with NO2 to cause earlier desorption. The extent of earlier desorption cannot be
determined without additional analysis and experimentation.
145
-C* +NO2 -C(NO2) {5-13a}
- -C(NO2) -C(O) + NO {5-13b}
- -C(O) + NO2 -C(ONO2) {5-13c}
Equation 5-13 (a-c): Reaction mechanism proposed by Jeguirim et al 288
5.3.8.3 Hypothetical surface and reaction pathway Here, a generalized reaction pathway and a hypothetical surface composition are proposed to
explain the observations from the TP-ToFSIMS data for the NOX dosed sample. In the initial
surface composition the carbon edges are likely populated with oxygen containing functional
groups such as lactones, carboxylic acids, and carboxylic anhydrides. In addition, the dosing
with the NO/NO2/O2 mixture forms weakly adsorbed NO2 and possibly NO on the carbon
surface. As the temperature is increased from the starting state to the intermediate state
(300°C-400°C)) the adsorbed NO2 has at least two possible reaction pathways: 1) weakly
adsorbed species are desorbed into the gas phase (100°C-200°C), and 2) there is rearrangement
on the carbon surface where C-NO type functional groups and C-O type functional groups are
formed (200°C-400°C). These C-O type groups either remain on the surface or are desorbed.
Further increasing the temperature to the final state (500°C-550°C) causes the C-NO
containing functional groups to be further reduced to CN- containing functional groups. At
this final stage the carbon structure consists of primarily pyridine-N, and possibly quaternary N
type structures. How the NOX adsorbed in the initial step gets to the final stage is uncertain.
The following reaction mechanisms are proposed for an NO2 –carbon reaction; although NO is
not discussed it is reported to play a role in N-ring formation 97.
In the initial state the NO2 has three possible configurations for bonding to the carbon surface
(Figure 5.3.29). These are by forming a chelated bond, non-chelated bond and/or bonding
through the N atom in the NO2 molecule. These structures are supported by DRIFTS data,
although the DRIFTS assignment of between 1485cm-1 and 1330 cm-1 for NO2 bonding to
carbon via the N atom is vague 78.
146
NO O
+N
O
O
N+
OH
O
Chelated Non-chelated Bonding via N - atom
Figure 5.3.29: Examples of NO2 bonding to carbon
One possible reaction pathway is to consider the NO2 bonds to the carbon surface at an
armchair site (Figure 5.3.30). One of the oxygen atoms on the NO2 molecule could be
transferred to a neighboring carbon site. This oxygen would cleave the ring structure and
produce carbon monoxide. It is speculated that the dangling N – O structure would close the
ring by bonding via the nitrogen. This could occur at intermediate temperatures where the TP-
ToFSIMS results show the greatest value of NO type ion fragments. As temperature increases,
the remaining oxygen bonded to the nitrogen is removed by some unclear mechanism leaving
behind the nitrogen incorporated into the carbon ring structure. The nitrogen is incorporated
into carbon structure as either a five or six membered ring. However, assuming that the
DRIFTS assignment by Muckenhuber et al. 78 is correct, the peak representing NO2 bonded via
the nitrogen atom disappears after heating from room temperature to above 100°C. This
suggests that the weakly adsorbed NO2 species may be bonded via the nitrogen atom.
147
C-
O+
N+
O
O-
N-
O-
N2-
O+
C-NH
N+O
-
ON
+
O
O
N
O-
O
OH
NH
OR
+
+
Figure 5.3.30: Reaction Scheme 2 - Generalized surface reaction mechanism with NO2 bonding via nitrogen atom to the carbon surface
A second possible mechanism is that in the initial state, NO2 is bonded to an armchair site via
the oxygen atom (Figure 5.3.31- Scheme 3). In this case, as temperature increases the oxygen
bonds to an adjacent carbon and is cleaved leaving a C-O bond and a C-NO bond. This is
consistent with the TP-ToFSIMS for NO type fragments and follows the reported IR data 31,78
for the presence of C-NO bonds. Further rearrangements allow for the evolution of CO, CO2
or H2O from the carbon surface and eventual N-incorporation within the carbon structure as a
five-member or six-member ring. Although additional evidence is needed, the weakly bonded
NO2 fragments at the initial state may be attributed to NO2 bonded via the nitrogen atom.
These proposed reaction pathways are consistent with the TP-ToFSIMS data and the literature
for each surface state. Another possible reaction pathway is the formation of a C-NO3 group
148
on the surface that follows a similar pathway as reaction schemes 2 and 3. Although these TP-
ToFSIMS data provide supporting evidence for possible reaction pathways further
investigations are needed to clarify the intermediate states. Recommendations for future
studies are discussed below.
CH2
O+
N-NO
O
NO
O+
O+
N
C-
O+
NH C-
O++
+
Figure 5.3.31: Reaction Scheme 3 - Generalized surface reaction mechanism of NO2 bonding via the oxygen atom to the carbon surface
149
5.4 Conclusions
1) Nitrogen content on the carbon surface increases during temperature ramping in a
vacuum environment after pre-dosing with NOX.
2) Two reaction pathways are proposed for NO2 bonding to an armchair site at low
temperatures and eventually forming pyridine –N or quaternary N in the carbon ring.
Intermediate surface structures contain NO type functional groups.
3) Large dataset of ions created during TP-ToFSIMS can be simplified to a few kinetic
sets by using principal component analysis (PCA), correlation inspection (C-I) with
integral rate method.
4) Sets using integral rate method are both kinetically and chemically distinct. It is
possible to narrow down TP-ToFSIMS sets to a few possible functional groups using
other reported analytical techniques for carbon.
5) Identified that there are four distinct sets for sample SC_NOX. Set II loses oxygen
quickly and reaches a stable surface concentration at approximately 300°C. Set IV is
initially created at low temperature and is then removed at a slower rate than Set II. Set
IV is identified to be possibly carboxylic acid, lactone, or carboxylic anhydride groups.
Set I are surface intermediates with a maximum concentration at 300°C. Set IV
represents the reduced structure and has two regions: 1) an initial increase in surface
concentration that mirrors Set I followed by 2) a linear increase in surface
concentration.
6) Initial surface composition changes of NOX functional groups (between 25°C and
200°C) occur within the area of relevance for low temperature soot filter regeneration.
7) Further investigation is needed to determine if the PCA, correlation- inspection method
used here can help group ion fragments for fingerprinting the parent functional groups
on the carbon.
8) Ion groupings produced using the integral method are different from thos e of the
differential method. The reason is unclear but it is suspected that time period of
analysis may play a role.
150
5.5 Recommendations for future work
1) Collect ToFSIMS spectra of model compounds to help isolate and identify surface
species on the carbon surface.
2) Track N accumulation on the carbon surface with reaction turnover using successive
ToFSIMS and NO2 dosing cycles. As well, monitor the surface concentration of N
using XPS. This, of course, will not be possible until the N concentration reaches the
detection limit of the XPS.
3) Simultaneously monitor evolved gas phase components and surface concentrations
(ToFSIMS and/or XPS) to complete mass balance and deduce surface reaction
mechanism.
4) Use a carbon source that has fewer contaminants than the materials used here such as
pure graphite.
5) Continuously acquire ToFSIMS spectra to determine if the integral and differential
methods can create similar ion groupings.
151
6 Conclusions and Recommendations
The objective of the thesis was to understand the limiting factors of carbon oxidation at
temperatures of 200°C. An initial literature survey was performed to test if selected catalytic
materials can oxidize carbon at a rate necessary to maintain a steady state carbon oxidation that
would be necessary for soot outputs of a modern diesel engine equipped with a diesel
particulate filter. The literature survey reveals that few catalysts can meet this criterion.
Selected carbon samples impregnated with alkali catalyst at loadings of K/C of 1/50 mol ratio
were evaluated in this thesis and found to not meet this criterion. The results of this thesis are
similar to literature catalysts with higher K/C ratios and with K on different supports.
Additionally, the selected catalysts were evaluated in a gas atmosphere with high NO2 content
(1%) that is higher than concentrations seen in engine exhaust; this was also found to not meet
the criteria. However, the data show that catalyzing the C-O2 reaction shows a greater
improvement in reactivity over catalyzing the C-NO2 reaction. Further studies are needed to
clarify if the catalyst is only creating NO2 for the C-NO2 reaction or is directly involved in
catalyzing the C-NO2 reaction in the reactant gas mixture (1% NO2, 1.5% NO, and 4.5% O2).
Additional screening experiments were performed to identify if thermal annealing of the
carbon would affect its reactivity through changes in its morphology. Extended thermal
anneals under inert atmospheres followed by oxidizing conditions indicate that the carbon
reactivity decreases. This study provides an upper limit to reactivity losses (maximum 40%)
that are contributed by thermal annealing in a non-reactive gas atmosphere. However, these
conditions are not experienced by diesel particulate filters and suggest that thermal annealing is
a minor contributor to carbon reactivity losses in DPF systems. This loss of reactivity could be
attributed to either morphology changes or to loss of reactive functional groups on the carbon
surface. To further explore this possibility the experimental technique of TP-ToFSIMS was
used to examine the surface groups on the carbon. Published literature on this technique as
applied to carbons was not found making this the first reported study to apply this technique to
determine the reactivity of carbons.
152
TP-ToFSIMS showed that the carbon surface changes with temperature. The data clearly show
that carbon exposed to NOX has a higher N content on the surface as temperature increases in a
non-reactive gas atmosphere. Using the rate of change of the ion fragments and PCA to group
the ions with similar rate trends, the large quantity of ion fragments was separated and grouped
into four ion sets. Each of the sets corresponds to distinct chemical species with similar
kinetics. Set II loses oxygen quickly and reaches a stable surface concentration at approx.
300°C. Set IV is initially created at low temperature and is then removed at slower rate than
Set II. Set IV is identified as possibly representing lactone type surface groups. Set I ions are
surface intermediates with a maximum concentration at 300°C. Set IV represents the reduced
carbon surface and has two regions: an initial increase in surface concentration that mirrors Set
I followed by a linear increase in surface concentration. Identification of the source of the ion
fragments was attempted but was difficult due to the limited information on ion fragmentation
patterns from carbon surfaces. Further work is needed identifying ion fragments produced
from the carbon surface during SIMS. One method is SIMS studies of model compounds to
create ion fragments that can be related to the carbon surface. However, even without this
detailed information it was possible to deduce a reaction mechanism for the interaction of NO2
with the carbon surface.
Using this TP-ToFSIMS data, two reaction pathways are proposed for NO2 bonding to an
armchair carbon site at low temperatures that rearranges and eventually forms pyridine –N or
quaternary N in the carbon ring. Although, the intermediate steps for incorporation of N into
the carbon ring are not proven here there are three clear observations: 1) At low temperatures
NO2- ions are present and disappear at temperatures greater than 200°C. 2) The TP-ToFSIMS
clearly identified NO type functional groups as surface structures at intermediate temperatures
around 300°C to 400°C. 3) N is clearly accumulating and forming a stable structure on the
carbon surface and is incorporated into the carbon ring.
A few questions arise from this observation of N incorporation into the carbon ring. Does the
increasing N content on the carbon surface affect the reactivity of the carbon? Furthermore
only select fragments were examined in the TP-ToFSIMS data. The other inorganic ion
fragments may affect the rate of the carbon surface and may provide further insight into the
carbons’ reactivity. In summary, no rate processes meet the steady state gasification criteria
153
during reactivity studies or surface functional group studies and the reason for this is not
understood. However this study clearly shows that the technique of TP-ToFSIMS can provide
pertinent reactivity data and chemical surface information of the reactions of oxidants with the
carbon surface and provide clues towards reaction mechanisms.
154
7 References 1. Chameides WL; Bergin M. Climate change – soot takes center stage. Science
2002, 297, 2214.
2. Dockery DW; Pope CA; Xu XP; Fay Me; et al An association between air pollution and mortality in six US cities. New Engl J Medicine 1993, 29, 1753.
3. US EPA . US EPA, Federal Register/Vol66, No. 12/ Thursday January 18, 2001/ Rules and Regulations. I.B. 3. 2001.
4. Johnson TV Diesel Emission Control: 2001 in Review. SAE Paper 2002, 2002-01-0285.
5. Walker AP Controlling particulate emissions from diesel vehicles. Topics in Catalysis 2004, 28, 165-170.
6. Dementhon JB; Belot G; Jeuland N; Momique J-C; Bruchet D Performances and Durability of DPF (Diesel Particulate Filter) Tested on a Fleet of Peugeot 607 Taxis: Final Results. SAE Paper 2004-01-0073 2004.
7. Watanabe T; Kawashima K; Tagawa Y; Tashiro K; Anoda H; Ichioka K; Sumiya S; Zhang G New DOC for Light-Duty Diesel DPF System. SAE Paper 2007, 2007-01-1920.
8. Chatterjee S; Hallstrom K; Alleman T; Kimura K Long-Term Durability of Passive Diesel Particulate Filters on Heavy-Duty Vehicles. SAE Paper 2004, 2004-01-0079.
9. Stanmore BR; Tschamber V; Brilhac J-F Oxidation of carbon by NOx, with particular reference to NO2 and N2O. Fuel 2008, 87, 131-146.
10. Vander Wal RL; Tomasek AJ Soot nanostructure: dependence upon synthesis conditions. Combustion and Flame 2004, 136, 129-140.
11. DaCosta HFM; Shannon CM; Silver RG Durability of Diesel Particulate Filters - Bench Studies on Cordierite Filters. DEER Conference 2006, http://www1.ere.energy.gov/vehiclesandfuels/resources/proceedings/index.html, last accessed July 10, 2008.
12. Marsh H Introduction to Carbon Science; Butterworth & Co: Cornwall, 1989.
13. Stanmore BR; Brilac JF; Gilot P The oxidation of soot: a review of experiments, mechanisms and models. Carbon 2001, 39, 2247.
155
14. Franklin R Interpretation of Diffuse X-ray Diagrams of Carbon. Acta. Cryst. 1950, 3, 107-121.
15. Franklin R Crystallite Growth in Graphitizing and Non-Graphitizing Carbons. In Series A, Mathematical and Physical Sciences; 1951; pp 196-218.
16. Boehm HP Surface oxides on carbon and their analysis: a critical assessment. Carbon 2002, 40, 145.
17. Langley LA; Villanueva DE; Fairbrother DH Quantification of surface oxides on carbonaceous materials. Chem. Mater. 2006, 18, 169.
18. Smith DM; Chughtai AR The surface structure and reactivity of black carbon. Colloids and Surfaces A: Physiochemical and Engineering Aspects 1995, 105, 47. Figure reprinted with permission from Elsevier.
19. Vander Wal RL; Mueller CJ Initial investigation of effects of fuel oxygenation on nanostructure of soot from a direct-injection engine. Energy and Fuels 2006, 20, 2364-2369.
20. Braun A; Mun BS; Huggins FE; Huffman GP Carbon speciation of diesel exhaust and urban particulate matter NIST standard reference materials with C(1s) NEXAFS spectroscopy. Environmental Science and Technology 2007, 41, 173-178.
21. Mims CA Catalytic Gasification of Carbon: Fundamentals and Mechanisms. Kluwer Acadamic Publishers: Netherlands, 1991; pp 383-407.
22. Marsh H A tribute to Philip L Walker. Carbon 1991, 29, 2. Figure reprinted with permission from Elsevier.
23. Bansal RC; Donnet JP In Carbon Black: Science and Technology, 2 ed.; Donnet JP, Ed.; Marcel Dekker: New York, 1993; p 175.
24. Boehm HP Some aspects of the surface chemistry of carbon blacks and other carbons. Carbon 1994, 32, 759-769.
25. Montes-Moran MA; Suarez D; Menendez; JA; Fuente E On the nature of basic sites on carbon surfaces: An overview. Carbon 2004, 42, 1219-1225. Figure reprinted with permission from Elsevier.
26. Fiqueiredo JL; Freitas MMA; Orfao JJM; Pereira MFR Modification of the surface chemistry of activated carbons. Carbon 1999, 37, 1379-1389.
27. Chughtai AR; Welch WF; Akhter MS; Smith DM A spectroscopy study of gaseous products of soot - oxides of nitrogen/water reactions. Applied Spectroscopy 1990, 44, 294-298.
156
28. Chughtai AR; Gordon SA; Smith DM Kinetics of the hexane soot reaction with NO2/N2O4 at low concentration. Carbon 1994, 32, 405-416.
29. Sellitti C; Koenig JL; Ishida H Surface characterization of graphitized carbon fibers by attenuated total reflection fourier transform infrared spectroscopy. Carbon 1990, 28, 221.
30. Fanning PE; Vannice MA A DRIFTS study of the formation of surface groups on carbon by oxidation. Carbon 1993, 31, 721.
31. Zawadski J In Chemistry and Physics of Carbon, Thrower PA, Ed.; Marcel Dekker: New York, 1989; p 147.
32. Meldrum BJ; Rochester CH Insitu infrared study of the modification of the surface of activated carbon by ammonia, water and hydrogen. Journal of the Chemistry Society Faraday Transactions 1990, 86, 1881.
33. Moreno-Castilla C; Lopez-Ramon MV; Carrasco-Marin F Changes in surface chemistry of activated carbons by wet oxidation. Carbon 2000, 38, 1995.
34. Zhuang Q-L; Kyotani T; Tomita A DRIFT and TK/TPD analyses fo surface oxygen complexes formed during carbon gasification. Energy and Fuels 1994, 8, 714-718.
35. Grothe H; Muckenhuber H The heterogeneous reaction between soot and NO2 at elevated temperature. Carbon 2006, 44, 546-559. Figure reprinted with permission from Elsevier.
36. Vander Wal RL; Bryg V; Hays MD X-ray photoelecton spectroscopy (XPS) Applied to soot & what it can do for you. DEER Conference 2006, http://www1.ere.energy.gov/vehiclesandfuels/resources/proceedings/index.html, last accessed July 10, 2008.
37. Albers PW; Klein H; Lox ES; Seibold K; Prescher G; Parker SF INS-, SIMS- and XPS- investigations of diesel engine exhaust exhaust particles. Phys. Chem. Chem. Phys. 2000, 2, 1051.
38. Puri BR In Chemistry and Physics of Carbon, Walker PL, Ed.; Marcel Dekker: New York, 1970; p 191.
39. Bansal RC; Donnet J-B Surface Groups on Carbon Blacks. In Carbon Black Science and Technology Revised and Expanded, 2 ed.; Donnet J-B, Bansal RC, Wang M-J, Eds.; Marcel Dekker: New York, 1993.
40. Rivin D Surface properties of carbon. Rubber Chemistry Technology 1971, 44, 307-343.
157
41. Rivin D Use of lithium aluminum hydride in the study of surface chemistry of carbon black. Rubber Chemistry Technology 1963, 36, 729-739.
42. Garten VA; Weiss DE Ion- and electron - exchange properties of activated carbon in relation to its behavior as a catalyst and adsorbent. Rev. Pure Appl. Chem. 1957, 7, 69-122.
43. Vinke P; Van der Eijk M; Verbree M; Voskamp AF Modification of the surfaces of a gas activated carbon and a chemically activated carbon with nitric acid, hypochlorite, and ammonia. Carbon 1994, 32, 675.
44. Ishizaki C; Marti I Surface oxide structure on a commercial activated carbon. Carbon 1981, 19, 409.
45. Ashman PJ; Buckley AN; Haynes BS; Nelson PF The fate of char-nitrogen in low-temperature oxidation. Proceedings of the Combustion Institute 1998, 27, 3069-3075.
46. Rangel-Mendez JR; Streat M Adsorption of cadmium by activated carbon cloth: influence of surface oxidation and solution pH. Water Res. 2002, 36, 1244.
47. Chen JP; Wu SN Acid/base-treated activated carbons: Characterization of functional groups and metal adsorptive properties. Langmuir 2004, 20, 2233.
48. Valdes H; Sanchez-Polo M; Riviera-Utrilla J; Zaror CA Effect of ozone treatment on surface properties of activated carbon. Langmuir 2002, 18, 2111.
49. Briggs D Interpretation of Spectra. In ToFSIMS: Surface Analysis by Mass Spectroscopy, 2 ed.; Vickerman JC, Briggs D, Eds.; IM Publications and Surface Spectra Limited: 2001.
50. Briggs D Surface Characterization of Polymers. In ToFSIMS: Surface Analysis by Mass Spectroscopy, Vickerman JC, Briggs D, Eds.; IM Publications and Surface Spectra Limited: 2001.
51. Franklin R Changes in the Structure of Carbon during Oxidation. Nature 1957, 108, 1190-1191.
52. Laine NR; Vastola FJ; Walker PL The importance of active surface area in the carbon-oxygen reaction. Journal of Physical Chemistry 1963, 67, 2030.
53. Boudart M; Djega-Mariadassou G Kinetics of heterogeneous catalytic reactions; Princeton University Press: Princeton, 1984.
54. Stein SE; Brown RL Chemical theory of graphite-like molecules. Carbon 1985, 23, 105-109.
158
55. Thomas JM In Chemistry and Physics of Carbon, Walker PL, Ed.; Marcel Dekker: New York, 1965; p 121.
56. Smith RN; Young DA; Smith RA Infra-red study of carbon-oxygen surface complexes. Transactions of the Faraday Society 1966, 62, 2280.
57. Sendt K; Haynes B Density functional study of the reaction of carbon surface oxides: The behavior of ketones. Journal of Physical Chemistry A 2005, 109, 3438-3447.
58. Calo JM; Hall PJ Applications of Energetic Distributions of oxygen surface complexes to carbon and char reactivity and characterization. In Fundamental Issues in Control of Carbon Gasification Reactivity, Kluwer Academic Publishers: Netherlands, 1991; pp 383-407.
59. Walker PL; Austin LG; Nandi SP In Chemistry and Physics of Carbon, Walker PL, Ed.; Marcel Dekker: New York, 1966.
60. Ismail IMK; Walker PL Detection of low temperature carbon gasification using DSC and TGA. Carbon 1989, 27, 549-559.
61. Walker PL; Taylor RL; Ranish JM An update on the carbon-oxygen reaction. Carbon 1991, 29, 411-421.
62. Radovic LR; Walker PL; Jenkins RG Importance of carbon active sites in the gasification of coal chars. Fuel 1983, 62, 849.
63. Day RJ; Walker PL; Wright CC The carbon-oxygen reaction at high temperature and high gas flow rates. SCI: London, 1975; pp 348-370.
64. Vastola FJ; Hart PJ; Walker PL A study of carbon -oxygen surface complexes using O18 as a tracer. Carbon 1964, 2, 65-71.
65. Walker PL; Shelf M; Anderson RA In Chemistry and Physics of Carbon, Walker PL, Ed.; Marcel Dekker: New York, 1968; p 287.
66. Hennig GR In Chemistry and Physics of Carbon, Walker PL, Ed.; Marcel Dekker: New York, 1961; p 1.
67. Phillips R; Vastola FJ; Walker PL The effect of oxygen pressure and carbon burnoff on the product ratio of the carbon-oxygen reaction. Carbon 1969, 7, 479-485.
68. Yezerets A; Currier N; Epling W; Kim D-H; Peden C; Muntean G; Wang C; Burton S; Vander Wal R Towards Fuel Efficient DPF Systems: Understanding the Soot Oxidation Process. DEER Conference 2005, http://www1.ere.energy.gov/vehiclesandfuels/resources/proceedings/index.html, last accessed July 10, 2008.
159
69. Yezerets A; Currier NW; Kim DH; Eadler HA; Epling WS; Peden CHF Differential kinetic analysis of diesel particulate matter (soot) oxidation by using a step-response rechnique. Applied Catalysis B 2005, 61, 120-129.
70. Yezerets A; Currier NW; Eadler HA; Suresh A; Madden PF; Branigin MA Investigation of the oxidation behaviour of diesel particulate matter. Catalysis Today 2003, 88, 17-25.
71. Ahmed S; Back MH; Roscoe JM A kinetic model for the low temperature oxidation of carbon. Combustion and Flame 1987, 70, 1-16.
72. Tong SB; Pareja P; Back MH Correlation of the reactivity, the surface area and the total surface area of thin films of pyrolytic carbon. Carbon 1982, 20, 191-194.
73. Bokova M; Decarne C; Abi-Aad E; Prykahin A; Lunin V; Aboukais A Kinetics of catalytic carbon black oxidation. Thermochimica Acta 2005, 428, 165-171.
74. Cooper BJ; Jung HJ; Thoss JE US 4902487, 1990.
75. Cooper BJ; Thoss JE Role of NO in diesel particulate emission control. SAE 1989, 890404.
76. Jacquot F; Logie V; Brilhac JF; Gilot P Kinetics of the oxidation of carbon black by NO2. Influence of the presence of water and oxygen. Carbon 2002, 40, 335-343.
77. Jacquot F; Brilhac JF Soot oxidation by O2 and/or NO2 in the presence of catalysts under lean burn and rich atmospheres. SAE 2004, 2004-01-1943.
78. Muckenhuber H; Grothe H A DRIFTS study of the heterogeneous reaction of NO2 with carbonaceous materials. Carbon 2007, 45, 321-329.
79. Muckenhuber H; Grothe H The reaction between soot and NO2 - investigation on functional groups using TPD-MS. Topics in Catalysis 2004, 30, 287-291.
80. Bokova M; Decarne C; Abi-Aad E; Prykahin A; Lunin V; Aboukais A Effects of ozone on the catalytic combustion of carbon black. Applied Catalysis B 2004, 54, 9-17.
81. Kamm S; Saathoff H; Naumann KH; Mohler O; Scharath U Gasification of a soot aerosol by O3 and NO2: Temperature dependence of the reaction probability. Combustion and Flame 2004, 138, 353-361.
82. Gomez-Serrano V; Alvares PM; Jaramillo J; Beltran FJ Formation of oxygen complexes by ozonation of carbonaceous materials prepared from cherry stones I. Thermal Effects. Carbon 2002, 40, 513-522.
160
83. Gomez-Serrano V; Alvares PM; Jaramillo J; Beltran FJ Formation of oxygen complexes by ozonation of carbonaceous materials prepared from cherry stones II. Kinetic study. Carbon 2002, 40, 523-529.
84. Chen SG; Yang RT Unified mechanism of alkali and alkaline earth catalyzed gasification reactions of carbon by CO2 and H2O. Energy and Fuels 1997, 421-427.
85. Haynes BS A turnover model for carbon reactivity I. Development. Combustion and Flame 2001, 126, 1421-1432.
86. Biniak S; Szymanski G; Siedlewdki J; Swiatkowski A The characterization of activated carbons with oxygen and nitrogen surface groups. Carbon 1997, 35, 1799-1810.
87. Stohr B; Boehm HP; Schlogl R Enhancement of the catalytic activity of activated carbons in oxidation reactions by thermal treatment with ammonia or hydrogen cyanide and observation of a superoxide species as a possible intermediate. Carbon 1991, 29, 707.
88. Zawadski J In Chemistry and Physics of Carbon, Thrower PA, Ed.; Marcel Dekker: New York, 1988; pp 147-380.
89. Hurt RH; Haynes BS On the origin of power-law kinetics in carbon oxidation. Proceedings of the Combustion Institute 2005, 30, 2161-2168.
90. Suuberg EM; Wojtowicj M; Calo JM Reaction order for low-temperature oxidation of carbons. Proceedings of the Combustion Institute 1989, 22, 79-87.
91. Suuberg EM; Wojtowicj M; Calo JM Some aspects of the thermal annealing process in a phenol-formaldehyde resin char. Carbon 1989, 27, 431-440.
92. Madsen PM; Fletcher TH; Hecker WC ACS Division of Fuel Chemistry Preprints 2001, 318-320.
93. Tyler RJ; Wouterlood HJ; Mulcahy MFR Kinetics of the graphite-oxygen reaction near 1000°K. Carbon 1976, 14, 271-278.
94. Smith RN; Swinehart J; Lesnini D The oxidation of carbon by nitric oxide. Journal of Physical Chemistry 1959, 63, 544-547.
95. Smith RN; Swinehart J; Lesnini D The oxidation of carbon by nitrous oxide. Journal of Physical Chemistry 1959, 61, 81-86.
96. Aarna I; Suuberg EM A review of the kinetics of the nitric oxide carbon reaction. Fuel 1997, 76, 475-491.
161
97. Suzuki T; Kyotani T; Tomita A Study on the carbon nitric-oxide reaction in the presence of oxygen. Industrial Engineering Chemistry Research 1994, 33, 2840-2845.
98. Uchisawa JO; Obuchi A; Ogata A; Enomoto R; Kushimaya S Effect of feed gas composition on the rate of carbon oxidation with Pt/SiO2 and the oxidation mechanism. Applied Catalysis B 1999, 21, 9-17.
99. Jelles SJ; Krul RR; Makkee M; Moulijn JA The influence of NOx on the oxidation of metal activated diesel soot. Catalysis Today 1999, 53, 623-630.
100. Gray PG; Do DD Modelling the interaction of nitrogen dioxide with activated carbon. I. Adsorption dynamics at a single particle. Chemical Engineering Communications 1992, 117, 219-240.
101. Lur'e BA; Mikhno AV Interaction of NO2 with soot. Kinetics and Catalysis 1997, 38, 490-497.
102. Gray PG; Do DD Modelling the interaction of nitrogen dioxide with activated carbon. I. Adsorption dynamics at a single particle. Chemical Engineering Communications 1993, 118, 333-342.
103. Bueno-Lopez A; Garcia-Garcia A; Illan-Gomez MJ; Linares-Solano A; Salinas-Martinez de Lecea C Advances in Potassium Catalyzed NOx Reduction by Carbon Materials: An Overview. Industrial Engineering Chemistry Research 2007, 46, 3891-3903.
104. Akhter MS; Chughtai AR; Smith DM The structure of hexane soot - 1-Spectroscopic studies. Applied Spectroscopy 1985, 39, 143.
105. Al-Abadleh AH; Grassian VH Heterogeneous reaction of NO2 on hexane soot: A Knudsen cell and FT-IR study. Journal of Physical Chemistry 2000, 104, 11926.
106. Zawadski J; Wisniewski M; Skowronska K Heterogeneous reactions of NO2 and NO-O2 on the surface of carbons. Carbon 2003, 41, 235-246.
107. Shirahama N; Moon SH; Choi KH; Enjoji T; awano S; Korai Y; Mochida I; Tanoura M Mechanistic study on adsorption and reduction of NO2 over activated carbon fibers. Carbon 2002, 40, 2605-2611.
108. Arens F; Gutzwiller L; Baltensperger U; Gaggeler Hw; Ammann M Heterogeneous reaction of NO2 on diesel soot particles. Environmental Science and Technology 2001, 35, 2191-2199.
162
109. Tabor K; Gutzwiller L; Rossi MJ Heterogeneous chemical kinetics for NO2 on amorphous carbon at ambient temperature. Journal of Physical Chemistry 1994, 98, 6172-6186.
110. Kleffman J; Becker KH; Lackhoff M; Wiesen P Heterogeneous conversion fo NO2 on carbonaceous surfaces. Phys. Chem. Chem. Phys. 1999, 1, 5443.
111. Chambrion P; Kyotani T; Tomita A C-NO reaction in the presence of O2. 27th Symposium (International) on Combustion 1998, 3053.
112. Ishiguro T; Suzuki N; Fujitani Y; Morimoto H Microstructural changes of diesel soot during oxidation. Combustion and Flame 1991, 85, 1-6.
113. Vander Wal RL; Yezerets A; Currier NW; Kim DH; Wang CM HRTEM study of diesel soot collected from diesel particulate filters. Carbon 2007, 45, 70-77.
114. Choi MY; Lee KO; Yozgatligil A; Zhu J Effects of engine operating conditions on morphology, microstructure and fractal geometry of light-duty diesel engine particulates. Proceedings of the Combustion Institute 2005, 30, 2781-2789.
115. Boehman AL; Song J; Mahabubul A Impact of biodiesel blending on diesel soot and the regeneraton of particulate filters. Energy and Fuels 2005, 19, 1857-1864.
116. Song J; Zello V; Boehman AL Comparison of the impact of intake oxygen enrichment and fuel oxygenation on diesel combustion and emission. Energy and Fuels 2004, 18, 1282-1290.
117. Song J; Wong J; Boehman AL The role of fuel-borne catalyst in diesel particulate oxidation behaviour. Combustion and Flame 2006, 146, 73-84.
118. Song J; Alam M; Boehman AL Examination of the oxidation behaviour of biodiesel soot. Combustion and Flame 2006, 146, 589-604.
119. Su DS; Jentoft RE; Muller JO; Rothe D; Jacob E; Simpson CD; Tomovic Z; Mullen K; Messerer A; Poschl U; Niesssner R; Schlogl R Microstructure and oxidation behaviour of Euro IV diesel engine soot: a comparative study with synthetic model soot substances. Catalysis Today 2004, 90, 127-132.
120. Muller JO; Su DS; Jentoft RE; Krohnert J; Jentoft FC; Schlogl R Morphology-controlled reactivity of carbonaceous materials towards oxidation. Catalysis Today 2005, 102-103, 259-265.
163
121. Kanadas AW; Senel IG; Levendis Y; Sarofim AF Soot surface area evolution during air oxidation as evaluated by small angle X-ray scattering and CO2 adsorption. Carbon 2005, 43, 241-251.
122. Yang RT; Goethel PJ Mechanism of catalyzed graphite oxidation by monolayer channeling and monolayer edge recession. Journal of Catalysis 1989, 119, 201-214.
123. Dresselhaus G; Dresselhaus MS; Endo M; Matthews MJ; Pimenta MA Origin of dispersive effects of the Raman D band in carbon materials. Physical Review B 1999, 59 (10), 1-4.
124. Tuinstra F; Koenig JL Raman spectra of Graphite. Journal of Chemical Physics 1970, 53, 1126.
125. Compagnini G; Foti G; Puglisi O Raman spectra of virgin and damaged graphite edge planes. Carbon 1997, 35, 1793-1797.
126. Ferrari AC Raman Spectroscopy of graphene and graphite: Disorder, electron-phonon coupling, doping and nonadiabatic effects. Solid State Communications 2007, 143, 47-57.
127. Pimenta MA; Dresselhaus G; Dresselhaus MS; Cancado LG; Jorio A; Saito R Studying disorder in graphite-based systems by Raman spectroscopy. Physical Chemistry Chemical Physics 2007, 9, 1276-1291.
128. Sadezky A; Muckenhuber H; Grothe H; Niessner R; Poschl U Raman spectra of soot: spectral analysis and structural information. Carbon 2005, 43, 1731-1742.
129. Lorentzou S; Pagkoura C; Zygogianni A; Kastrinaki G; Konstandopoulos AG Catalytic Nano-structured Materials for Next Generation Diesel Particulate Filters. SAE 2008, 2008-01-0417.
130. Higgins KJ; Jung H; Kittelson DB; Roberts JT; Zachariah MR Size-selected nanoparticle chemistry: kinetics of soot oxidation. Journal of Physical Chemistry A 2002, 106, 96-103.
131. Masi S; Salatino P; Senneca O The influence of char surface oxidation on thermal annealing and loss of combustion reactivity. Proceedings of the Combustion Institute 2005, 30, 2223-2230.
132. Masi S; Salatino P; Senneca O The influence of heat treatment and weathering on the gasification reactivity of montana lignite. Symposia (International) on Combustion 1998; pp 2991-2999.
133. Senneca O; Russo P; Salatino P; Masi S The relevance of thermal annealing to the evolution of coal char gasification reactivity. Carbon 1997, 35, 141-151.
164
134. Lahaye J; Boehm P; Chambrion P; Ehrburger P Influence of cerium oxide on the formation and oxidation of soot. Combustion and Flame 1996, 104, 199-207.
135. Aboukais A; Courcot D; Pruvost C; Zhilinskaya EA Potential of supported copper and potassium oxide catalysts in the combustion of carbonaceous particles. Kinetics and Catalysis 2004, 45, 614-621.
136. Fino D; Russo N; Saracco G; Specchia V The role of suprafacial oxygen in some perovskites for the catalytic combustion of soot. Journal of Catalysis 2003, 217, 367-375.
137. Bueno-Lopez A; Krishna K; Makkee M; Moulijn JA Enhanced soot oxidation by lattice oxygen via La3+ - doped CeO2. Journal of Catalysis 2005, 230, 237-248.
138. Nanba T; Ohi JA; Obuchi A; Uchisawa JO; Wang S Improvement of Pt catalyst for soot oxidation using mixed oxide as a support. Applied Catalysis B 2003, 44, 207-215.
139. Fu CH; Tseng HH; Wey MY Carbon materials as catalyst supports for SO2 oxidation: catalytic activity of CuO-AC. Carbon 2003, 41, 139-149.
140. Fino D; Fino P; Saracco G; Specchia V Studies of kinetics and reaction mechanisms of La2-xKxCu1-xVyO4 layered perovskites for the combined removal of diesel particulate and NOx. Applied Catalysis B 2003, 43, 243-259.
141. Makkee M; Moulijn JA; Setiabudi A An optimal NOx assisted abatement of diesel soot in an advanced catalytic filter design. Applied Catalysis B 2003, 42, 35-45.
142. Nanba T; Obuchi A; Ohi J; Uchisawa JO; Wang S Catalytic performance of Pt/MOx loaded over SiC-DPF for soot oxidation. Applied Catalysis B 2003, 43, 117-129.
143. Ball IK; Cespades M; Daniell W; Harrison PG; Lukinskas P; Miro EE; Ulla MA Cobalt catalysts for the oxidation of diesel soot particulate. Chemical Engineering Journal 2003, 95, 47-55.
144. Fino D; Russo N; Saracco G; Specchia V Studies on the redox properties of chromite perovskite catalysts for soot combustion. Journal of Catalysis 2005, 223, 114-121.
145. Braun S; Leocadio ICL; Schmal M Diesel soot combustion on Mo/Al2O3 and V/Al2O3 catalyst: investigation of the active catalytic species. Journal of Catalysis 2004, 223, 114-121.
165
146. Badini C; Biamino S; Fino D; Fino P; Russo N Catalyzed traps for diesel soot abatement: Insitu processing and deposition of perovskite catalyst. Applied Catalysis B 2005, 61, 297-305.
147. Choi DK; Lee JH; Lee YW; Park JW; Yun JH Studies on the surface chemistry based on competitive adsorption of NOx-SO2 onto a KOH impregnated activated carbon in excess O2. Environmental Science and Technology 2002, 36, 4928-4935.
148. Ciambelli P; D'Amore M; Palma V; Vaccaro S Catalytic combustion of carbon particulate at high values of the carbon/catalyst mass ratio. Symposia (International) on Combustion 1996, 27, 1789-1796.
149. Cimino A; Gazzoli D; Valigi M XPS quantitative analysis and models of supported oxide catalysts. Journal of Electron Spectroscopy 1999, 104, 1-29.
150. McGinn H An PJ; Reichenbach HM Combinatorial synthesis and characterization of mixed metal oxide for soot combustion. Applied Catalysis B 2003, 44, 347-354.
151. Yuan S; Meriaudeau P; Perrichon V Catalytic combustion of diesel soot particles on copper catalyst supported on TIO2, Effect of potassium promoter on the activity. Applied Catalysis B 1994, 3, 319-333.
152. Ciambelli P; Corbo P; Parrella P; Scialo M; Vaccaro S Catalytic oxidation of soot from diesel exhaust gases I. Screening of metal oxide catalysts by TG-DTG-DTA analysis. Thermochimica Acta 1990, 162, 83-89.
153. Vonarb R; Hachimi A; Jean E; Bianchi D Catalytic Oxidation of a diesel soot formed in the presence of a cerium additive II Temperature Programmed experiments on the surface -oxygenated complexes and kinetic modeling. Energy and Fuels 2005, 29, 35-48.
154. Neeft JPA; Makkee M; Moulijn JA Catalytic oxidation of carbon black I. Activity of catalysts and classification of oxidation profiles. Fuel 1998, 77, 111-119.
155. Jimenez R; Garcia X; Cellier C; Ruiz P; Gordon AL Soot Combustion with K/MgO as catalyst II. Effect of K-precursor. Applied Catalysis A 2006, 314, 81.
156. Querini CA; Ulla MA; Requejo F; Soria J; Sedran UA; Miro EE Catalytic combustion of diesel soot particles. Activity and characterization of Co/MgO and Co, K/MgO catalysts. Applied Catalysis B 1998, 15, 5-19.
166
157. Neri G; Bonaccorsi L; Donato A; Milone C; Musolino MG; Visco AM Catalytic combustion of diesel soot over metal oxide catalysts. Applied Catalysis B 1997, 11, 217-231.
158. Ciambelli P; D'Amore M; Palma V; Vaccaro S Catalytic combustion of an amorphous carbon black. Combustion and Flame 1994, 99, 413-421.
159. McCabe RW; Sinkevitch RM A laboratory combustion study of diesel particulate containing metal additives. SAE 1986, 860011.
160. Neeft JPA; Makkee M; Moulijn JA Catalysts for the oxidation of soot from diesel exhaust gases I. An exploratory study. Applied Catalysis B 1996, 8 (57), 78.
161. Ciambelli P; Di Pietro A; Palma V; Vaccaro S Characterization of the catalytic oxidation of carbon black by TPD. Thermochimica Acta 1993, 227, 19-26.
162. Ciambelli P; Palma V; Vaccaro S Low temperature carbon particualte oxidation on a supported Cu/V/K catalyst. Catalysis Today 1993, 17, 71-78.
163. McKee DW Metal oxides as catalysts for the oxidation of graphite. Carbon 1970, 8, 623-635.
164. Ahlstrom AF; Odenbrand CUI Catalytic combustion of soot deposits from diesel engines. Applied Catalysis 1990, 60, 143-156.
165. Ahlstrom AF; Odenbrand CUI Combustion of soot deposits from diesel engines on mixed oxides of vanadium pentoxide and cupric oxide. Applied Catalysis 1990, 60, 157-172.
166. Badini C; Saracco G; Serra V Combustion of carbonaceous material by Cu-K-V based catalysts I. Role of copper and potassium vanadates. Applied Catalysis B 1997, 11, 307-328.
167. McKee DW Mechanisms of the alkali metal catalyzed gasification of carbon. Fuel 1983, 62, 170-175.
168. Bellaloui A; Varloud J; Meriaudeau P; Perrichon V; Lox E; Chevrier M; Gauthier C; Mathis F Low Temperature diesel soot combustion using copper based catalysts modified by niobium and potassium promoters. Catalysis Today 1996, 29, 412-415.
169. Shaugguan WF; Teroaka Y; Kagawa S Simultaneous removal of NOx and diesel soot particulates over ternary Al2O4 spinel -type oxides. Applied Catalysis B 1996, 8, 217-227.
170. McKee DW; Chatterji D The catalytic behaviour of alkali carbonates and oxides in graphite oxidation reactions. Carbon 1975, 13, 381-390.
167
171. McKee DW Catalysis of the graphite-water vapour reaction by alkaline earth salts. Carbon 1979, 17, 419-425.
172. Peralta MA; Milt VG; Cornaglia LM; Querini CA Stability of Ba, K/CeO2 catalyst during diesel soot combustion: Effect of temperature, water and sulfur dioxide. Journal of Catalysis 2006, 242, 118-130.
173. Bueno-Lopez A; Makkee M; Krishna K; Moulijn JA Active oxygen from CeO2 and its role in catalysed soot oxidation. Catalysis Letters 2005, 99, 203-205.
174. Setiabudi A; Chen J; Mul G; Makkee M; Moulijn JA CeO2 catalyzed soot oxidation The role of active oxygen to accelerate the oxidation conversion. Applied Catalysis B 2004, 51, 9-19.
175. Illan-Gomez MJ; Linares-Solano A; Salinas-Martinez de Lecea C NO reduction by Activated carbon. 6. Catalysts by transition metals. Energy and Fuels 1995, 9, 976-983.
176. Illan-Gomez MJ; Linares-Solano A; Radovic LR; Salinas-Martinez de Lecea C NO reduction by Activated carbon. 7. Some Mechanistic Aspects of Uncatalyzed and Catalyzed Reaction. Energy and Fuels 1995, 10, 158-168.
177. Nejar N; Illan-Gomez MJ Noble free potassium bimetallic catalysts supported on beta zeolite for the simultaneous removal of NOx and soot from simulated diesel exhaust. Catalysis Today 2007, 119, 262-266.
178. Xue E; Seshan K; Ross JRH Roles of supports, Pt loadings and Pt dispersion in the oxidation of NO to NO2 and of SO2 to SO3. Applied Catalysis B 1996, 11, 65-79.
179. Uy D; O'Neill; Weber WH UV Raman studies of adsorbed oxygen and NOx species on Pt/gamma-alumina catalysts. Applied Catalysis B 2002, 35, 219-225.
180. Liu S; Obuchi A; Uchisawa A; Nanba T; Kushimaya S An exploratory study of diesel soot oxidation with NO2 and O2 on supported metal oxide catalysts. Applied Catalysis 2002, 37, 309-319.
181. Pisarello ML; Milt VG; Peralta MA; Querini CA; Miro EE Simultaneous removal of soot and nitrogen oxides from diesel engine exhausts. Catalysis Today 2002, 75, 465-470.
182. Matsuoka K; Itoh Y; Chambrion P; Tomita A Reaction of NO with soot over Pt-loaded catalyst in the presence of oxygen. Applied Catalysis B 2000, 26, 89-99.
168
183. Mims CA; Pabst JK Alkali-Catalyzed carbon gasification kinetics: Unification of H2O, D2O and CO2 reactivities. Journal of Catalysis 1987, 107, 209-220.
184. Chen SG; Yang RT The active surface species in alkali-catalyzed carbon gasification: Phenolate (C-O-M) groups vs clusters (Particles). Journal of Catalysis 1993, 141, 102-113.
185. Mulla SS; Chen N; Cumaranatunge L; Blau GE; Zemlyanov DY; Delgass WN; Epling WS; Ribieiro FH Reaction of NO and O2 to NO2 on Pt: Kinetics and catalyst deactivation. Journal of Catalysis 2006, 241, 389-399.
186. Galdeano NF; Carrascull AL; Ponzi MI; Lick ID; Ponzi EN Catalytic combustion of particulate matter Catalysts of alkaline nitrates supported on hydrous zirconium. Thermochimica Acta 2004, 421, 117-121.
187. Wigmans T; Haringa H; Moulijn JA Nature, activity and stability of active sites during alkali metal carbonate-catalyzed gasification reactions of coal char. Fuel 1983, 62, 185-189.
188. Liu J; Zhao Z; Xu C; Duan A; Zhu L; Wang X The structures of VOx/MOx and alkali-VOx/MOx catalysts and their catalytic performances for soot combustion. Catalysis Today 2006, 118, 315-322.
189. Seipenbusch M; van Erven J; Schalow T; Weber AP; van Langeveld AD; Marijnissen JCM; Friedlander SK Catalytic soot oxidation in microscale experiments. Applied Catalysis B 2005, 55, 31-37.
190. Wu X; Radovic LR Catalytic oxidation of carbon/carbon composite materials in the presence of potassium and calcium acetates. Carbon 2005, 43, 333-344.
191. Lee YH; Lee GD; Park SS; Hong SS Catalytic removal of carbon particulates over MgFe2O4 catalysts. React. Kinet. Catal. Lett. 2005, 84, 311-317.
192. Wang S; Haynes BS Catalytic combustion of soot on metal oxides and their supported metal chlorides. Catalysis Communications 2003, 4, 591-596.
193. Carrascull AL; Lick ID; Ponzi EN; Ponzi MI Catalytic combustion of soot with a O2/NO mixture KNO3/ZrO2 catalysts. Catalysis Communications 2003, 4, 124-128.
194. Carabineiro SA; Bras Fernandes F; Ramos AM; Vital J; Silva IF Vanadium as a catalyst for NO, N2O and CO2 reaction with activated carbon. Catalysis Today 2000, 57, 305-312.
195. Neri G; Rizzo G; Galvagno S; Donato A; Musolino MG; Pietropaolo R Characterization and catalytic activity of unsupported FeV and K-FeV
169
catalysts for diesel soot combustion. React. Kinet. Catal. Lett. 2003, 78, 243-250.
196. Sosa RC; Masy D; Rouxhet PG Influence of surface properties of carbon black on the activity of adsorbed catalase. Carbon 1994, 32, 1369-1375.
197. Braun A; Huggins FE; Kelly KE; Mun BS; Ehrlich SN; Huffman GP Impact of ferrocene on the structure of diesel exhaust soot as probed with wide-angle X-ray scattering and C(1s) NEXAFS spectroscopy. Carbon 2006, 44, 2904-2911.
198. Kanadas AW; Levendis Y; Senel IG; Sarofim AF Soot surface area evolution during air oxidation as evaluated by small angle X-ray scattering and CO2 adsorption. Carbon 2005, 43, 241-251.
199. Saracco G; Badini C; Russo N; Specchia V Development of catalysts based on pyrovanadates for diesel soot combustion. Applied Catalysis B 1999, 21, 233-242.
200. Setiabudi A; Van Setten BAAL; Makkee M; Moulijn JA The influence of NOx on soot oxidation rate: molten salt versus platinum. Applied Catalysis B 2002, 35, 159-166.
201. Van Setten BAAL; van Dijk R; Jelles SJ; Makkee M; Moulijn JA The potential of supported molten salts in the removal of soot from diesel exhaust gases. Applied Catalysis B 1999, 21, 51-61.
202. Encinar JM; Gonzalez JF; Sabio E; Rodriguez JJ Catalyzed gasification of active carbon by oxygen:influence of catalyst type, temperature, oxygen partial pressure and particle size. J Chem. Techol. Biotechnol 2000, 75, 213-222.
203. Badini C; Mazza D; Ronchetti S; Saracco G Effect of chemical composition of isomorphous metavanadates on their catalytic activity toward carbon combustion. Materials Research Bulletin 1999, 34, 851-862.
204. Yang RT; Wong C Catalysis of carbon oxidation by transition metal carbides and oxides. Journal of Catalysis 1984, 85, 154-168.
205. Ambrogio M; Saracco G; Specchia V Combining filtration and catalytic combustion in particulate traps for diesel exhaust treatment. Chemical Engineering Science 2001, 56, 1613-1621.
206. Mims CA; Krajewski JJ Mechanisms of methane formation in potassium catalyzed carbon gasification. Journal of Catalysis 1986, 102, 140-150.
207. Mims CA; Pabst JK Role of surface salt complexes in alkali-catalysed carbon gasification. Fuel 1983, 62, 176-179.
170
208. Baker RTK; Thomas RB; Wells M Controlled atmosphere electron microscopy studies of graphite gasification - the catalytic influence of vanadium and vanadium pentoxide. Carbon 1975, 13, 141-145.
209. Baker RTK; France JA; Rouse L; Waite RJ Catalytic oxidation of graphite by platinum and palladium. Journal of Catalysis 1976, 41, 22-29.
210. Heintz EA; Parker WE Catalytic effect of major impurities on graphite oxidation. Carbon 1966, 4, 476-482.
211. McKee DW Rare earth oxides as carbon oxidation catalysts. Carbon 1985, 23, 707-713.
212. Rakszawski JF; Parker WE The effect of group IIIA- VIA elements and their oxides for graphite oxidation. Carbon 1964, 2, 53-63.
213. Yang RT; Duan RZ Kinetics and mechanisms of gas-carbon reactions: conformation of etch pits, hydrogen inhibition and anisotropy in reactivity. Carbon 1985, 23, 325-331.
214. Harris PS; Feates FS; Reuben BG Controlled atmosphere electron microscopy studies of graphite gasification - 4. Catalysis of the graphite-O, Reaction of silver. Carbon 1974, 12, 189-197.
215. McKee DW The copper catalyzed oxidation of graphite. Carbon 1970, 8, 131-139.
216. McKee DW The catalyzed gasification reactions of carbon. In Chemistry and physics of carbon, Walker PL, Ed.; Marcel Dekker: New York, 1981; pp 1-118.
217. Mims CA Catalytic gasification of carbon: Fundamentals and mechanism, Plenary Lecture. NATO Advanced Research Workshop on "Fundamental Issues in Control of Carbon Gasification reactivity 1990.
218. Olong NE; Stowe K; Maier WF A combinatorial approach for the discovery of low temperature soot oxidation catalysts. Applied Catalysis B 2007, 74, 19-25.
219. Wu X; Liu D; Li K; Li J; Weng D Role of CeO2-ZrO2 in diesel soot oxidation and thermal stability of potassium catalyst. Catalysis Communications 2007, 8, 1274-1278.
220. Negro A; Montanaro L On the effects induced by the accumulation of sodium, iron and cerium on diesel soot filters. SAE 1998, 980540.
171
221. Illan-Gomez MJ; Linares-Solano A; Radovic LR; Salinas-Martinez de Lecea C NO Reduction by Activated Carbons 2. Catalytic Effect of Potassium. Energy and Fuels 1995, 9, 97-108.
222. Allanson R; Blakeman PG; Cooper BJ; Hess H; Silcock PJ; Walker AP Optimizing the Low Temperature Performance and Regeneration Efficiency of the Continuously Regenerating Diesel Particulate Filter (Cr-Dpf) System. SAE 2002, 2002-01-0428.
223. Van Setten BAAL; Makkee M; Moulijn JA Science and Technology of Diesel Particulate Filters. Catalysis Reviews 2001, 43, 489-564.
224. Pfeifer M; Votsmeier M; Spurk PC; Kogel M; Lox E; Knoth JF The Second Generation of Catalyzed Diesel Particulate Filter Systems for Passenger Cars - Particulate Filters With Integrated Oxidation Catalyst Function. SAE 2005, 2005-01-1756.
225. Soeger N; Mussmann L; Sesselmann R; Leippe G; Gietzelt C; Bailey O; Hori M Impact of Aging and NOx/Soot Ratio on the Performance of a Catalyzed Particulate Filter for Heavy-Duty Diesel Applications. SAE 2005, 2005-01-0663.
226. Chatterjee S; McDonald C; Conway R; Windawi H; Vertin KD; LeTavec CA; Clark N; Gautam M Emission Reductions and Operational Experiences With Heavy-Duty Diesel Fleet Vehicles Retrofitted With Continuously Regenerated Diesel Particulate Filters in Southern California. SAE 2001, 2001-01-0512.
227. Vincent MW; Richards P; Novel-Cattin F; Marcelly B; Favre C Fuel Additive Performance Evaluation for Volume Production Application of a Diesel Particulate Filter. SAE 2001, 2001-01-1286.
228. Valentine JM; Peter-Hoblyn JD; Acres GK Emissions Reduction and Improved Fuel Economy Performance From a Bimetallic Platinum/Cerium Diesel Fuel Additive At Ultra-Low Dose Rates. SAE 2000, 2000-01-1934.
229. Levendis Y; Larsen CA Use of Ozone-Enriched Air for Diesel Particulate Trap Regeneration. SAE 1999, 1999-01-0114.
230. Yamamoto T; Okubo M; Kuroki T; Miyairi Y Nonthermal Plasma Regeneration of Diesel Particulate Filter. SAE 2003, 2003-01-1182.
231. Hoard JW Plasma-Catalysis for Diesel Exhaust Treatment: Current State of the Art. SAE 2001, 2001-01-0185.
232. Joshi A Development of an Actively Regenerating DPF System for Retrofit Applications. SAE 2006, 2006-01-3553.
172
233. Fayard J-C; Joubert E A New Active DPF System for "Stop & Go" Duty Cycle Vehicles: Durability and Improvements. SAE 2005, 2005-01-1754.
234. Michelin J; Figueras B; Bouly C; Maret D Optimized Diesel Particulate Filter System for Diesel Exhaust Aftertreatment. SAE 2000, 2000-01-0475.
235. Shirk R; Bloom RL; Kitahara Y; Shinzawa M Fiber Wound Electrically Regenerable Diesel Particulate Filter Cartridge for Small Diesel Engines. SAE 1995, 950153.
236. Kobashi K; Hayashi K; Aoki H; Kurazono K; Fujimoto M Regeneration Capability of Diesel Particulate Filter System Using Electric Heater. SAE 1993, 930365.
237. Zhi N; Guanglong Z; Yong L; Junmin L; Xiyan G; Lunhui L; Jiahua C Analysis of Characteristic of Microwave Regeneration for Diesel Particulate Filter. SAE 1995, 952058.
238. Christensen R; Hansen MB; Schramm J; Binderup M-L; Jorgensen V Mutagenic activity of soluble organic fraction of exhaust gas particulate from direct injection diesel engine. SAE 1996, 961977.
239. Muhle H Toxic and carcinogenic effects of fine particles- observations and hypotheses. International ETH workshop on nanoparticle measurement 1999.
240. US EPA . Review of EPA's Health Assessment Document for Diesel Exhaust. EPA 600/8-90/057E. 2000.
241. California Air Resources Board . Staff Report. 4-22-1998.
242. California Air Resources Board . Resolution 98-35. 8-27-1998.
243. Johnson TV Diesel Emission Control in Review. SAE Paper 2006, 2006-01-0030.
244. US EPA . EPA 40 CFR Parts 69, 80,86, 'Control of Air Pollution from new motor vehicles: Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements: Final Rule', Federal Register Vol 66, No. 12, Thursday January 18, 2001. 2001.
245. US EPA . EPA420-F-00-057, 'Fact Sheet (EPA 420-F-00-057): "Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements" - December 2000', United States Environmental Protection Agency, Air and Radiation, December 2000. 2000.
246. US EPA . EPA420-R-01-052, 'Nonroad Diesel Emission Standards-Staff Technical Paper', United States Environmental Protection Agency, Air and Radiation, October 2001. 2001.
173
247. Locker RJ; Sawyer CB; Floerchinger P; Menon S; Craig A Diesel Particulate Filter Operational Characterization. SAE 2004, 2004-01-0958.
248. Kuki T; Miyairi Y; Kasai Y; Miyazaki M; Miwa S Study on Reliability of Wall-Flow-Type Diesel Particulate Filter. SAE 2004, 2004-01-0959.
249. Ohno K; Taoka N; Furuta T; Kudo A; Komori T Characterization of High Porosity SiC-DPF. SAE 2002, 2002-01-0325.
250. Presti M; Pace L; Carelli G; Spurk P; Kargel M Innovative Metal Supported Catalysts for EURO V Diesel Engines. SAE 2005, 2005-02-4003.
251. Farafontov P; Muter JP; Williams S Optimization of Partial Filter Technology for Diesel Engines. SAE 2007, 2007-01-4025.
252. Maricq MM Chemical characterization of particulate emissions from diesel engines: A review. Aerosol Science 2007, 38, 1079-1118.
253. Mayer A; Nothiger P; Zbinden R; Evequoz R Particulate Trap Selection for Retrofitting Vehicle Fleets Based on Representative Exhaust Temperature Profiles. SAE 2001, 2001-01-0187.
254. California Air Resources Board . CARB Engelhard Verification Letter Dated October 3, 2001. 2001.
255. California Air Resources Board . California Code of Regulation, Title 13, Chapter 14 Verification Procedure, Warranty and In-Use Compliance Requirements for In-Use Strategies to Control Emissions from Diesel Engines Section 2706 (a)(1), pg 25. 2002.
256. Pentetrante BM; Brusasco RM; Merritt BT; Vogtlin GE Feasibility of Plasma Aftertreatment for Simultaneous Control of NOx and Particulates. SAE 1999, 1999-01-3637.
257. Lepperhoff G; Scharr D; Pischinger S; Neff W; Trompeter F-J; Pochner K Exhaust Emission Reduction of Combustion Engines By Barrier Discharge - A New Reactor/Generator System. SAE 1999, 1999-01-3638.
258. Breuer K Deutz Particulate Filter Systems. Canadian Mining Diesel Conference (CANMET) 1998.
259. Houben H; Miebach R; Sauerteig JE The Optimized Deutz Service Diesel Particulate Filter System DPFS II. SAE 1994, 942264.
260. Kojetin P; Janezich F; Sura L; Tuma D Production Experience of a Ceramic Wall Flow Electric Regeneration Diesel Particulate Trap. SAE 1993, 930129.
174
261. Ma J; Fang M; Li P; Zhu B; Lu X; Lau NT Microwave-assisted catalytic combustion of diesel soot. Applied Catalysis A 1997, 159, 211-228.
262. Wang J; Hickman D; Corrigan E; Chatterjee S; Kimura K; Fang H; Lynskey M Real World Study of Diesel Particulate Filter Ash Accumulation in Heavy-Duty Diesel Trucks. SAE 2006, 2006-01-3257.
263. Bunting BG; More KL; Toops TJ; Lewis, Sr. S. Phosphorous Poisoning and Phosphorous Exhaust Chemistry with Diesel Oxidation Catalysts. SAE 2005, 2005-01-1758.
264. Hurt R; Sun J-K; Lunden M A kinetic model of carbon burnout in pulverized coal combustion. Combustion and Flame 1998, 113, 181-197.
265. Sui L; Yu LY; Zhang YH Catalytic combustion of diesel soot on Co-Sr-K catalysts. Energy and Fuels 2007, 21, 1420-1424.
266. Zhang Y; Zou X The catalytic activities and thermal stabilities of Li/Na/K carbonates for diesel soot oxidation. Catalysis Communications 2007, 8, 760-764.
267. Tikhomirov K; Krocher O; Wokaun A Influence of potassium doping on the activity and the sulfur poisoning resistance of soot oxidation catalysts. Catalysis Letters 2006, 109, 49-53.
268. Messerer A; Niessner R; Poschl U Comprehensive kinetic characterization of the oxidation and gasification of model and real diesel soot by nitrogen oxides and oxygen under engine exhaust conditions: Measurement, Langmuir-Hinshelwood, and Arrhenius parameters. Carbon 2006, 44, 307-324.
269. Ehrburger P; Brilhac J-F; Drouillot Y; Logie V; Gilot P Reactivity of Soot with Nitrogen Oxides. SAE 2002, 2002-01-1683.
270. Darcy P; Da Costa PD; Mellottee H; Trichard JM; Mariadassou GD Kinetics of catalyzed and non-catalyzed oxidation of soot from a diesel engine. Catalysis Today 2007, 119, 252-256.
271. Chu X; Schmidt LD Intrinsic rate of NOx-Carbon reactions. Industrial Engineering Chemistry Research 1993, 32 (7), 1359-1366.
272. Choi K-Y; Cant NW; Trimm DL Gasification of carbonaceous particulates. J Chem. Techol. Biotechnol 1998, 71, 57-60.
273. Liu S; Obuchi A; Uchisawa JO; Nanba T; Kushimaya S Synergistic catalysis of carbon black oxidation by Pt with MoO3 or V2O5. Applied Catalysis B 2001, 30, 259-265.
175
274. Teroaka Y; Nakano K; Shaugguan WF; Kagawa S Simultaneous catalytic removal of nitrogen oxides and diesel soot particulate over perovskite- related oxides. Catalysis Today 1996, 27, 107-113.
275. Neeft JPA; Makkee M; Moulijn JA Metal oxides as catalysts for the oxidation of soot. Chemical Engineering Journal 1996, 64, 295-302.
276. Badini C; Saracco G; Serra V; Specchia V Suitability of some promising soot combustion catalysts for application in diesel exhaust treatment. Applied Catalysis B 1998, 18, 137-150.
277. Matyshak VA; Sadykov VA; Kuznetsova TG; Ukharskii AA; Khomenko TI; Bykhovskii MY; Sil'chenkova ON; Korchak VN Role of Nitrogen Dioxide in the Oxidation of Diesel Soot on Promoted Mixed Catalysts with Fluorite and Perovskite Structures. Kinetics and Catalysis 2006, 47, 400-411.
278. Du Z; Sarofim AF; Longwell JP; Mims CA Kinetic measurement and modeling of carbon oxidation. Energy and Fuels 1991, 5, 214-221.
279. Haynes BS A turnover model for carbon reactivity I. Development. Combustion and Flame 2001, 126, 1421-1432.
280. Ciambelli P; Palma V; Russo P; Vaccaro S The role of NO in the regeneration of catalytic ceramic filters for soot removal from exhaust gases. Catalysis Today 2000, 60, 43-49.
281. Krishna K; Bueno-Lopez A; Makkee M; Moulijn JA Potential rare-earth modified CeO2 catalysts for soot oxidation. Topics in Catalysis 2007, 42-43, 221-228.
282. Tschamber V; Jeguirim M; Villani K; Martens J; Ehrburger P Comparison of the activity of Ru and Pt catalysts for the oxidation of carbon by NO2. Applied Catalysis B 2007, 72, 299-303.
283. Querini CA; Fung SC Temperature-programmed oxidation technique: kinetics of coke-O2 reaction on supported metal catalysts. Applied Catalysis A 1994, 117, 53-74.
284. Redhead PA Thermal Desorption of Gases. Vacuum 1962, 12, 203-211.
285. Collura S; Chaoui N; Azambre B; Finqueneisel G; Heintz O; Krzton A; Koch A; Weber JV Influence of the soluble organic fraction on the thermal behaviour, texture and surface chemistry of diesel exhaust soot. Carbon 2005, 43, 605-613.
286. Glasson WA; Tuesday CS The Atmospheric Oxidation of Nitric Oxide. J. Am. Chem. Soc. 1963, 85, 2901-2904.
176
287. Tsukahara H; Ishida T; Mayumi M Gas-Phase Oxidation of Nitric Oxide: Chemical Kinetics and Rate Constant. Nitric Oxide: Biology and Chemistry 1999, 3, 191-198.
288. Jeguirim M; Tschamber V; Brilhac JF; Ehrburger P Oxidation mechanism of carbon black by NO2: Effect of water vapour. Fuel 2005, 84, 1949-1956.
289. Yezerets A; Currier NW; Eadler HA; Popuri S; Suresh A Quantitative Flow-Reactor Study of Diesel Soot Oxidation Process. SAE 2002, 2002-01-1684.
290. Nakatani K; Hirota S; Takeshima S; Itoh K; Tanaka T Simultaneous PM and NOx Reduction System for Diesel Engines. SAE 2002, 2002-01-0957.
291. Jelles SJ; Van Setten BAAL; Makkee M; Moulijn JA Molten salts as promising catalysts for oxidation of diesel soot: importance of experimental conditions in testing procedures. Applied Catalysis B 1999, 21, 35-49.
292. Szymanski GS; Karpinski Z; Biniak S; Swiatkowski A 'The effect of the gradual thermal decompostion of surface oxygen species on the chemical and catalytic properties of oxidized activated carbon. Carbon 2002, 40, 2627-2639.
293. Donnet JB The chemical reactivity of carbons. Carbon 1963, 6, 161-176.
294. Hearn MJ; Briggs D; Yoon SC; Ratner BD SIMS and XPS Studies of polyurethane surfaces 2. Polyurethanes with fluorinated chain extenders. Surface Interface Analysis 1987, 10, 384.
295. Briggs D; Vickerman JC ToF-SIMS: Surface Analysis by Mass Spectroscopy; IM Publications and SurfaceSpectra Limited: 2001.
296. White JM Measuring surface reaction rates using SIMS and TPD: An overview. Applied Surface Science 1986, 26, 392-407.
297. Kirchner U; Vogt R; Natzeck C; Goschnick J Single particle MS, SNMS, SIMS, XPS, and FTIR spectroscopic analysis of soot particles during the AIDA campaign. Aerosol Science 2003, 34, 1323-1346.
298. Gong B; Pigram PJ; Lamb RN Identification of inorganic nitrogen in an Australian bituminous coal using X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (TOFSIMS). International Journal of Coal Geology 1997, 34, 53-68.
299. Berman ESF; Kulp KS; Knize MG; Wu L; Nelson EJ; Nelson DO; Wu KJ Distinguishing monosaccharide stereo- and structural isomers with TOF-SIMS and multivariate statistical analysis. Analytical Chemistry 2006, 78, 6497-6503.
177
300. Peled E; Golodnitsky UA; Yufit V Effect of carbon substrate on SEI composition and morphology. Electrochemica Acta 2004, 50, 391-395.
301. Mahoney CM; Gillen G; Fahey AJ Characterization of gunpowder samples using time-of-flight secondary ion mass spectrometry (TOF-SIMS). Forensic Science International 2006, 158, 39-51.
302. Solumko V; Delcorte A; Garrison BJ; Bertrand P Sputtering of a polycyclic hydrocarbon molecule: TOF-SIMS experiments and molecular dynamic simulations. Applied Surface Science 2004, 231-232, 48-53.
303. Vickerman JC ToF-SIMS- An Overview. In ToF-SIMS: Surface Analysis by Mass Spectroscopy, Vickerman JC, Briggs D, Eds.; M Publications and SurfaceSpectra Limited: 2001.
304. Kapteijn F; Moulijn JA; Matzner S; Boehm HP The development of nitrogen functionality in model chars during gasification in CO2 and O2. Carbon 1999, 37, 1143-1150.
305. Wagner MS; Graham DJ; Ratner BD; Castner DG Maximizing information obtained from secondary ion mass spectra of organic thin films using multivariate analysis. Surface Science 2004, 78, 570.
306. Wagner MS; Castner DG Characterization of Adsorbed Protein Films by Time-of-Flight Secondary Ion Mass Spectroscopy with Principal Component Analysis. Langmuir 2001, 17, 4649.
307. Suzuki N; Gamble L; Tamerler C; Sarikaya M; Castner DG; Ohuchi FS Adsorption of genetically engineered proteins studied by TOFSIMS. Part A: data acquisition and principal component analysis (PCA). Surface Interface Analysis 2007, 39, 419-426.
308. Yang L; Lua Y-Y; Tan M; Scherman OA; Grubbs RH; Harb JN; Davis RC; Linford MR Chemistry of Olefin-Terminated Homogeneous and Mixed Monolayers on Scribed Silicon. Chem. Mater. 2007, 19, 1671-1678.
309. Cheng F; Gamble LJ; Grainger DW; Castner DG X-ray photoelectron Spectroscopy, Time of Flight Secondary Ion Mass Spectrometry, and Principal Component Analysis of the Hydrolysis, Regeneration, and Reactivity of N- Hydroxysuccinimide- Containing Organic Films. Analytical Chemistry 2007, 79, 8781-8788.
310. van den Berg RA; Hoefsloot HCJ; Westerhuis JA; Smilde AK; van der Werf MJ Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 2006, 7, 142.
178
311. Graham DJ; Wagner MS; Castner DG Information from complexity: Challenges of TOF-SIMS data interpretation. Applied Surface Science 2006, 252, 6860.
312. Wagner MS; Graham DJ; Castner DG Simplifying the interpretation of TOF-SIMS spectra and images using careful application of multivariate analysis. Applied Surface Science 2006, 252, 6575.
313. Wise BM; Gallagher NB; Bro R; Shaver JM PLS_Toolbox 3.0 for use with MATLABTM; Eigenvector Research, Inc: 2002.
314. Garrison BJ; Kodali PBS; Srivastava D Modeling of surface processes as exemplified by hydrocarbon reactions. Chemical Reviews 1996, 96, 1327-1341.
315. Garrison BJ Molecular Dynamics Simulations, the Theoretical Partner to Static SIMS. In ToF-SIMS: Surface Analysis by Mass Spectroscopy, Vickerman JC, Briggs D, Eds.; IM Publications and SurfaceSpectra Limited: 2001.
316. Azambre B; Collura S; Trichard JM; Weber JV Nature and thermal stability of adsorbed intermediates during the reaction of diesel soot with nitrogen dioxide. Applied Surface Science 2006, 253, 2296-2303.
317. McArthur SL; Wagner MS; Hartley PG; McLean KM; Griesser HJ; Castner DG Characterization of sequentially grafted polysaccharide coating using time- of-flight secondary ion mass spectroscopy (ToFSIMS) and principal component analysis (PCA). Surface Interface Analysis 2002, 33, 924-931.
318. Muir BW; McArthur SL; Thissen H; Simon GP; Griesser HJ; Castner DG Effects of oxygen plasma treatment on the surface of bisphenol A polycarbonate: a study using SIMS, principal component analysis, elliposometery, XPS and APM nanoindentation. Surface Interface Analysis 2006, 38, 1186-1197.
319. Licciardello A; Auditore A; Samperi F; Puglisi C Surface evolution of polycarbonate/polyethylene terphthalate blends induced by thermal treatments. Applied Surface Science 2003, 203-204, 556-560.
320. Escribano R; Sloan JJ; Siddique N; Sze N; Dudev T Raman spectroscopy of carbon-containing particles, Vibrational Spectroscopy. Vibrational Spectroscopy 2001, 26, 179-186.
321. Ferrari AC; Robertson J Resonant Raman spectroscopy of disordered, amorphous, and diamondlike carbon. Physical Review B 2001, 64, 075414.
322. Ilie A; Durkan C; Milne WI; Welland ME Surface enhanced Raman spectroscopy as a probe for local modification of carbon films. Physical Review B 2002, 66, 045412.
179
323. Li XK; Liu L; Li Zh H; Wu D; Shen Sh.D The characterization of ultrafine carbon powders by SAXS and Raman spectra. Carbon 2000, 39, 623.
324. Ray KG; McCreery RL Characterization of the surface carbonyl and hydroxyl coverage on glassy carbon electrodes using Raman spectroscopy. Journal of Electroanalytical Chemistry 1999, 469, 150.
325. Shimodaira N; Masui A Raman spectroscopic investigations of activated carbon materials. Journal of Applied Physics 2002, 92, 902.
326. Sze SK; Siddique N; Sloan JJ; Escribano R Raman spectroscopic characterization of carbonaceous aerosols. Atmospheric Environment 2001, 35, 561-568.
327. Gouadec G; Colomban P Raman Spectroscopy of nanomaterials: How spectra relate to disorder, particle size and mechanical properties. Progress in Crystal Growth and Characterization of Materials 2007, 57, 1-56.
328. Vehring R; Schweiger G Dispersive Raman Spectroscopy on Soot Particles. Journal of Aerosol Science 1998, 29, S1251.
329. Ivleva NP; Messerer A; Yang X; Niessner R; Poschl P Raman Microspectroscopic analysis of changes in the chemical structure and reactivity of soot in a diesel exhaust aftertreatment model system. Environmental Science and Technology 2007, 41, 3702-3707.
330. Wang Y; Alsmeyer DC; McCreery RL Raman spectroscopy of carbon materials: structural basis of observed spectra. Chemistry of Materials 1990, 2, 557-563.
331. Ferrari AC; Roberson J Interpretation of Raman spectra of disordered and amorphous carbon. Physical Review B 2000, 61, 14095.
332. Dresselhaus MS; Dresselhaus G Carbon. In Light Scattering in Solids, Guntherodt G, Ed.; Springer: Berlin, 1982; pp 3-58.
333. Fityk curve fitting free software V5.0, 2005, http://www.unipress.waw.pl/fityk, last accessed July 10, 2008
334. Amin A; Philip CA; Girgis BS Influence of reacting atmosphere on isothermal decomposition of ammonium metavanadate. Collection of Czechoslovak Chemical Communications 1994, 59, 1086-1095.
180
8 Appendix Information The appendix contains the following:
8.1) Appendix A contains the preparation method of the catalyst impregnated carbons
and screening experiments using image analysis.
8.2) Appendix B contains scanning electron microscopy images of the sucrose char and
NIST soot.
8.3) Appendix C contains exploratory experiments using Raman spectroscopy
8.4) Appendix D contains principal component analysis plots of negative and positive
ions for NIST, CAT, SC_AIR and SC_NOX samples.
8.5) Appendix E contains the certification data of the NIST soot and the specification
sheet of the sucrose precursor used to make the sucrose char.
181
8.1 Appendix A: Soot impregnation technique and procedure The following describes the preparation of carbons impregnated with catalyst used in the
reactivity studies. A variety of element precursors and wetting agents were used to disperse
the catalytic materials over the carbon surface. In addition, an experiment is described that
was used to quickly evaluate many catalysts at once under the same operation conditions.
Catalyst Preparation
NIST SRM 2975 (NIST) soot samples used for oxidation trials were wetted using a 90%-
ethanol--10%-water solution with dissolved catalyst precursors, with the exception of some
trials. Prior to impregnation with catalyst, it was determined that NIST soot samples favorably
absorbed solutions up to 2.1 times their own mass. Accordingly, solutions of KOH, FeNO2,
NH4VO3 and CeNO3 catalyst solutions were produced such that the impregnation solutions
would contain ratios of catalyst to carbon atoms of 1:50 up to 1:1000.
Initially each catalyst was dissolved in water to a predetermined concentration. This solution
was subsequently mixed with the correct amount of ethanol to produce solutions that could be
used to impregnate carbon to the predetermined catalyst-to-carbon ratio. The solution was
stored in a polypropylene bottle for later application to impregnate NIST soot samples.
Carbon samples were wetted using a micropipette filled with the prepared catalyst solutions.
Once the soot samples were wetted, they were dried in an oven for two hours at 65°C, ramped
at 1°C/min from room temperature until the final temperature was reached.
Table 8-1 below summarizes the samples that were prepared. It describes the solution that was
made and how much solution was added to each soot sample.
182
Table 8-1: Catalyst impregnated carbon samples prepared for image analysis experiments
Sample Code Ion Ratio Solution Preparation μL Solution added to mg Amount Soot
T1 Na+ 1:50 26.0g NaOH pellets added to water to make 100ml solution; 5.55ml of resulting solution mixed with 50ml Ethanol
90.6μL to 34.9mg
T2 K+ 1:50 34.47g KOH pellets added to 100 ml water; 5.55ml of resulting solution mixed with 50ml Ethanol
106μL to 40.8mg
T3 V+ 1:125 3.75g of NH4VO3 added to 100mL of water in presence of 5.0mL 18.0M HCl
50.8μL applied three times to 24.2mg
T4 Fe3+ 1:150 4.7938g Fe(CO3)3 added to 100 ml water; 5.55ml of resulting solution mixed with 50ml Ethanol
8.34μL to 32.1mg
T5 Ce3+ 1:150 Added 221μL of Ce(NO3)3 solution to 779μL of water and mixed with 9.0mL of Ethanol
D2 K+ 1:230 1.8269g K2CO3 added to 20 ml water; 1 ml of resulting solution mixed with 9ml Ethanol
154.8μL to 61.9mg
Eth. -- -- Ethanol solution; no catalyst -- Diol. -- -- 1,4-Butandiol used in a mixture with
ethanol to wet soot --
N4 Std. -- -- Standard Soot. --
Image Analysis Experiment
The initial experiment (Experiment 1) was a broad test to determine oxidation temperatures of
the different soot-catalysts samples. A bench test was completed where less than 1mg of soot
from several different dried catalyst samples was taken and spread on a quartz plate over an
area less than one square centimeter. The procedure resulted in a relatively small area on the
quartz plate that was covered with a thin film of the soot layer. This was repeated several
times until several different soot-catalyst samples were on the plate.
Photographs of the quartz plate were taken using the following procedure. The sample loaded
quartz plate was placed on a white sheet of paper that was marked to locate the quartz plate for
later images. An enclosure was placed over the quartz plate and located based on markings on
the white background paper. The enclosure consists of two holes on the side of the enclosure
and a hole on top to locate the camera. The two holes on the side of the enclosure were used to
183
provide light from two light sources. The above equipment was used to minimize the light
contrast and allow the photos to be taken in a similar manner.
A photograph was taken of the quartz plate before being exposed to temperature in the oven.
The quartz plate was placed in the oven and heated to the required temperature and held for a
constant time and then cooled. The quartz plate was transferred to the photo equipment and a
photo taken. The procedure was repeated until all temperatures were evaluated.
Figure 8.1.1: Sample location for Image Experiment 1
The results of the first screening experiment (Experiment 1) to 425ºC were quite significant
(sample locations in Figure 8.1.1; images after temperature exposure Figure 8.1.3). Soot
samples that contained sodium, potassium and vanadium were each oxidized over 90% in the
regions of interest (see circled areas in Figure 8.1.1). In contrast, the control samples of soot
without metal catalyst (i.e. N4 Standard and N4 + Ethanol) showed no visible sign of
oxidation. Thus, observed oxidation was a result of some catalytic activity. The remaining
samples, including the [1:150] Ce+ showed some signs of oxidation, but it was not significant
when compared to sodium, potassium or vanadium, less than 10% (approximately) compared
to the other catalysts studied.
T1 T2 T3
T4 T5 N4
D2 Eth Diol
Quartz plate
Soot-Catalyst Samples
184
In the second ramp, the temperature was raised up to 450ºC. Sodium, potassium and vanadium
showed continued signs of oxidation. The N4 Std. soot, which did not oxidize, was thus used
as a benchmark for comparison between soot that had oxidized and had not oxidized in the
previous run. Iron impregnated soot showed some further signs of oxidation while the cerium
impregnated soot began to show signs of initiation of oxidation. However it is difficult to say
conclusively that both these samples did in fact oxidize. The grounds for this reasoning are
that in the 425ºC and 450ºC ramps, there was a noticeable contrast between the focus and
darkness of the overall picture, especially between the N4 Std. samples. Since it is probable
that the N4 Std. soot did oxidize between 425ºC and 450ºC, then the contrast between these
two samples is likely a result of lighting and focus. On similar grounds, the difference between
the iron and the cerium, samples may be attributed to lighting and focus.
The table below summarizes the results of the analysis of the images that were taken of the
quartz plates. The results were used to select the catalysts for the reactor oxidation
experiments found in Chapter 4.
Table 8-2: Estimate of oxidation for Experiment 1 Image Analysis (Peak Temperature 425 ºC)
Sample Code Ion Ratio Rough Estimate of Oxidation
T1 Na+ 1:50 >90% T2 K+ 1:50 >90% T3 V+ 1:125 >90% T4 Fe3+ 1:150 <10% T5 Ce3+ 1:150 <10% D2 <5%
Eth. -- -- None Diol. -- -- None
N4 Std. -- -- None A second image reactivity experiment (Experiment 2) was performed using a similar procedure
as Experiment 1. For this experiment, only sodium, potassium, and vanadium were used, since
it was these three samples that showed the greatest sign of oxidation during the first
experiment.
185
The first step was to 350ºC, but unlike the previous experiment it was held at that temperature
for 30 min and not one hour (Table 8-3). There was evidence to show that all three samples
oxidized. Of the three samples, the potassium - impregnated sample showed the greatest
oxidation. The sodium and vanadium also showed signs that oxidation had taken place (See
Figure 8.1.2 below for photo images of Experiment 2).
Table 8-3: Estimate of oxidation for Experiment 2 Image Analysis (Peak Temperature 350 ºC)
Sample Code Ion Ratio Rough Estimate of Oxidation
T1 Na+ 1:50 >40% T2 K+ 1:50 >70% T3 V+ 1:125 >20%
N4 Std. -- -- None
The experiment was repeated with a peak temperature of 325ºC. Oxidation of all three soot
samples had significantly decreased. Despite this, the soot sample with the potassium catalyst
continued to show signs of oxidation. As the images show, potassium was the most oxidized
sample. Sodium and vanadium impregnated soot samples also showed some signs of
oxidation, though not to the same extent as potassium samples.
186
Figure 8.1.2: Photo Images of Experiment 2
At Room Temperature At 350oC
At 325oC
T1 (Na) T2 (K) T3 (V)
187
Figure 8.1.3: Photo Images of Experiment 1
At Room Temperature After First Ramp at 425oC
187
188
After Second Ramp at 450oC
188
189
Conclusions and Analysis
Experiments showed that of the several different catalysts used to oxidize soot, potassium
acted as the best catalyst. It appears that potassium loadings at concentrations higher
than (Catalyst/Carbon mole ratio) 1:50 will likely result in lower oxidation temperatures
(from comparison of samples T2 and D2). It may also be worthwhile to determine
whether OH- had played any role in oxidation of the carbon. Although experiments run
to compare, methanol, ethanol and butanediol, showed no influence of OH- groups, none
of these solutions had free OH-. Both the sodium and potassium solutions were prepared
using hydroxide solutions that contained a free OH- ion.
190
8.2 Appendix B: SEM photos Scanning electron microscopy photos show the morphology of the carbon tested in this study:
1) Sucrose char has a smooth surface that has low surface area as confirmed by BET (12m2/g) (Figure 8.2.1).
2) CAT diesel soot consists of small particles of about 1 um diameter particles
suggest high surface area (Figure 8.2.2)
3) NIST diesel soot consists of larger particles than the CAT diesel soot with particles ranging in size from 2um to 25 um (Figure 8.2.3)
Figure 8.2.1: Sucrose char
191
Figure 8.2.2: CAT diesel soot
192
Figure 8.2.3: NIST diesel soot
193
8.3 Appendix C: Raman experimental procedure and results
Raman spectroscopy was used to determine qualitatively the presence of edge sites. A
description of the technique, experimental procedure and results are found here. The Raman
experiments were abandoned early in this thesis due to the observation of laser exposure on
this instrument changing the D/G peak ratio. It was concluded that local heating was being
caused by the laser exposure and light adsorption properties of the black carbon. An
unsuccessful attempt was made to find a spinning sample stage to minimize these effects.
Described below are the investigations performed before abandoning this experiment.
8.3.1.1 Objective Use Raman Spectroscopy to identify edge groups on carbon structures. It is expected that the
results will show a relationship between reactive edge carbons to less reactive basal plane
carbon atoms.
8.3.1.2 Background
One of the key parameters of this study is obtaining the number of available edge sites on the
carbon. Understanding the relationship of active edge sites on the carbon structure to bulk
carbons is key to determining the maximum edge site reactivity. A technique that is popular in
investigating carbon structures and reported to give information on carbon disorder is Raman
spectroscopy 124,125,320-326. A description of the Raman spectroscopy technique can be found in
a recent review on the applications of Raman 327. The application of Raman to graphite and
amorphous carbon is found in review articles by Ferrari 126 and Pimenta et al. 127. The structure
of diesel soot has also been examined 128,328, and more recently its reactivity 329.
The pioneering work of Tuinstra and Koenig 124 showed that variations in Raman spectra were
observed in different carbon materials. In this initial work, they observed a shift of the
intensities between the 1350 cm-1 and 1600 cm-1 wave number peaks. These wave numbers
have been assigned as the G peak and the D peak respectively. These peaks are interpreted to
194
indicate the amount of graphitic characteristics (G peak ) or bulk carbon and the D peak as the
disordered carbon peak 123,125,330,331. In carbons, the disordered peak has been suggested to
indicate the presence of edge carbons 125. G peak at 1585 cm-1 is the fundamental mode of the
graphite crystal. The D’ peak at 1620cm-1 is suggested to be sensitive to the composition of
the material in contact with the surface layers of the graphite (modifies their electronic
environment) 330,332. The 1360 cm-1 (D band – ‘disorder’ band) indicates edge vibrations.
Compagnini et al 125 provide evidence of the correlation of this feature with the graphite edge.
The ratio of intensity of D/G peak gives an indication of the amount of disorder in the carbon
structure. A direct relationship between the amounts of carbon edge sites to the intensity of the
D peak has not been reported. Although Compagnini looked at creating edge sites by using an
Ar ion gun, they found that the intensity ratio of D/G increases with ion influence 125. A survey
of the literature did not reveal a quantitative relationship between the peak areas or peak ratios
to the number of actual edge carbons.
8.3.1.3 Analytical Equipment
University of Toronto’s Institute of Optical Science, laser spectroscopy department in-house
designed Micro-Raman spectrometer using a con-focal microscope for imaging and beam
focusing with an argon laser operating at 514 nm was used for analysis. Laser power was
adjustable; two settings, 200mA and 150mA settings were used. The lower setting was used
because it caused less destruction of the carbon. The spectrometer was equipped with filters to
help diffuse the laser power and with variable pinhole and slit widths to help improve data
collection. A video camera is installed to help focus the beam.
195
8.3.1.4 Experimental
1) Examine sucrose chars and NIST diesel particulate matter using Raman Microscopy
2) Adjust power, filters, pinhole and slit size to optimize data collection and prevent
damage to the sample.
3) Select appropriate magnification lenses and filters.
4) Place sample on slide and place under microscope.
5) Adjust stage as close as possible to microscope.
6) Switch lens system to camera. Turn on camera and light.
7) Focus area of interest by backing the stage away from the lense (coarse adjustment)
8) Switch lens system to detector. Turn off camera and light.
9) Activate the scan mode and adjust to maximum intensity using fine adjustment knob
10) Select appropriate number of scans and scan delay time
11) Run scan accumulation mode.
12) Switch lenses system to camera. Turn on camera and light.
13) Check for damage on the material. Material damage is observed by discoloration of
material.
8.3.1.5 Instrument setup
A series of initial setup experiments were performed to qualify the instrument by examining
the sample to find the D and G carbon peaks as found in the literature. Secondly, experiments
were performed to determine if the laser caused damage to the carbon structure. Through the
use of optical filters, changing the laser power and sample exposure time, the laser was
optimized to minimize damage to the carbon. Sample damage was obvious through the
increasing D peak intensity with exposure time and by the appearance of a dark spot on the
sample when viewed with the video camera and the laser turned off.
Analysis of spectral data was performed using the peak fitting program (Fityk). Fityk is a
shareware program 333. The peaks were corrected by fitting a baseline curve. The baseline was
196
subtracted from the original data and then the baseline corrected curve was fitted with
Gaussian or Lorentzian type peaks. In all cases the data could be fitted with two or three
peaks. The program is able to integrate the area of the individual fitted peaks and provide peak
heights and peak area. A graph of peak height versus peak area for all samples shows that
there is no clear correlation between these two parameters. In this analysis the peak areas are
used. Table 8-4 contains a list of the samples investigated using the Raman technique.
Table 8-4: Sample designation of Raman Samples. # indicates file of Raman spectra and that the measurement was repeated on a different part of the sample.
Sample Name
SCV3a_# SCV5_# SCV6_# SCVM_# SC# DS DS450
Description Ammonium metavanadate impregnated sucrose char (Dried at 125°C in air)
SCV3a exposed above 200°C (473K)
SCV3a exposed above 260°C (533K)
Mechanical mixture of V2O5 and sucrose char
Sucrose char exposed to 400°C in air (SC3, SC2, same sample different area)
NIST diesel soot
NIST diesel soot exposed to 450°C
Ammonium metavanadate is not the active form of V2O5 and samples SCV5 and SCV6 are
thermally treated in air to convert the ammonium metavanadate into reaction intermediates in
the reaction formation pathway of V2O5. V2O5 is not formed until temperatures of 400°C are
reached. During the heat up of the sample the intermediate vanadium compounds are likely
changing the carbon characteristics and likely converting the carbon into CO or CO2.
8.3.1.6 Results
One of the parts of this project is to characterize the active sites of the carbon. Raman
spectroscopy has been used to compare the disordered carbon peak to the graphite peak. The
disordered peak has been proposed to indicate edge site characteristics.
197
Raman studies on vanadium impregnated sucrose char, non-catalyzed sucrose char and diesel
soot (Table 8-4) have been performed. The disordered/graphite peak ratios (D (1350cm-1)/G
(1600 cm-1) were calculated by fitting the collected data with Gaussian peak shapes using a
shareware program 333. Screenshots of the fitted data are shown in Figure 8.3.1 and Figure
8.3.2.
Figure 8.3.1: Raman spectra of SCV3a x-axis: wavenumber, y axis: intensity, dots= data, solid line fitted peaks
198
Figure 8.3.2: Raman Spectra of SCVM_4 x-axis: wavenumber, y axis: intensity, dots= data, solid line fitted peaks
A comparison of the diesel soot and the sucrose char indicates that there are some differences
between materials (Figure 8.3.3). These differences may later explain any reactivity
differences between the model sucrose char and the diesel soot. The most observable
difference is the lower Raman intensity of diesel soot compared to the char. This difference
could be attributed to higher graphitic and crystalline character of the char or more interference
in diesel soot preventing the photons from reaching the detector
The effect of the soluble organic fraction (SOF) on the disordered carbons was considered as a
possible cause of the low intensity of the NIST soot. A sample of the NIST soot was heated to
450°C in air to oxidize the hydrocarbon (SOF). It is suspected that oxidizing the hydrocarbons
in air may affect the carbon sites by possibly creating disorders or exposing disorders. Results
show (Figure 8.3.3) that the adsorbed hydrocarbon does not affect the D/G ratio. A repeat
measurement of the DS 450 sample shows that the D/G ratio measurement varies by more than
25%.
Vanadium impregnated sucrose char was used to test if Raman would be able to measure the
catalysts occupying edge sites of carbon. An initial sample was made by impregnating sucrose
char (SC) with ammonium metavanadate (SCV3a). Ammonium metavanadate forms three
decomposition products: ammonium tetravanadate at 453K, ammonium hexavanadate at 503K
199
and V2O5 at 623K 334. Samples of SCV3a were exposed to 473K (SCV5) and 533K (SCV6).
The further exposure to temperature of V2O5 was not performed. In addition, a mechanical
mixture of V2O5 and sucrose char was made. The Raman spectra show features of the V2O5
below 1000cm-1 (Figure 8.3.3). This was confirmed by performing a baseline on the V2O5.
The data from the vanadium-impregnated samples is not clear. Ammonium metavanadate
appears to improve the D/G ratio when compared to non-catalyzed sucrose char. This
exposure to higher temperatures either causes the destruction of the edge sites or changes their
Raman feature. A baseline of ammonium metavanadate exposed at the different temperatures
was not performed and thus the effects on Raman features from ammonium metavanadate
cannot be discounted. Further Raman studies are needed to clarify this.
In addition, Raman spectra have not been taken of the reacted carbons. Samples of partial
reacted carbons need to be analyzed by Raman to determine if the D/G ratio has changed due
to reaction or by wetting of the catalysts; these changes in the Raman spectra may be correlated
to reaction data.
Correlation of the Raman data to oxygen chemisorption data is proposed as a means to quantify
the Raman data. It may be possible to perform these experiments similarly to those used to
determine the amount of loosely held carbon oxides. Exposure of the sample to He and
increasing the temperature of the reactor to high temperatures to remove the weakly held
oxygen groups and then cooled. An oxygen pulse can be passed over the sample to reoxidize
the sites and then reheated. This may require more sensitive analytical equipment. Further
thought is need to on how to perform this experiment.
200
0
0.5
1
1.5
2
2.5
3
3.5
4
scv3
a_3
scv3
a_2
scv3
a_1
scv5
_1
scv6
_2
scvm
_4 sc3
SC2
Ds450
_2
DS450_
1DS-1
D/G
Rat
io
0
5000
10000
15000
20000
25000
30000
35000
40000
Are
a
D/G Fityk D Fityk G
Figure 8.3.3: D/G peak ratios of Raman spectra
8.3.1.7 Conclusion
It was observed that exposure of the carbon to the laser gradually created an increase in the
intensity of the D and G peaks similar to others127. This is suspected to be a result of localized
heating of the carbon surface and reaction with oxygen in the air. Changes in peak heights
were observed by changing the laser power and length of time exposure of the carbon surface
to the laser. Attempts to reduce this effect were not successful. A possible solution would be
to perform the experiment under non-reactive gas conditions (i.e., under He or AR only
conditions). In addition sample spinning is necessary to prevent self-heating as discovered by
others more than 30 years ago.
201
8.4 Appendix D: PCA plots
Figure 8.4.1: PCA Loading Plots for Sample CAT-0 negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.
202
Figure 8.4.2: PCA Loading Plots for Sample NIST-ANN negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.
203
Figure 8.4.3: PCA Loading Plots for Sample SC_NOX negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.
204
Figure 8.4.4: PCA Loading Plots for Sample SC_AIR negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicates identified groupings within the TOFSIMS data.
205
Figure 8.4.5: PCA Loading Plots for Sample NIST-0 negative ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicates identified groupings within the TOFSIMS data.
206
Figure 8.4.6: PCA Loading Plots for Sample NIST-0 positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.
207
Figure 8.4.7: PCA Loading Plots for Sample CAT-0 positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.
208
Figure 8.4.8: PCA Loading Plots for Sample NIST_ANN positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.
209
Figure 8.4.9: PCA Loading Plots for Sample SC_NOX positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.
210
Figure 8.4.10: PCA Loading Plots for Sample SC_AIR positive ions: Loading plot (a) and Biplot (b) of PC1 versus PC2 (▼- scores, ♦ - loadings), Cumulative variance captured by each PC (c). Loading plots indicate identified groupings within the TOFSIMS data.
211
8.5 Appendix E: Certification and specification sheets
212
213
214
215
216
217
218
219
220
221
222