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Classification and Characterization of Cellulosic Materials by Chemometrics

Matthew Leonard

Matthew Leonard, B.Sc. (Chemistry)

Honours RMIT - 2010

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Classification and Characterization of Cellulosic Materials

by Chemometrics

A thesis presented for Bachelor of Applied Science Honours in Chemistry

School of Applied Sciences (Chemistry)

RMIT University

November 2010

by

Matthew Leonard

B.Sc. (Chemistry), La Trobe University 2007


Honours RMIT - 2010

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Declaration

I, Matthew Leonard, certify that except where due acknowledgment has been made, this work is

mine alone; it has not been submitted previously, in whole or in part, to qualify for any other

academic award; the content of the thesis is the result of work which has been carried out since the

official commencement date of the approved research program; and, any editorial work, paid or

unpaid, carried out by a third party is acknowledged.

Matthew Leonard

04-Nov-2010


Honours RMIT - 2010

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Acknowledgements

Professor Robert Shanks, for taking me on board as a student. You have seen talent in me that few

noticed before. Your adoption of others’ ideas and positive thinking and has allowed me to realize

my full potential. I look forward to many more years of us working together. Thank you.

Dr Bruce James, for taking the time and attention to help me find my confidence. You have freed

me from a life of caste. You always saw my true nature. I miss you.

Dr Jeff Hughes, for taking me on board as a student. Your approach to teaching is first class. We are

blessed that people like you still exist. Your common sense and work rate are matched by no

lecturer. You have empowered me with key elements previously absent from my repertoire. Thank

you.

Dr Ian Potter, for always being there for a chat, some good advice and a laugh. I continue to channel

your unique ability to selectively focus on good science and teaching. Thank you.

Roslyn Lampard, for instilling in me that study always comes first, not to be afraid to use

knowledge to the best of our abilities and that hard work pays off. You have encouraged me to think

long term and helped me on the path to leadership. Thank you.

Associate Professor Barry Meehan, for help with sample collection. You have set a positive

example for me and given me many new ideas. Thank you.

Karl Lang, for finding the time to welcome me to RMIT. Not only have you made me feel

comfortable in my workspace, but you have kept things functional when nobody else could. You

are a very underappreciated person. You have never let me down. Thank you.

Professor Philip Marriott, for teaching me impromptu advanced separation science. A game of catch

up was required to achieve results. Your diligence allowed me to succeed. Thank you.

Dr Janetta Culvenor, for demonstrating a calm and effectual handle of situations. You have been an

example to me of a good scientist and person for a very long time. Thank you.

Dr Nichola Porter, for helping drive me to a more sustainable life. Your honest confidence places

people at ease, while getting the job done right. You have shown me how to spot detail in a sea of

information. Thank you.

Steve Spoljaric, for constantly being nearby to help with the smallest problems. From making a

coffee to writing a thesis, you are always able to pitch in. You will have a great career as a scientist.

I look forward to your future work after your PhD. Thank you.

Peter Laming, for teaching me many small curiosities. Your honest advice has guided me well.

Thank you.

Izan Roshawaty Mustapa, for being close by with a glowing smile daily. Despite your workload of

family and study, you always remain positive. I admire your hands on approach and determination

to achieve your PhD. Thank you.

Professor Mike Adams, for welcoming me to RMIT with a kind smile. Despite the many stresses of

your position, you manage to go out of your way to make others feel good about themselves. You

have also taught me the concept of Principal Components Analysis and the role of the

Chemometrician. Thank you.


Honours RMIT - 2010

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Dr Mohammed Al Kobaisi, for being there to share a laugh at the toughest moments. Your advice

on people skills has helped me greatly. You are a kind hearted and talented individual. Australia is a

better place now that you have joined us. Thank you.

Dr Ing Kong, for support and appreciation and the occasional walk for a hot chocolate. Your

presence has made a very positive difference. Thank you.

Cam Pham, for welcoming me to RMIT. You have had a struggle to get through your degree. You

are a highly skilled chemist, with great future prospects. I look forward to us spending more time

together after Honours. I will continue to advocate for you becoming gainfully employed. I wish I

was in a position to help you more. You are great company. Thank you.

Frank Antolasic, for sharing your observations and working hard to keep my spectra on track.

Thank you.

Dr. Cybele Lotti, for putting a smile on my face and supplying valuable samples. Thank you.

Blake Plowman, for help with Raman spectra that showed the way. Your depth of knowledge has

been very valuable. Thank you.

Michael Czajka, for your thoughts and willingness to learn. Your persistence and open mind has led

you to adopt many good ideas and share them with others. You have helped me tirelessly with a

variety of topics. You are a true Polymath, something to which I aspire. Thank you.

Bryan Hill, for good advice towards stressful situations and feedback on work done. I will now have

a good friend in WA. Thank you.

My housemates; Lauren, Oliver and Angel, for an unforgettable year. You have provided me with

much needed support. Thank you.

To my partner and future wife, Jennifer, for being so close in an emotional way. You are the most

loving person I have ever met. I am so relieved that I managed to find you before we were old. I

look forward to seeing you first and last every day. We will have a happy life together and one

reason for that is that you have helped me to get through my postgraduate studies. I will love you

forever. Thank you.


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Table of contents

Declaration .................................................................................................................................. ii

Acknowledgements..................................................................................................................... iii

Table of contents ........................................................................................................................ v

List of figures .............................................................................................................................. vi

List of tables ................................................................................................................................ iix

List of terms ................................................................................................................................ ix

Abstract ....................................................................................................................................... x

1 Introduction .................................................................................................................... 1

Cellulosics 1

Cellulose 1

Hemicellulose 3

Pectin 3

Lignin 6

Tannins 8

Aim and objectives 12

Recent research 13

Instrumental methods for analysis of cellulosics 16

Chemometric methods 21

Instruments used 23

Chemometric techniques used 25

2 Methodology ................................................................................................................... 26

Sample collection and preparation 26

Instrumentation 28

Data treatment 32

3 Results and Discussion ................................................................................................... 33

TGA 33

FTIR 35

Raman 37

WAXS 38

Chemometrics 41

TGA instrument Intra-Day variance 41

FTIR standard deviation and drift 42

Principal Components Analysis 43

Linear Discriminant Analysis 44

4 Conclusion....................................................................................................................... 49

5 References ....................................................................................................................... 50


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6 Appendices ...................................................................................................................... 55

Appendix A: Thermograms 55

Appendix B: FTIR spectra 58


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List of figures

Figure 1.1. Molecular structure of cellulose 1

Figure 1.2. Mercerized cotton towel 2

Figure 1.3. Cellulose I and II 2

Figure 1.4. Major hemicellulose monomers 3

Figure 1.5. A piece of hemicellulose 3

Figure 1.6. Homogalacturonan – base molecule of pectin 4

Figure 1.7. Borate 1:2 ester diol crosslinked with two Rhamnogalacturonan II units 5

Figure 1.8. A piece of lignin polymer 6

Figure 1.9. Common monolignols 7

Figure 1.10. Proposed model of structural organization of cellulosics in wood 7

Figure 1.11. Gallic acid – A monomer of tannin 8

Figure 1.12. Condensed tannin 9

Figure 1.13. Complexed tannin 9

Figure 1.14. X-ray diffractograms of treated hemp fibres 13

Figure 1.15. Schematic of the crystalline structure transformation by NaOH treatment 14

Figure 1.16. Thermograms and DTG curves of biomass obtained by TGA 16

Figure 1.17. Proposed kinetic scheme for the main step of cellulose pyrolysis 17

Figure 1.18. Raman spectra of generated mixtures of cellulose I and II 19

Figure 1.19. SEMs of fine and coarse flax fibers 19

Figure 1.20. RDA misclassification plot of polyethylene samples by density 23

Figure 2.1. Studley Park Reserve, Kew 26

Figure 2.2. Serrated Tussock sample 26

Figure 2.3. Dried Serrated Tussock 27

Figure 2.4. Banana fruit fibres 27

Figure 2.5 Coconut with husk fibres 27

Figure 2.6. Perkin Elmer TGA7 29

Figure 2.7. KBr grinding of sample and diffuse reflectance sampler 29

Figure 2.8. Perkin Elmer RamanStation 400F spectrometer 30

Figure 2.9. Flax samples in Nanostar sample holder 31

Figure 2.10. Bruker Nanostar spectrometer in 4.8 cm sample to detector mode 31

Figure 3.1. TGA thermograms of cottons and Avicel 33

Figure 3.2. TGA thermograms of bast fibres and woods 33

Figure 3.3. TGA thermogram class comparison 34

Figure 3.4. FTIR spectrum of jute 35

Figure 3.5. FTIR spectrum of coconut fibre 36

Figure 3.6. FTIR spectrum of Avicel 36

Figure 3.7. Raman spectra of hemp 38

Figure 3.8. WAXS diffractograms of cellulosics 39

Figure 3.9. Integrated diffractograms of cellulosics 40

Figure 3.10 Intra-Day variance of flax; Pyris 1 TGA 41

Figure 3.11 Intra-Day variance of flax; TGA7 41

Figure 3.12 Standard deviation of FTIR sample spectra 42

Figure 3.13 FTIR of all 4 cotton samples showing baseline drift 42

Figure 3.14 Score plot of PC1 vs PC2 from TGA data 43

Figure 3.15 Score plot of cellulosics PC1 vs PC2 (TGA data) 44

Figure 3.16. LDA class centroid profiles from TGA data 45

Figure 3.17. LDA class centroid profiles PC1-6 from FTIR 45

Figure 3.18. LDA class assignments from PC1-6 of TGA data 46

Figure 3.19. LDA cross validated class assignments from PC1-6 of TGA data 46

Figure 3.20. LDA class assignments from PC1-6 of FTIR data 47

Figure 3.21. LDA cross validated class assignments from PC1-6 of FTIR data 47


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List of tables

Table 1.1. Cellulosic components 10

Table 1.2. XRF analysis of ashes of sugarcane bagasse 15

Table 1.3. Common instruments for cellulosics observation 20

Table 1.4. Common Chemometric techniques 22

Table 2.1. Sample classes 28

Table 3.1. Peak assignments for FTIR of cellulosics 37

Table 3.2. X-ray diffraction peak positions and diffraction planes of cellulose polymorphs 39

Table 3.3. Eigenvalues and % variance of Principal Components from TGA and FTIR data 43


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List of terms

CP/MAS 13

C-NMR Cross Polarization Magic Angle Spinning

Carbon-13 Nuclear Magnetic Resonance

Spectroscopy

DPLS Discriminant Partial Least Squares

DSC Differential Scanning Calorimetry

FTIR Fourier Transform Infrared Spectroscopy

ICP-MS Inductively Coupled Plasma-Mass

Spectrometry

KNN K-Nearest Neighbours

LDA Linear Discriminant Analysis

MTG Modulated Thermogravimetry

NIR Near Infrared Spectroscopy

PCA Principal Components Analysis

PLS Partial Least Squares Regression

QDA Quadratic Discriminant Analysis

Raman Raman Spectroscopy

RDA Regularized Discriminant Analysis

SAXS Small Angle X-ray Scattering

SEM Scanning Electron Microscope

SIMCA Soft Independent Modelling of Class

Analogies

TGA Thermogravimetric Analysis

TG-MS Thermogravimetry-Mass Spectrometry

UV-Vis UltraViolet-Visible light Spectroscopy

WAXS Wide Angle X-ray Scattering

XPS X-ray Photoelectron Spectroscopy

XRD X-ray Diffraction


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Abstract

Cellulosics were gathered and chemical structures characterized by TGA, FTIR and WAXS.

TGA thermograms were compared and contrasted with FTIR band assignments for cellulosic

composition. TGA and FTIR data was extracted and used with chemometric methods to

classify cellulosics by source. Results showed some level of grouping and discrimination by

principal components (PC) score plots. Linear Discriminant Analysis (LDA) has correctly

differentiated 15 of 17 samples classed by source of cellulosic using both TGA and FTIR

data. Cross validated LDA class assignments were reduced to 10 out of 17 for both TGA and

FTIR data; likely due to small numbers of samples in classes (3-4). Further information may

be obtained by this approach with a more comprehensive investigation which may involve

more samples from each class and/or analysis of separate classes for further method

development of cellulosics classification.


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1. Introduction

Cellulosics are a chemical group of much interest in materials science. Due to their

abundance they are very worthwhile to understand but have complex molecular structure.

Cellulosics occur in wood, fibres and other plant materials. The main components of

cellulosics are cellulose, hemicellulose, pectins, lignin and tannins with the distribution

varying by structure and function of the cellulosics.

Native cellulosics have high porosity/air permeability1, compressive strength

2 and absorb

water3. Cellulosics exhibit a wide range of dimensional stability. They are stable at room

temperature, undergoing pyrolysis on heating and combustion on burning. These physical

properties vary by chemical composition of the cellulosics. A deep understanding of

cellulosics is important to allow prediction of such properties.

The base molecule of cellulose is made up of β-1,4-glycosidic linked glucose units4.

Figure 1.1. Molecular structure of cellulose

Cellulose occurs in conjunction with other substances. In wood cellulose is combined with

lignin, hemicellulose and waxes5. Wood cellulose may be purified

6 to pulps (~96%) or Avicel

(99.9%). In fibres such as flax and hemp cellulose is combined with pectins, hemicellulose

and waxes7. In other plant materials the organic components may be more complex and partly

composed of inorganic material.


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Native crystalline cellulose forms macrofibrils (300-500 μm) made up of microfibril bundles

of diameter 20-30 nm8. Cellulose fibres may be treated with NaOH; a process known as

mercerization9. This restructures and purifies cellulose

10 to give holocellulose; the total

polysaccharide fraction. Removal of lignin9, pectins and waxes gives fibres with enhanced

physical properties11

. Removal of pectins by mercerization leads to increased thermal

resistance12

and fibre durability13

.

Figure 1.2. Mercerized cotton towel

Cellulose has four polymorphs; I, II, III and IV, distinguishable by Wide Angle X-ray

Scattering (WAXS)14

. Cellulose crystal forms I and II co-exist in native cellulosic sources,

with Cellulose I in the majority. Cellulose I is converted irreversibly to cellulose II by NaOH

treatment known as mercerization12

. Cellulose I is thought to have a crystal structure with

monomeric chains in parallel conformation and cellulose II to have chains in an anti-parallel

conformation15

(Figure 1.3.)16

. Crystal structure I has been shown to be made up of Iα and Iβ

forms17

.

Figure 1.3. Cellulose I and II16


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Hemicellulose is an amorphous18

polysaccharide composed of hexose and pentose sugars5.

Xylose is the sugar monomer present in the largest amount19

. Other sugars include arabinose,

mannose and galactose19

.

Figure 1.4. Major hemicellulose monomers

It is hard to distinguish glucose from acid hydrolysis of cellulosics as originating from

cellulose or hemicellulose19

. Hemicellulose consists of shorter chains; 300 - 2 000 sugar

units20

as opposed to 7 000 - 15 000 in cellulose21

. Hemicellulose is a branched polymer while

cellulose is unbranched22

.

Figure 1.5. A piece of hemicellulose

Pectin; from the Greek pektikos, meaning congealed or curdled is a heteropolysaccharide

found in cellulosics. Pectin binds to cellulose23

but has a more complex structure than

cellulose or hemicellulose. The base molecule of pectin, homogalacturonan is a linear chain of

α-(1-4)-linked D-galactosyluronic acids in which some of the carboxyl groups are methyl

esterified24

.

http://en.wikipedia.org/wiki/D-galacturonic_acid


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Figure 1.6. Homogalacturonan – base molecule of pectin

24

In native pectins ~70 % of the carboxyl groups are methylated25

. These methoxy groups may

be a precursor to atmospheric methane26

. Three pectic polysaccharide groups have been

characterized; Homogalacturonans, substituted galacturonans and rhamnogalacturonans24

.

Non methyl-esterified galacturonans such as substituted galacturonans and

rhamnogalacturonans are less abundant. Rhamnogalacturonan II exists predominantly in a

crosslinked form of a 1:2 borate diol ester complex27-28

(Fig 1.7.).


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Figure 1.7. Borate 1:2 ester diol crosslinked with two Rhamnogalacturonan II units24

Pectic polysaccharides may be cross-linked by Ca2+

bridges29

, phenolic coupling,

galacturonate ester bonds30

or borate-diester bonds27

. Homogalacturonan is believed to be

covalently linked to Rhamnogalacturonan I and II in the plant24

. Other components of pectin

include xylogalacturonan and apiogalacturonan31

.

Pectins become more thermally stable with increased methyl-esterification25

. Acid treatment

of pectin gives low-esterified pectins32

.

Pectin is a soluble dietary fibre33

as it goes through the small intestine mostly intact. Pectin

finds use in food products; as a thickener in many foods such as jams and jellies34

. With

sufficient sugar in a mixture pectin forms a firm gel. As low ester and amidated pectins are of

higher viscosity35

; they require less sugar so may be used for diet foods. Pectins are used in

lozenges as a demulcent to form a soothing film over mucous membranes to relieve pain and

inflammation36

. Pectins can protect the stomach against gastric mucosa37

. As pectin binds

water it keeps products moist. Pectin combines with calcium and whey proteins to stabilize

foams and gels made with cream or milk29, 38

. Pectin also stabilizes emulsions39

.

Holocellulose is the term given to the polysaccharide portion of cellulosics. This includes

cellulose, hemicellulose and pectin.


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Lignin; from the Latin lignum, meaning wood is a low modulus40

amorphous41

polymer. Its

racemic macromolecular structure is highly phenolic5 and crosslinked

42. Lignin makes up

15-30 % of the dry mass of cellulosics43

.

Figure 1.8. A piece of lignin polymer

In the carbon cycle lignin sequesters atmospheric CO2 into woody vegetation. Lignin

decomposes slowly into a major fraction of the material that becomes humus44

to improve

forest soil by increased cation exchange capacity, moisture retention and photosynthesis of

plants45

. Petrified lignin is the source of fossil fuels from plants44

. Lignin acts as a pipe for

passage of water in plant stems due to its high hydrophobicity compared with polysaccharide

components of cellulosics (Fig 1.10). The crosslinking of polysaccharides by lignin is an

obstacle for water absorption to the cell wall, thus lignin makes it possible for plant vascular

tissue to conduct water46

. Lignin’s high calorific value, importance in the environment and

ability to give density and strength to wood mean that while it is slow growing it is also the

most valuable component of wood. Lignin is not a simple polymer with a regular repeat


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unit47

. The majority of lignin is made up of methoxylated phenolics called monolignols48

(Fig 1.9.) which congeal together to give the crosslinked macromolecule.

Figure 1.9. Common monolignols: paracoumaryl alcohol 1, coniferyl alcohol 2 and sinapyl alcohol 3

In cooking, hardwood lignin is the source of methoxyphenols which along with tannins give

the desired aroma and taste to smoked foods49

.

Figure 1.10. Proposed model of structural organization of cellulosics in wood50

If wood is a composite, lignin is the matrix material that holds polysaccharide fibrils51

(Figure 1.10). In woods lignin acts as an unwettable structural material and increased lignin


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content gives enhanced dimensional stability52

. Lignin gives mechanical strength to cell

walls53

and the plant as a whole as it is covalently bonded to holocellulose54

(Fig 1.10.).

High lignin wood is dense and a good material for many applications. Lignin is abundant in

compression wood but scarce in tension wood21

. Lignin yields more energy when burned than

cellulose55

so high lignin woods make better fuel. Mechanical pulp used for newsprint has a

portion of lignin retained. This cheaper product gives more pulp per ton of wood but higher

acid content reduces its archival properties56

. Lignin is responsible for newsprint yellowing

with age. This also explains why newsprint burns better than plain white paper. Lignin must

be chemically removed from pulp to produce high quality bleached paper57

. Removed lignin

is burned in a recovery boiler for energy to run the mill58

. More recently extracted lignin from

wood has been used for polyols in polyurethane foam59

.

Tannins are polyphenolics which can give a strong colour for leather tanning and may act as a

defence mechanism for plants. Tannins present in coffee, chocolate and red wine can cause

yellowing of teeth60

. Tannins effect grape ripening and ageing of wine61

. Tannins are found in

fruit, vegetables and nuts where they give a bitter taste62

. High tannin intake can be toxic to

humans63

.

Figure 1.11. Gallic acid – A monomer of tannin

Tannin monomers are small phenolics units (Fig 1.11.) which may be hydrolysed and

condensed (Fig 1.12.).


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Figure 1.12. Condensed tannin

Condensed tannins may then form complexes with sugars (Figure 1.13) by hydrolysable ester

bridges64

.

Figure 1.13. Complexed tannin

Milk proteins are also known to form complexes with tannins65

. This is why milk is added to

tea and coffee to neutralize the bitter tasting components66

. Little work has been done to

characterize the thermal stability of tannins.


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Common components of cellulosics are summarized in Table 1.1.

Table 1.1. Cellulosic components

Component Composition Molecular units Properties Abundance

Cellulose Glucose units 7 000 - 15 000 Strong

microfibrils ~40

Hemicellulose Hexose/pentose

sugars 500 - 3 000 Varied ~5

Pectin Methyl-esterified galacturonans

60 - 130 000 Gel forming ~10

Lignin Methoxylated

phenolics 10 000 + Hydrophobic ~30

Tannin Polyphenolics 500 - 20 000 Strong

colour/odour ~2

Wax Long chain

hydrocarbons 50-100 Malleable ~1

Considering the complex molecular structure of cellulosics, it would be beneficial to develop

methods to identify the source. With this goal in mind, some common cellulosic sources

include:

Bast Fibres; cellulosic materials collected from the phloem or inner bark of certain

plants. Biologically they provide strength to the plant and act to transport sugars67

.

Bast fibres such as hemp may be mercerized to remove lignins and produce durable

fibres high in cellulose II. They may also be swelled in water followed by bacterial

decay; a process known as retting which removes pectins and separates fibres from the

outer stem. Treated and untreated bast fibres are used to make clothing, rope, carpet,

sacks and tents.

Wood; a lignocellulosic material readily available for construction, fuel and pulp

making. The cellulosic composition of wood dictates its properties. Wood absorbs

moisture and in order to retain shape (dimensional stability) must be dried slowly52

.

As wood is burned as fuel, controlled thermal analysis of wood will lead to improved

identification techniques for best source & physical properties. Lyocell fibres are made

from regenerated wood pulp for use as fabric68

. Avicel is a microcrystalline cellulose

powder69

made from grinding high purity wood pulp and is used in makeup, sunscreen

and as an inert filler in food70

. It adds no taste, smell or consumer calories to food.


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Cotton; a plant native to subtropical regions. Cotton is a unique type of plant fibre. It

is valued for its high purity cellulose that comes in durable bolls. Mercerized cotton is

used for clothing and fine fabrics.

Grasses/tussock; cellulosic source that can grow unaided in many parts of the world.

Grasses and tussock are often planted for aesthetic purposes. Grasses such as

switchgrass (Panicum virgatum) and Miscanthus giganteus may be grown for biofuels.

Conversely they may be a pest, such as South American Nassella trichotoma known

as serrated tussock (introduced to Australia).

Cellulosic biomass; cellulosic by products from industry may be used for fuel. Food

waste such as from banana, coconut and olive production may give cellulosics for fuel

making prediction of calorific value important. Coconut fibres between the hard

internal shell and outer coat are retted to produce coir to make door mats, brushes or

pot plants. Cellulosic fibres from industrial waste may be used as reinforcement for

polymer matrices in composites71

.

Other cellulosic materials; Cellulose acetate, cellulose nitrate and Bacterial cellulose

are also of interest particularly for crystallinity.

With such a wide variety of cellulosics in use, the original source of a sample may not be

clear. It may also be desired to characterize crystal form or molecular component distribution

by simpler, quicker, cheaper technology than that presently used. As crystal form and

molecular component distribution determine properties of the material, rapid characterization

and classification methods may lead to a wide number of applications.


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Aim and objectives

Aim: Explore Chemometric classification techniques for cellulosics to add to knowledge of cellulosics chemistry.

Objectives:

• Gather and prepare cellulosic samples from a variety of sources

• Analyze with proven instruments using repeatable and non-biased experiments to

produce spectral data

• Extract and treat data for application of proven Chemometric techniques

• Observe chemistry of cellulosics and explore techniques to classify cellulosics by

source

• Add to knowledge for further research by showing whether there is useful information

by this approach


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Cellulosics that have been observed in recent literature include bast fibres such as jute72

,

sisal73

, flax2, 74-75

and hemp12, 76

. Bos et al. have shown compression failure of flax fibres to be

similar to that of stranded wire2. Martin et al. have compared chemically treated with native

sisal to show that removal of lignin lowers the thermal stability of sisal fibres73

. Wang et al.

have observed an interconnected web-like structure of hemp fibres after mercerization13

.

Much interest in Mercerization and cellulose crystal polymorphs exists in recent literature.

WAXS has shown the distribution of cellulose crystal forms I and II from bast fibres12

,

composites70

, cotton6 and wood

6, 77. Ouajai et al.

12 have used changes of peak position in

X-ray diffractograms to show conversion of cellulose I to cellulose II in mercerized hemp

fibres (Figure 1.14.)

Figure 1.14. X-ray diffractograms of treated hemp fibres12

An early proposal for the mechanism of mercerization of cellulosics was by production of

Na-cellulose I starting in amorphous parts of the fibre, giving conversion from parallel to

antiparallel polymer crystal stereo configuration78

. Low crystallinity cellulose proceeds by

this mechanism more easily78

which can be obtained by ball milling to reduce cellulose order


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to a mostly amorphous state79

. Fink et al. have proposed a nonuniform fringed fibrillar

model16

rather than the parallel-antiparallel transition of chain arrangement. Gwon et al. have

proposed a mechanism where Na+ ions effect the degree of fibre swelling by widening the

smallest pores of the cellulose I polymorph80

. This mechanism has an intermediate lattice

where OH groups are replaced with larger dimension ONa groups. Na is then removed by

water during rinsing to leave the reordered cellulose II polymorph (Figure 1.15.).

Figure 1.15. Schematic of the crystalline structure transformation by NaOH treatment80

Borysiak et al. have shown that degree of conversion of cellulose polymorph I to II in

beechwood depends on concentration and time of NaOH treatment77

.

Thermal properties have been observed of viscose1, 3

; a clothing fibre regenerated by

extrusion of hemp cellulose. Stanković et al. have shown that natural fibre fabrics give better

heat transfer properties than regenerated fibre fabrics1.

The thermal decomposition of cellulosics is of great interest. Wood cellulosics have been

observed for degradation and crystallinity properties in species such as Norway spruce81

,

pine82

and eucalypt83

. Thermal decomposition of Lyocell; a fibre from regenerated wood, has

been observed by Dadashian et al. using FTIR and WAXS84

. Cotton products and sources

have been observed for thermal properties85

, crystallinity86

and best harvest time87

. Crystallite


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size of cellulose polymorph Iβ has been shown not to effect activation energy for thermal

degradation88

. Brosse et al. have shown that average molecular weight of heat treated milled

wood lignin is lower than native wood lignin89

. Müller-Hagedorn et al. have observed the

pyrolysis kinetics of wood90

. Avicel has been compared to unrefined cellulose forms81

.

Chabannes et al. have shown that lignin in cell walls of transgenic tobacco is tightly and

independently regulated in individual cell types of the plant91

.

Serrated tussock; a weed of significance in Victoria92

is known as a pest due to its high fibre

and low protein content93

. Relatively little peer reviewed research of its properties has been

published. Recent biomass research includes coconut husk fibres94

, switchgrass95

and sugar-

cane bagasse92, 94, 96

. Mothé et al. have analyzed chemical composition by X-ray fluorescence

(XRF) of ash residue from sugarcane bagasse in TGA94

.

Table 1.2. XRF analysis of ashes of sugarcane bagasse94

Naik et al. have determined sugar monomer content from cellulosic biomass by acid

hydrolysis and high performance liquid chromatography (HPLC)5. The study also showed that

pinewood had a lower lignin content than other sources observed by FTIR5.

Lee et al. have compared thermograms and derivative thermograms from a variety of biomass

sources (Figure 1.16.)97

.


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Figure 1.16. (A) Thermograms and (B) DTG curves of biomass obtained by TGA (heating rate 25 °C·min-1

)97

Methods; for analysis of cellulosics in recent literature are widely varied. They include

TGA98-99

, FTIR86, 100-101

, WAXS6, 77

, Raman spectroscopy102

, Scanning Electron Microscopy

(SEM)103

and solid state Cross-Polarisation Magic Angle Spinning Carbon-13 Nuclear

Magnetic Resonance (CP/MAS 13

C-NMR)104-105

.

Cellulosics have characteristic thermal degradation patterns that depend on structure of the

source material. It has been observed by TGA that higher lignin content leads to increased

thermal stability12, 106

. Mamleev et al. have used Modulated thermogravimetry (MTG) to

propose a main step of mass loss in the thermal decomposition of cellulose by a pyrolysis

reaction that proceeds in the presence of oxygen107

. This is a depolymerisation and unzipping

reaction by transglycosylation (Figure 1.17.).


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Figure 1.17. Proposed kinetic scheme for the main step of cellulose pyrolysis107

Shanks et al. have used TGA to show change in crystallinity of hemp fibres after

mercerization12

and bioscouring103

. Donato et al. have shown that archaeological wood may

be assessed by TGA for preservation treatment 99

.

FTIR may be used to characterize cellulosics108

. Naik et al. have characterized biomass

cellulosics by FTIR of solvent extract5. Extracts showed consistent yield of glucose, but a

large deviation of cellulobiose, xylose, galactose, arabinose and mannose among cellulose

source5. Ouajai et al. have shown a way to observe the level of cellulose crystallinity by

intermolecular hydrogen bonding from FTIR spectroscopy. As hydrogen bonding can effect

the electronic environment of the covalent bonds, a change in the bands in FTIR spectra

occurs with differing crystallinity. This was confirmed by correlation with WAXS results4.

Multiple internal reflectance is a sampling method for FTIR. A diamond or other crystal is

used as an internal reflectance element and can show chemical differences between inner and

outer layer of cellulosics109

.


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X-ray diffraction (XRD) which has been used to observe cellulose110

is interchangeable with

WAXS. Both methods describe crystal unit cell dimension by 2θ angle results from scattering

of X-rays. In WAXS X-rays are diffracted by passing through crystalline samples, whereas

XRD measures diffraction. Crystalline molecules give coherent scattering111

, also known as in

phase scattering. Greater in phase scattering or order in diffractograms shows a higher level of

crystallinity. For diffraction of photons the wavelength must be close to half of the particle

size, which makes X-rays suited to diffraction by molecules. Wider angles measure smaller

particles/crystals, while smaller angles measure larger particles112

. Ouajai et al.12

and

Borysiak et al.77

have used WAXS to show level of transformation to cellulose II from

mercerization.

Raman uses the concept of inelastic scattering of light to measure molecular structure from

vibrational modes. FTIR peaks tend to be intense from polar bonds like O-H and C=O but

weak from less polar bonds like C-C and C-H. The reverse is true with Raman. Polarity of

covalent bonds gives anharmonic vibration. More polar bonds and especially those involving

hydrogen generate weak overtones in the NIR region. The Raman Effect intensifies these

overtones and Raman spectra of cellulose are more difficult to achieve. Raman has only

recently developed to a point able to give acceptable quality spectra for cellulosics47

. As well

as a powerful laser, increased excitation time at 1064 nm can give more observable spectra as

lignin, which tends to fluoresce and give noise has significantly reduced absorption at that

wavelength47

. Band intensities have been extensively assigned to vibrational modes47

.

Schenzel et al. have used Raman to observe the transformation of cellulose I → cellulose II

by conversion of a peak due to glucopyranose ring structure from 352 cm-1

to 379 cm-1

(Figure 1.18)113

. They also showed creation of a peak at 1461 cm-1

due to CH2OH

polysaccharide side chains from two different stereochemical environments converting to

one113

.


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Figure 1.18. Raman spectra of generated mixtures of cellulose I and II representing adjusted degrees of

the polymorphic transformation of cellulose I → cellulose II113

These CH2OH groups were shown to hydrogen bond in a different lattice structure to effect

twisting modes113

which caused a peak at 1294 cm-1

to move to 1265 cm-1

.

Agarwal et al. have developed a silver nanoparticle treatment to enhance surface effects of

spruce milled-wood lignin and describe lignin monomers in more detail by Raman spectra47

.

SEM has been used for surface observation of cellulose fibres6. Zhao et al. have used SEM to

compare cellulose macrofibrils partially acid hydrolyzed and untreated cotton linters. They

found that cellulose macrofibrils became more agglomerated with increased acid hydrolysis

treatment8. Faughey et al. have compared thermogram data with Scanning electron

microscopy (Figure 1.19.) to show that fibre fineness in flax may be predicted from TGA74

.

Figure 1.19. SEMs of fine (a) and coarse (b) flax fibers, showing the presence of noncellulosic

polysaccharides cementing the fiber bundles (b). (Bars represent 20 μm)74


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CP/MAS 13

C-NMR measures cellulose crystallinity by different spin-lattice relaxation times

of crystalline and amorphous cellulose114

. CP/MAS 13

C-NMR results have correlated with

WAXS115

. Wickholm et al. have used CP/MAS 13

C-NMR to assign signals to amorphous

cellulose (80-86 ppm) and para-crystalline cellulose I (86-90 ppm) from pulp and cotton

linters116

. Delmotte et al. have used CP/MAS 13

C-NMR to show crosslinking of lignin by

rearrangement of methoxy groups in wood during friction welding100

. Common instruments

for observation of cellulosics are listed in Table 1.3.

Table 1.3. Common instruments for cellulosics observation

Instrument Shows

TGA Mass loss as a function of temperature IR Asymmetric vibrational modes of covalent bonds Raman Vibrational modes of less polar bonds WAXS Crystal polymorph distribution SAXS Distance between crystallites NIR Asymmetric vibrational modes/overtones 13C-NMR Change in chemical shift of Carbon-13 nuclear magnetic resonance SEM Surface images

Small-angle X-ray scattering (SAXS) can complement WAXS to observe cellulosics117

.

Leppänen et al. have used SAXS to detect average diameter of cellulose microfibril cores

from Norway spruce wood6. Kennedy et al. have measured diameter of celery microfibril

cellulose by SAXS118

. Elazzouzi-Hafraoui et al. have observed crystalline nanoparticles from

cotton and Avicel using SAXS to compliment WAXS119

. Other recent methods for cellulosics

observation include trace metal analysis by Inductively Coupled plasma-Mass Spectrometry

(ICP-MS)5, X-ray photoelectron spectroscopy (XPS)

120-121, Ultraviolet-Visible spectroscopy

(UV-Vis)122

, Fourier transform near infrared spectroscopy (NIR)123

, modulated

thermogravimetry (MTG)107

and TGA-mass spectrometry (TG-MS)124

.

ICP-MS has been used to analyze cellulosic pyrolysis residues extracted from TGA. Helsen

et al. have used this method to compare Cr, Cu and As levels in treated and untreated

sapwood. The presence of Cr, Cu and As were shown to lower pyrolysis onset temperature

and temperature of maximum mass loss rate125

. Rout et al. have used ICP-MS to determine


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elemental concentration in TGA ash residue from Biomass oil derived from wheat and wood

sawdust18

.

Vilela et al. have observed surface properties of prepared CaCO3-Cellulose nanocomposites

by XPS to show that carbon and oxygen were the dominant elements120

. Brígida et al. have

evaluated surface element distribution of NaOCl, NaOH and H2O2 treated coconut fibers121

.

Chen et al. have used UV-Vis to assess raw sugarcane bagasse used for bioethanol production

for its visible light absorption122

. This same study found that TGA was effective for

comparison of hemicellulose, cellulose, lignin, dextran and glucose levels in raw and acid

treated bagasse122

. NIR has used vibrational mode overtones to show chemical bonds in

thicker samples to reduce sample preparation time126

.

A wide variety of methods have been used for cellulosics analysis in recent literature. They

vary in cost, time, expertise, sample preparation and sample destruction. Some methods

complement others and often more than one is necessary for meaningful results. A wise

selection of technique can give insight into the physical properties and best use of cellulosics.

Some methods provide data that is suitable for use with Chemometrics for further

identification102

.

Chemometrics; is the science of extracting useful information from chemical data. This data

is analysed using chemometric methods and results may be visualized so that seemingly

insignificant data show clear patterns. Software includes Minitab, Unscrambler123

, MatLab

and Umetrics. Software may also be used for optimization of the design of experiments127

.

Chemometric techniques may be broken down into unsupervised (pattern exploration) and

supervised (pattern recognition). Unsupervised Chemometric methods include PCA 124, 128-130

,

Partial Least Squares (PLS) regression131

and Principal Components Regression (PCR)132

.

Supervised methods include LDA, K-Nearest Neighbours (KNN) and Discriminant Partial

Least Squares (DPLS) (Table 1.4.). The related methods LDA and QDA vary by their cluster

differentiation. LDA uses a straight line to define clusters while QDA uses a quadratic.


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Table 1.4. Common Chemometric techniques

Technique Type

Principal Components Analysis (PCA) Unsupervised Partial Least Squares (PLS) Unsupervised Principal Components Regression (PCR) Unsupervised Nearest Mean Classifier Supervised Linear Discriminant Analysis (LDA) Supervised Regularized Discriminant Analysis (RDA) Supervised Quadratic Discriminant Analysis (QDA) Supervised K-Nearest Neighbours (KNN) Supervised Discriminant Partial Least Squares (DPLS) Supervised Soft Independent Modelling of Class Analogies(SIMCA) Supervised

When applied to project data an unsupervised technique is first chosen for feature selection to

reduce data dimensionality. A supervised method is chosen to discriminate sample classes. A

common unsupervised technique is PCA with a plot of the first two Principal Components

used to visualize clusters. An unsupervised method is tested and another tried later if the first

gives poor response. Choice of supervised method depends on the size of the training set, how

closely the class population follows a normal (Gaussian) distribution and whether covariances

in the classes are equal133

. Wu et al. compared LDA, RDA and QDA using NIR data133

to

show that QDA is unlikely to produce satisfactory results unless the ratio of the class sample

sizes is large relative to the number of variables. QDA had no advantage over LDA except

when class covariance matrices were quite different. RDA gave results equal or better than

LDA and QDA133

. Roggo et al. compared supervised methods on NIR data to show that non-

linear methods gave less accurate results134

.

Recent Chemometric research has used TGA124

, FTIR135

and Raman113, 136

data for feature

extraction and classification. Lee et al. have used statistical analysis for quick assessment of

thermal decomposition behaviour of Lignocellulosic biomass by NIR and TGA97

. Mann

et al.95

and Malkavaara et al.137

used PLS on FTIR and UV-Vis data respectively to construct

models for rapid lignin content prediction. Basile et al. have used PLS cross validated by

RDA to quantify serpentine in nickel laterite ores by FTIR data135

.


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Gilbert et al. have used SIMCA with FTIR data to discriminate cellulosic fabrics by dye and

processing stage130

. Lennholm et al.128

have used a PLS with CP/MAS 13

C-NMR data to

quantify cellulose I crystal forms. X-ray and NMR spectrometers are expensive so

Chemometrics aims to find low costs alternatives. WAXS Bragg peaks have been predicted

with high correlation by PLS using NIR123

and Raman data102, 113

. Other polymeric

compounds may also be assessed by Chemometrics. Hughes et al. have characterized

polyethylenes by PCA score plots from differential scanning calorimetry (DSC) data and

RDA using FTIR data138

. The RDA model correctly assigned polyethylenes by 3 density

classes from DSC data (Figure 1.20.).

Figure 1.20. RDA misclassification plot of polyethylene samples by density from DSC data

138

Statheropoulos et al. observed a styrene-isoprene copolymer139

and cellulose thermal

decomposition124

by PCA and factor analysis from TG-MS data.

Instrumental techniques used in this project:

Thermogravimetric analysis (TGA); can show cellulose, hemicellulose and lignin

distribution in cellulosics140

. TGA uses small samples (~2 mg)141

and a controlled

temperature program. Mass is recorded continuously as a function of temperature as

the sample is heated142

. Sample mass is plotted against temperature to show the

temperature where mass loss occurs in the sample. In some cases the first or second

derivative is plotted to help pick a maximum point of mass loss. As cellulosics are


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made up of hydrocarbons they decompose by combustion with oxygen. TGA aims to

overcome this by use of an inert purge gas. Cellulosics will then decompose by

pyrolysis and will give more varied decomposition temperature. Perkin Elmer

manufactures TGAs with glass rods that connect the sample crucible to the balance.

Older models have platinum rods. Heating rate has been shown to effect accuracy of

cellulosic analysis by TGA143

. TGA provides a great deal of information, but is not

molecule specific. TGA is often used as a confirmatory technique for material

synthesis; such as polymers144-145

, catalysts145-146

and inorganic materials147-148

.

Fourier transform Infrared spectroscopy (FTIR); is a well used method for

measurement of atomic vibrations. Asymmetric vibrational modes give characteristic

absorbances in the IR spectrum. FTIR can detect subtle chemical changes and spectra

can contain a great deal of information.

Raman spectroscopy; excites atoms by laser and measures emitted inelastic light.

May be used as a confirmatory technique to FTIR as it gives more intense signals

from less polar bonds; the reverse of FTIR149

.

Wide angle X-ray scattering (WAXS); diffractograms give Bragg peaks, which each

correspond to a plane in a crystal unit cell150

. Known Bragg peaks can show

distribution of cellulose polymorphs and diffractograms may be used to calculate

crystal thickness70, 151

and crystallinity index of cellulose70

.

The chemistry behind spectroscopic observations reflects structural differences of cellulosics

on a molecular level. This may be a reflection of crystal alignment and level of crystallinity4.

Native cellulose is known to exist in two main crystal forms with most materials having a

combination of both152

. No peer reviewed research has used chemometrics from TGA to

classify cellulosics by source. It is an area worth exploring as thermal degradation

mechanisms of cellulosic components may be drawn from chemometric results. This project


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aims to build on knowledge of complex cellulosics chemistry by investigating how TGA,

FTIR and Raman can be used to elucidate information.

Chemometric techniques used in this project:

Principal components analysis (PCA); is a proven unsupervised pattern finding

technique153

that forms new artificial (latent) variables known as principal components

which are linear combinations of the original variables. These principal components

are formed from distance to repeated axes that lie along data means. Each successive

component accounts for as much variation as possible not accounted for by the

previous components124

. PCA reduces data but preserves essential information caused

by the main sources of data variability133

. It is suitable for large data sets and may give

groupings that would not otherwise appear.

Linear Discriminant Analysis (LDA); is a proven supervised technique133, 154

. Class

assignments are calculated from multidimensional scatter using the Mahalanobis

distance.

(1.1)

χ’s are column vectors of measurements on a single object, C is the variance-covariance

matrix whose elements represent the covariance between any two variables. k and l are the

distance between 2 samples. T is the transpose of a matrix. A straight line is drawn between

each cluster for class assignment. This assumes that each class is equally scattered with the

same covariance structure. Misclassification plots may be drawn to display the success of the

model in assigning classes.

Tlklk xxCxx ** 1


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2. Methodology

Sample collection and preparation:

Samples of Hemp (genus Cannabis), Jute (Corchorus olitorius), Flax (Linum usitatissimum),

Rayon, viscose (regenerated cellulose) and Avicel (purified wood fibre) were obtained from

the RMIT chemistry store. Samples of white and coloured cotton were provided by

Dr. Eliangela de Morais Teixeira from EMBRAPA Agricultural Instrumentation, São Carlos -

SP, Brazil. Samples of furniture wood offcuts were supplied by a local manufacturer in High

street, Preston; Pine (Pinus radiata), Blackwood (Acacia melanoxylon), Victorian Ash

(Eucalyptus regnans) and Jarrah (Eucalyptus marginata). Samples of Serrated Tussock

(Nassella trichotoma) were collected by myself and Associate Professor Barry Meehan from

Studley Park Reserve, Kew, Victoria.

Figure 2.1. Studley Park Reserve, Kew Figure 2.2. Serrated Tussock sample

Coconut (Cocos nucifera) husk fibres were collected from the outside of 2 coconuts and

banana fruit fibres from the top of a bunch of Cavendish bananas (Genus Musa). These were

purchased by myself from Queen Victoria market, Melbourne.

Hemp, Jute, Flax, rayon, viscose and Avicel samples from the store were sufficiently dry for

testing. Serrated Tussock samples were cut and oven dried at 100 ºC for 6 h. All moisture

appeared to have gone as green fibres turned a light brown.


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Figure 2.3. Dried Serrated Tussock

Banana fibres were cut and dried at 100 ºC for 6 h to produce dry, usable pieces (Figure 2.4.).

Figure 2.4. Banana fruit fibres

Coconut husk fibres were found to be particularly dry already so were placed in oven at 60 ºC

for only 2 h.

Figure 3.5. Coconut with husk fibres

Sample preparation for X-ray scattering involved cutting wood to ~1 mm thickness, 4 mm

height and 15 mm width.


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The 17 prepared samples were divided into 5 classes (Table 2.1.).

Table 2.1. Sample classes

Sample Class

Hemp Bast Jute Bast Flax Bast

Blackwood Wood Pine Wood

Victorian Ash Wood Jarrah Wood

White cotton Cotton Green cotton Cotton Ruby cotton Cotton

Brown cotton Cotton Rayon Regenerated cellulose

Viscose Regenerated cellulose Avicel Regenerated cellulose

Banana Other Coconut Other

Serrated Tussock Other

Instrumentation

TGA experiments were run using a Perkin Elmer Pyris 1 TGA. A 2 mg sample was prepared

wearing rubber gloves to prevent contamination. Upon loading a wait time of 30 min was

adhered to allow any moisture that could evaporate at room temperature to evaporate. Purge

gas of nitrogen was used from 30-700 °C and air from 700-830 °C. A heating rate of

10 °C·min-1

was used from 30-830 °C, followed by holding for 10 min at 830 °C to burn off

any residue. Each sample run took ~ 82 min. Total turnaround time per sample was ~ 3 h. If

care was not taken thermograms may have bumps from the bench, static charge build up on

rod or an incorrectly zeroed load if sample was not allowed long enough to evaporate. In the

best case 3-4 runs per day were achieved. Validation experiments were run on both

instruments to check for intraday drift (See figures 3.10. & 3.11.). Part way through this

project the Pyris 1 TGA unexpectedly failed. As Perkin Elmer were unable to repair it within

a reasonable time a new plan was made to use an older instrument; Perkin Elmer TGA7


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(Figure 2.6). This reduced the capacity to check sample variation over the duration of the

experiment.

Figure 2.6. Perkin Elmer TGA7

FTIR spectra were run with a Perkin Elmer Spectrum 100 FTIR Spectrometer. A diamond

crystal internal reflectance element sampler was first tried. This gave poor results so the

method was changed to diffuse reflectance sampling using KBr as a background medium and

to grind samples (Figure 2.7.).

Figure 2.7. KBr grinding of sample (a) and diffuse reflectance sampler (b)


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Raman experiments were attempted on a Perkin Elmer RamanStation 400F spectrometer

(Figure 2.8.).

Figure 2.8. Perkin Elmer RamanStation 400F spectrometer

Samples were placed in the laser area and aligned using a video camera. Full laser intensity

was used (100 mW) for 32 seconds, repeated 16 times.

WAXS diffractograms were carried out on native jarrah, mercerized cotton from a bath towel,

native flax and rayon. Experiments used a Bruker-Anton Paar Nanostar spectrometer.

Instrument was run with 35 mA and 40 kV. Sample pieces were taped to sample holder in

such a way that only the sample blocked the hole on the holder and left no part of the hole

uncovered (Fig 2.9.).


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Figure 2.9. Flax samples in Nanostar sample holder. Left sample is poorly prepared and X-rays don’t

scatter well

Other samples were attached to the sample holder in a similar way to the right hand side flax

in Figure 2.9.

The sample holder was then carefully placed into the instrument at a distance of 4.8 cm from

the detector (Figure 2.10.).

Figure 2.10. Bruker Nanostar spectrometer in 4.8 cm sample to detector mode


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Data treatment

Data treatment was aimed to preserve properties of the cellulose, rather than the properties of

the instrument. TGA spectra were collated into columns with temperature versus mass from

each sample. For further data treatment only values between 30 and 700 °C were used as the

temperature program changed purge gas at 700 °C so data above this would not reflect the

nature of the cellulose. It is known that carbohydrate mixtures give more information in TGA

spectra under an inert purge gas than an oxidative purge gas due to pyrolysis of the

components giving a staged and more thermally resistant response than combustion155

.

The TGA data files had 1421 data points and FTIR had 1801 data points. A Savitsky-Golay

five point second derivative moving convolution was applied to FTIR data (Equation 3.1)156

.

(2.1)156

Data obtained then underwent PCA using a multivariate analysis add-in for Excel which may

be downloaded from Richard Brereton’s website at the University of Bristol157

. From this

Principal Components 1-6 were transferred to SCAN software (MiniTab corporation, 1995)

and used for LDA.

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211222

2

7yyyyy

dd

iiiii

y


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3. Results and discussion

TGA

Thermograms appear similar to one another. White cotton has a similar decomposition trend

to Avicel. Coloured cottons appear similar to each other (Figure 3.1.). Differing levels of

residue are due to some samples being more tightly rolled into 2 mg balls than others.

Figure 3.1. TGA thermograms of cottons and Avicel

Figure 3.2. TGA thermograms of bast fibres and woods


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It is evident by comparison of Figure 3.1. and 3.2. that the more pure cellulose sources

(cottons/Avicel) have a more narrow mass loss trend than native cellulosics with more diverse

composition (Bast fibres/Woods). White cotton is ~ 88 % cellulose85

and Avicel is 99.9%

cellulose6 while other cellulosics only ~ 30-40 %

43. Woods and bast fibres contain a greater

degree of hemicellulose, pectins, lignin and tannins to give more spread out pyrolysis of

components. Decomposition temperatures of cellulosic components may overlap and

pyrolysis of one may effect that of another. This makes it difficult to separate mass loss by

cellulosic component.

Jarrah; a hardwood is high in lignin making it the most thermally resistant of the woods.

Lignin content is higher in more hard slow growing woods than in soft fast growers5.

Figure 3.3. TGA thermogram class comparison

Figure 3.3. compares thermal decomposition profiles of the five classes of cellulosics

collected in this project. Although there are some differences it is difficult to make any

definitive claims about which profile would belong to which class. Other thermograms are

included in Appendix A.


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FTIR

FTIR spectra obtained have some peaks in common but some different due to presence of

cellulosic components. Aromatic methyl group vibrations96

occur at ~ 1462 cm-1

. Aromatic

skeletal vibrations96

give peaks at 1600, 1508 & 1422 cm-1

. Bast fibres (Figure 3.4.), coconut

(Figure 3.5.), woods and others (Appendix B) show intense peaks in the region 1745-

1620 cm-1

from carbonyl stretching18

which indicate a carbonyl ester in pectin. Jarrah has a

much weaker peak at 1687 cm-1

compared to other woods. As Jarrah is a hardwood this may

be due to enhanced lignin over time of growth. Jarrah gives intense peaks at 1508 and

1606 cm-1

from aromatic skeletal vibrations of lignin. Similar peaks exist for woods, bast

fibres, serrated tussock and coconut. The region 1150-1060 cm-1

has activity in all samples

which may be attributed to ether C-O-C stretching in cellulose158

. Bands in the

region 1318-1329 cm-1

may be attributed to C-CH2 wag106

which is present in all cellulosics.

Further study of peak locations in this region may show correlation with certain components,

the presence of which effects the electronic environment of the C-CH2 wag.

Figure 3.4. FTIR spectrum of jute


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Figure 3.5. FTIR spectrum of coconut fibre

Figure 3.6. FTIR spectrum of Avicel

Figure 3.6. of Avicel (purified cellulose) shows the absence of peaks in the region

1745-1620 cm-1

(pectin) and 1600 & 1508 (lignin). Note: Figures 3.4.-3.6. received a baseline

correction from instrument software for viewing purposes. Table 3.1 provides a summary of

cellulosic peak assignments.


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Table 3.1. Peak assignments for FTIR of cellulosics

Region cm-1 Asymmetric vibrational mode Cellulosic component

1745-1620 Carbonyl C=O stretching Pectin 1600, 1508, 1422 Aromatic skeletal vibrations Lignin 1150-1060 Ether C-O-C stretching Cellulose/Hemicellulose ~ 1320 C-CH2 wag All

While there is some measure of discrimination for cellulosics by FTIR it is not a simple case.

For example peaks at 1462 cm-1

are caused by aromatic methyl group vibrations present in

cellulose, hemicellulose, pectin and lignin. Chemometric method development is aimed to

discriminate between such overlap.

Raman

Conditions for Raman analysis of cellulosics turned out to be complex. Cellulosics, especially

the lignin, give a lot of interference due to fluorescence. One example159

used 400 scans each

of 12 min laser time per sample to get usable spectra on an instrument with 300-500 mW laser

power output (ours only 100 mW). This instrument was also reconfigured to give more laser

time at 1064 nm wavelength, which has been shown to be a wavelength at which lignin

fluoresces least and so causes less interference and gives better signal to noise ratio136

. The

RMIT instrument (Perkin Elmer RamanStation 400F spectrometer) only focuses the laser

point to 100 microns but others focus to 1 micron. Spectra run during this project under the

conditions outlined on page 30 gave poor signal to noise ratio and very limited peak

resolution (Figure 3.7.). As this data was ill conditioned for Chemometrics the use of Raman

was discontinued in favour of TGA and FTIR.


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Figure 3.7. Raman spectra of hemp

High band density is expected for bast fibres in the region 950-1180 cm-1

due to stretching160

.

HCC and HCO bending is expected to give intense peaks160

in the region 1337-1407 cm-1

. All

cellulosics are expected to give strong Raman bands in the 890-820 cm-1

due to cellulose158

.

These should correlate with FTIR bands160

. A broadening of signal peaks is expected with

conversion of cellulose I → cellulose II113

. Raman spectra of cellulose can prepared for

chemometric analysis with more vigorous continuation of this work.

WAXS

Obtained 2D WAXS diffractograms show a distinct difference between the 4 samples

(Figure 3.8.). Native Jarrah appears to contain cellulose I but may contain cellulose II and

some amorphous cellulose which scatters randomly. Mercerized cotton showed the highest

degree of order in its crystalline structure by more defined peak maxima.


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Figure 3.8. 2D WAXS diffractograms of native jarrah (a), mercerized cotton (b), native

flax (showing fibre orientation) (c) and rayon (d)

Distribution of cellulose I and cellulose II crystals may be determined from WAXS

diffractograms. Cellulose I gives peaks at 14.8, 16.3 and 22.6 2θ angle, while cellulose II

gives peaks at 12.1, 19.8 and 22.0 2θ angle14

(Table 3.2).

Table 3.2. X-ray diffraction peak positions and diffraction planes of cellulose polymorphs14


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As each crystal plane only gives one scattering angle peak the three peaks from each of

Cellulose I and II respectively are produced by diffraction at three planes in that unit cell.

Native Jarrah appears to have peaks at 16.3 and 22.6 (Figure 3.9.a) which suggest a high level

of cellulose I.

Figure 3.9. Integrated 1D WAXS diffractograms of native jarrah (a), mercerized cotton (b), native flax (c)

and rayon (d)

A more comprehensive study of cellulosics diffractograms would reveal exact distribution of

cellulose polymorphs, level of crystallinity, level of mercerization/solvent treatment and fibril

thickness/distance between crystals from SAXS.


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Chemometrics

TGA spectra were carried out over several months. The same sample of flax was repeated to

measure drift with both Pyris 1 TGA (Figure 3.10.) and TGA7 (Figure 3.11). Pyris 1 TGA

had greater precision compared to TGA 7.

Figure 3.10. Intra-Day variance of flax; Pyris 1 TGA

Figure 3.11. Intra-Day variance of flax; TGA7


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FTIR

A plot of standard deviation of samples at each wavenumber was drawn (Figure 3.12.). This is

used to show regions which have higher variability and hence may be better for

discrimination of classes. However variability was shown across the whole range and no

regions seemed to be preferable for discrimination.

Figure 3.12. Standard deviation of FTIR sample spectra

FTIR baselines can vary even for similar samples (Figure 3.13.) which must be addressed by

the use of derivatized data for Chemometrics.

Figure 3.13. FTIR of all 4 cotton samples showing baseline drift


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PCA

PCA was carried out on untreated TGA data. Use of Equation 2.1 removes baseline drift and

scattering factors from FTIR data and accentuates differences between samples that are very

similar. This type of calculation is also used by some instrument software.

Figure 3.14. Score plot of PC1 vs PC2 from TGA data

Figure 3.14. shows sample classes to be somewhat grouped together. This may be due to

classes having components such as lignin, pectin or hemicellulose or absence of one of these.

Table 3.3. Eigenvalues and % variance of Principal Components from second derivative TGA(a) and

FTIR(b) data

Each consecutive PC includes the maximum possible variance. Percent variance decreases

with each consecutive PC (Table 3.3.).


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Figure 3.15. Score plot of cellulosics PC1 vs PC2 (second derivative FTIR data)

Score plots show useful sample classification from TGA and FTIR data. Figure 3.15. shows

cottons, woods and other to have grouped well.

Tables 3.3. shows percent variance at PC6 is still 4.20 for FTIR while for TGA it is only 0.68.

This means that more differentiating power is contained in each consecutive PC from FTIR

than TGA. We would expect this to make the LDA model more effective for FTIR data than

TGA data.

LDA

Linear Discriminant Analysis (LDA) is a supervised Chemometric method for differentiating

samples by the way their data clusters. Using PCA as a data reduction technique, Principal

Components are analysed by LDA. The LDA algorithm (Equation 1.1.) uses Mahalanobis

distance for assignment of sample PC clusters. A straight line is drawn mathematically

between the clusters, which in this case are in 6 dimensions (as PCs 1-6 were used).

LDA centroid coordinates of PC1-6 become closer more so for TGA data than FTIR.


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Figure 3.16. LDA class centroid profiles from TGA data

Figures 3.16. and 3.17. show the Mahalanobis distances decreasing in each consecutive set of

PCs from FTIR meaning less discriminating power from later PCs. This trend is expected

when using PCs for LDA modelling. There continues to be greater discriminating power in

PCs 2-5 from FTIR compared with that from TGA data (Figure 3.16.).

Figure 3.17. LDA class centroid profiles PC1-6 from FTIR

LDA sample classification correctly predicts 15 out of 17 sample classes using PC 1-6 from

TGA data (Figure 3.18.).


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Figure 3.18. LDA class assignments from PC1-6 of TGA data

Figure 3.19. LDA cross validated class assignments from PC1-6 of TGA data

Figure 3.19. shows cross validation by a leave out one method from each group still gives 10

out of 17 correct sample classes (47 %). The displayed error is the portion of samples

assigned incorrectly.


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Figure 3.20. LDA class assignments from PC1-6 of FTIR data

LDA sample classification correctly predicts 15 out of 17 sample classes using PC 1-6 from

TGA data (Figure 3.20.).

Figure 3.21. LDA cross validated class assignments from PC1-6 of FTIR data


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Cross validated class assignments (Figures 3.19. and 3.21.) use a leave one-out method where

a sample is omitted and a model developed on all the other samples. This sample is then

assigned to a class. This is repeated for all samples in turn. Classes have only 3 or 4 samples

so the leave out one method leaves only 2 or 3 samples. This makes cross validation of the

model difficult. PC’s 1-6 from both TGA and FTIR data still give 10 out of 17 correct sample

classes (59 %) from data using cross-validation. This is still good as there is only ~ 20 %

chance of randomly predicting the correct class.

Dendrograms were also constructed to see whether or not results gave any unexpected trends;

however these did not show any meaningful groupings.


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Conclusion

Cellulosics were analysed by TGA, FTIR and WAXS to characterize crystallinity, cellulose

polymorph and fibre orientation. TGA thermograms of varied cellulosic source have been

compared and contrasted to show the effect of chemical composition on pyrolysis behaviour.

Observations from TGA have been contrasted with FTIR band assignments. WAXS was used

to compare samples of varied crystallinity and cellulose polymorph.

Results from PCA of TGA and FTIR spectra were visualized to understand the clustering of

collected cellulosics. A good level of class differentiation by PCA and LDA was achieved.

Raman spectroscopy was attempted but a deeper approach is needed for usable spectra of

cellulosics.

Linear Discriminant Analysis of TGA and FTIR data both correctly differentiated 15 of 17

samples classed by source of cellulosic. Cross validated LDA class assignments were reduced

to 10 out of 17 for both TGA and FTIR data; likely due to small numbers of samples in

classes (3-4). However, despite the small number of samples, this study showed that useful

information may be obtained by this approach. A more comprehensive investigation is

expected to reveal useful methods for cellulosics analysis. This may involve a larger number

of samples from each class and/or analysis of classes separately. For example the same study

could be carried out with 8-10 samples of each of the five classes. Or the study could be

repeated but focus solely on wood varieties. Data collection rate for TGA of 20 temperature

measurements per minute could be reassessed. The same study could be tried with 5, 10 or 30

measurements per minute.


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Appendices

Appendix A: Thermograms


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Appendix B: IR spectra


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classification of cellulosic materials by … · sems of fine and coarse flax fibers 19 ... ftir...

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