photocatalytic reactor for dye …...politecnico di milano department of chemistry, material and...
TRANSCRIPT
POLITECNICO DI MILANO
Department of Chemistry, Material and Chemical Engineering
“Giulio Natta”
Materials engineering and Nanotechnology Master degree
PHOTOCATALYTIC REACTOR FOR DYE DEGRADATION WITH TIO2 NANOTUBES
Supervisor: Professor M.P. Pedeferri
Co-advisors: Professor M.V. Diamanti
Dr. B.E. Sanabria Arenas
Master degree thesis of:
Luca Casanova Matr. 864768
Alessandro Marcolin Matr. 854337
Academic year 2016/2017
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INDEX
1 TITANIUM and TITANIUM DIOXIDE ................................................................ 1
1.1 GENERAL OVERVIEW ....................................................................................... 1 1.2 INTRODUCTION TO PHOTOCATALYSIS ......................................................... 4
1.2.1 PHOTOCATALYTIC PROCESS ................................................................... 4 1.2.2 FACTORS INFLUENCING PHOTOCATALYTIC ACTIVITY .................... 8 1.2.3 TIME SCALE APPROACH ......................................................................... 11 1.2.4 CHEMICAL APPROACH ........................................................................... 12
1.3 PHOTOCATALYST MORPHOLOGY ................................................................ 13 1.3.1 SUSPENDED NANOPARTICLES .............................................................. 13 1.3.2 IMMOBILIZED NANOPARTICLES AND NANOSTRUCTURES ............... 14
1.4 PRODUCTION .................................................................................................... 15 1.4.1 ANODIZING ............................................................................................... 18 1.4.2 ADVANCED STRUCTURES ...................................................................... 31
1.5 APPLICATION OF NANOTUBES IN THE PHOTOCATALYTIC PROCESS .... 33 1.5.1 NANOTUBES DIMENSION AND SHAPE ................................................. 33 1.5.2 SURFACE PREPARATION ........................................................................ 34 1.5.3 ANNEALING .............................................................................................. 34 1.5.4 ENVIROMENTAL CONDITIONS .............................................................. 37
2 CHEMICAL REACTOR DESIGN ....................................................................... 39
2.1 APPLICATION OF MOLAR BALANCE TO REACTORS ................................. 41 2.1.1 BATCH REACTOR ..................................................................................... 41 2.1.2 CONTINUOUS STIRRED TANK REACTOR ............................................ 44 2.1.3 PLUG FLOW REACTOR (PFR) .................................................................. 45
2.2 PHOTOCHEMICAL REACTORS ......................................................................... 46 2.1.4 FLAT-PLATE WITH IMMOBILIZED CATALYST PHOTOREACTOR ... 46 2.1.5 SLURRY PHOTOREACTORS .................................................................... 47
2.2 PHOTOREACTOR MODELLING ...................................................................... 48 2.2.1 RADIATIVE MODEL ................................................................................. 49 2.2.2 PHYSICS BEHIND “FRED OPTICAL ENGINEERING” SOFTWARE ..... 52
3 METHODOLOGY................................................................................................. 57 3.1 SAMPLE PREPARATION .................................................................................. 57
3.1.1 POLISHING AND CLEANING ................................................................... 57 3.1.2 ANODIZING ............................................................................................... 58 3.1.3 ANNEALING .............................................................................................. 59
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3.2 PHOTOCATALYTIC REACTORS ..................................................................... 59
3.3 PHOTOCATALYTIC ACTIVITY ....................................................................... 62 3.3.1 EFFECT OF LIGHT SOURCE-SAMPLE DISTANCE ................................ 65 3.3.2 EFFECT OF STIRRING .............................................................................. 65 3.3.3 EFFECT OF POLLUTANTS ....................................................................... 65
3.4 DESIGN OF THE RADIATIVE MODEL USING “FRED OPTICAL
ENGINEERING” SOFTWARE ................................................................................... 66 3.4.1 OPTICAL SOURCE .................................................................................... 67 3.4.2 GEOMETRY ............................................................................................... 70 3.4.3 ANALYSIS SURFACE................................................................................ 70 3.4.4 MATERIALS ............................................................................................... 71 3.4.5 COATINGS ................................................................................................. 75 3.4.6 RAYTRACE PROPERTIES......................................................................... 77 3.4.7 ANALYSIS COMMANDS .......................................................................... 78
4 RESULTS ............................................................................................................... 79
4.1 OPTIMIZATION OF ANNEALING PARAMETERS .......................................... 83 4.2 DISTANCE EFFECT ........................................................................................... 85
4.3 STIRRING EFFECT ............................................................................................ 87 4.4 REUSE OF SAMPLES AND REPEATABILITY OF TEST ................................. 89 4.5 EFFECT OF SOLUTION COMPOSITION .......................................................... 90
4.6 FRED SIMULATION RESULTS ......................................................................... 98 4.7 FRED RESULTS VALIDATION ....................................................................... 103
5 CONCLUSION .................................................................................................... 109
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FIGURE LIST Figure 1.1 TiO2 crystal structure of rutile (a), anatase (b) and brookite (c)(10). ................... 2 Figure 1.2 Molecular orbital diagram of TiO2.11 ................................................................ 3 Figure 1.3 Photo-induced formation mecvhanism of electron-hole pair in a semiconductor TiO2 particle with the presence of water pollutant (P).17 ..................................................... 5 Figure 1.4 Relative positions of various redox couples and work functions of various metals relative to the band edges of TiO2.13 ........................................................................ 9 Figure 1.5 Possible relative bands position for anatase and rutile.18 ................................. 11 Figure 1.6 Overview of different morphology and properties of TiO2 nanotubes synthesized by different methods.46 .................................................................................. 17 Figure 1.7 General scheme of anodization process. ......................................................... 18 Figure 1.8 Map of anodising techniques as a function of applied voltages, and related range of oxide thickness.1 ................................................................................................ 20 Figure 1.9 Effect of fluorides containing electrolytes on formed TiO2 layer.54 ................. 21 Figure 1.10 Schematic representation of the Ti anodization (a) in absence of fluorides (results in flat layer), and (b) in presence of fluorides (results in the tube growth).54 ......... 22 Figure 1.11 Schematic representation of the TiO2 nanotube array formation: (a) compact layer growth to the maximun thickness, (b) breakdown of the barrier layer film, (c) growth of the pores resulting in a worm-like structure, (d) more ordered structure partially covered with the thin layer, and (e) regular self-organized porous TiO2 structure.56 ....................... 23 Figure 1.12 a) Current-time curve and b) respective growth stages during nanotubes formation.54 ..................................................................................................................... 24 Figure 1.13 SEM images showing TiO2 nanotube layers grown by different anodization processes of Ti. (A) Typical morphology obtained in acidic fluoride or HF electrolytes, (B) glycerol/fluoride electrolytes, (C) ethylene glycol/fluoride electrolytes. The insets show top-views (open tubes), bottom views (closed ends) and side walls in detail.54 ................. 25 Figure 1.14 TiO2 nanotube-layer thickness with anodization time for different electrolytes (anodization voltage for ethylene glycol electrolyte held at 60 V, and 40 V for other electrolytes): ■ water-based acidic, ▲ water based neutral, ☐ glycerol, ￮ glycerol/H2O 50:50, * ethylene glycol.38 .............................................................................................. 26 Figure 1.15 (Left) Effect of the relative field strength En-Eo (n=0~6) on nanotube length and pore diameter.57 (Right) Voltage dependence of the tube diameter for different electrolytes: ￮ water-based, ▼ glycerol/H2O 50:50, ☐ glycerol, ■ ethylene glycol.38 ........ 27 Figure 1.16 The tube length as a function of anodization time obtained directly from cross-section measurements of samples formed during different times in glycerol +0.5 wt.% NH4F and 1M (NH4)2SO4 + 0.5 wt.% NH4F at 20 V at 20°C.58 ........................................ 28 Figure 1.17 A (a) Schematic representation of the dissolution reactions and mechanisms; (b) the pH profile within a pore; (c) the dissolution-rate profile within a pore wall. B Experimental determination of the dissolution rate, Rdiss, of the anodic TiO2 depending on the pH value results are taken from XPS sputter profiles of 20 V anodic oxide immersed
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for different times in 1M (NH4)2SO4 + 0.5 wt.% NH4F solution with different pH values.61
........................................................................................................................................ 29 Figure 1.18 An evaluation of the tube diameter and length as a function of the temperature for glycerol electrolytes. The average tube diameters are 40±5, 50±6 and 60±7 nm and lengths are 800±0 nm, 2.2±0.1 µm and 3.4±0.1 µm for 0, 20 and 40°C.58 ........................ 30 Figure 1.19 Effect of aging on the conductivity of an organic electrolyte and length of the grown nanotubes.51 .......................................................................................................... 30 Figure 1.20 SEM and STEM imagines of advanced TiO2 morphologiesc: (a) on the top tube-stacks and branching of tubes, in the middle bamboo type nanotubes and at the bottom TiO2 nanolace. (b) SEM image showing TiO2 membrane open on top and bottom, optical image of a membrane and a scheme of nanotubular flow through photocatalytic membrane. (c) 'Superlattice' structures of a TiO2 /Ta2O5 nanotube arrays from a Ti/Ta multilayer substrate. 43 ..................................................................................................... 32 Figure 1.21 Degradation curves of AO7 for latest generation of TiO2 nanotubular arrays grown in ethylene glycol (EG) based electrolyte with different tube length.43................... 33 Figure 1.22 X-Ray diffraction patterns for TiO2 nanotubular arrays grown on Ti metal (15 µm long) at room temperature and annealed at different temperatures 350°C, 500°C, 700°C, and 900°C.43 ........................................................................................................ 35 Figure 1.23 SEM image of rutile layer formed underneath the nanotubes after annealing at temperature higher than 500°C in O2 containing atmosphere.43 ........................................ 36 Figure 1.24 Influence of annealing temperature (and atmosphere) on the photocatalytic degradation of AO7 using TiO2 nanotubes of thickness ∼ 1.5 µm.43 ................................. 36 Figure 1.25 Photocatalytic decomposition rates of AO7 for immobilized nanotubes.39 .... 37
Figure 2.1 A) Batch reactor, B) CSTR, C) PFR ............................................................... 39 Figure 2.2 Schematic representation of a generic reactor. ................................................ 40 Figure 2.3 Batch reactor. ................................................................................................. 41 Figure 2.4 Exponential decay of A concentration at different k values.73 ......................... 43 Figure 2.5 Concentration evolution in time in a semi-log plot.73 ...................................... 43 Figure 2.6 (Left) CSTR, (Right) CSTR scheme ............................................................... 44 Figure 2.7 Plug Flow Reactor .......................................................................................... 45 Figure 2.8 PFR scheme. .................................................................................................. 46 Figure 2.9 Flat plate reactor.74 ......................................................................................... 47 Figure 2.10 Slurry photoreactor....................................................................................... 47 Figure 2.11 UV-VIS spectrophotometer apparatus scheme. ............................................. 50 Figure 2.12 LVRPA distribution along the reactor cross section and axis. a) LVRPA distribution over a reactor cross section; b) LVRPA distribution along the reactor axis. ... 52 Figure 2.13 Gaussian beam decomposition.76 .................................................................. 53 Figure 2.14 Gaussian beam.76 .......................................................................................... 54
Figure 3.1 Sample pictures: a) before cleaning; b) after polishing; c) after sonication. ..... 58 Figure 3.2 Anodized and annealed 3 cm x 3 cm sample. .................................................. 59
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Figure 3.3 Emitting spectrum of OSRAM lamp............................................................... 59 Figure 3.4 Images of reactors at the beginning (left) and at the end (right) of the photodegradation test with AO7. ...................................................................................... 60 Figure 3.5 Resonant forms of AO7.79 .............................................................................. 62 Figure 3.6 Calibration curve of AO7. .............................................................................. 63 Figure 3.7 Absorbance spectrum of AO7.81 ..................................................................... 64 Figure 3.8 Tree folder menù of FRED software. .............................................................. 67 Figure 3.9 CAD of the light source NICHIA NCSU033B................................................ 67 Figure 3.10 FRED window dialog for optical source definition. ...................................... 68 Figure 3.11 Lambertian emission of the UV LED NICHIA NCSU033B.82 ...................... 68 Figure 3.12 Final aspect of the light source considering dynamic generation of rays. ...... 69 Figure 3.13 Spectrum of the UV LED NICHIA NCSU033B. .......................................... 69 Figure 3.14 Technical drawing of the beaker Schott Duran 100 ml. ................................. 70 Figure 3.15 Sizing of the analysis surface........................................................................ 71 Figure 3.16 Material creation window. ............................................................................ 71 Figure 3.17 Rutile real refractive index n versus lambda. ................................................ 72 Figure 3.18 Rutile extinction coefficient κ versus lambda. .............................................. 72 Figure 3.19 TiO2 nanotubes refractive index vs λ.84......................................................... 73 Figure 3.20 TiO2 nanotubes absorbance vs λ, where open side refers to the mouth.85....... 73 Figure 3.21 Refractometer Mettler Toledo Refracto 30PX............................................... 74 Figure 3.22 Pyrex coating designed as a “general sampled coating” command. ............... 75 Figure 3.23 Trasmittance of Pyrex glass. ......................................................................... 76 Figure 3.24 Reflectance curve of air-solution interface. ................................................... 76 Figure 3.25 Thin film layered coating command window. ............................................... 77
Figure 4.1 SEM image of the titanium dioxide sample used during photodegradation experiments ..................................................................................................................... 79 Figure 4.2 XRD results of the titanium dioxide sample ................................................... 79 Figure 4.3 Typical trend of absorbance during the photodegradation test. ........................ 81 Figure 4.4 Typical photodegradation test curve for AO7. ................................................ 82 Figure 4.5 Degradation curves of AO7 for samples annealed at 450°C; 1 hour and 2 hours. ........................................................................................................................................ 83 Figure 4.6 Degradation curve of AO7 for samples annealed at 500°C; 1 hour and 2 hours. ........................................................................................................................................ 84 Figure 4.7 influence of annealing parameters on apparent rate constants. ........................ 84 Figure 4.8 Degradation curves of AO7 for different source-sample distances. ................. 85 Figure 4.9 Effect of source-sample distance variation on the apparent rate constant. ....... 86 Figure 4.10 Irradiance vs sample-UV source distance ..................................................... 86 Figure 4.11 Degradation curves of AO7 in static, laminar and turbulent flow conditions. 87 Figure 4.12 Influence of stirring power on apparent rate constants. ................................. 88 Figure 4.13 Degradation curves of AO7 for the same sample subjected to 5 consecutive photodegradation tests. .................................................................................................... 89
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Figure 4.14 Effect of NaCl on degradation curves of AO7. ............................................. 91 Figure 4.15 apparent rate constants at different NaCl concentration. ............................... 91 Figure 4.16 Effect of NaHCO3 on degradation curves of AO7. ........................................ 92 Figure 4.17 apparent rate constants at different NaHCO3 concentration. .......................... 92 Figure 4.18 Effect of NaH2PO4 on degradation curves of AO7. ....................................... 93 Figure 4.19 apparent rate constants at different NaH2PO4 concentration. ......................... 93 Figure 4.20 Effect of NaNO3 on degradation curves of AO7. .......................................... 94 Figure 4.21 apparent rate constants at different NaH2PO4 concentration. ......................... 94 Figure 4.22 Effect of Na2SO4 on degradation curves of AO7........................................... 95 Figure 4.23 apparent rate constants at different Na2SO4 concentration. ............................ 95 Figure 4.24 Effect of CaCl2 on degradation curves of AO7. ............................................ 96 Figure 4.25 apparent rate constants at different CaCl2 concentration. .............................. 96 Figure 4.26 Effect of H2O2 on degradation curves of AO7. ............................................. 97 Figure 4.27 apparent rate constants at different H2O2 concentration. ................................ 97 Figure 4.28 Graphical representation of the simulation (the solution in orange, rays in yellow) ............................................................................................................................ 98 Figure 4.29 3-D Irradiance spread function evaluated on the analysis surface coinciding with the catalyst surface ................................................................................................... 99 Figure 4.30 2-D representation of the irradiance spread function ..................................... 99 Figure 4.31 Irradiance profile with respect to a section parallel to the x axis ................... 99 Figure 4.32 Irradiance profile with respect to a section parallel to the y axis ................. 100 Figure 4.33 Statistical report of the irradiance spread function ...................................... 100 Figure 4.34 3-D intensity distribution over the catalyst surface ..................................... 101 Figure 4.35 2-D intensity distribution over the catalyst surface ..................................... 101 Figure 4.36 Example of position spot diagram .............................................................. 102 Figure 4.37 Raytrace summary ...................................................................................... 102 Figure 4.38 Matlab code to define the ode describing the reaction ................................. 104 Figure 4.39 Second block of matlab code called “mySim” ............................................ 104 Figure 4.40 Call of the “mySim” function from the command window ......................... 105 Figure 4.41 Experimental data (red circles) vs simulation trend (blu line) with k0=0,06 on the right and k0=0,07 on the left. Note the better fitting using 0.07. ................................ 105 Figure 4.42 Objective function definition ...................................................................... 106 Figure 4.43 Script for the creation of a confidence interval where the k parameter will be estimated ....................................................................................................................... 106 Figure 4.44 Script containing the functions fminsearch and fmincon used to minimize the function “myObj” .......................................................................................................... 107 Figure 4.45 Final result of the matlab simulation printed on command window where k is evaluated with both fminsearch and fmincon Matlab functions. ..................................... 107 Figure 4.46 Final plot experimental data (in red) and simulated profile (in blue) ........... 108 Figure 4.47 Final result of the simulation ...................................................................... 108
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TABLE LIST Table 1 LED absolute maximum ratings (T = 25°C). ..………………………………..… 61 Table 2 LED initial electrical/optical characteristics (T = 25°C). ……………………….. 61
Table 3 List of the studied pollutants, their concentrations and pH. ……………………... 66 Table 4 Absorbance values for different solutions at different time for adsorption tests in dark. ...……………………………………………………………………………………. 80
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ABSTRACT
Projections made by “THE WORLD BANK” database estimate in 2050 an overall world
population of about 9,750,357,000 unities, leading to an exponential increase of pollutants
and waste produced during everyday life. Researchers are very active in the development of
new technologies allowing the regeneration of clean air and potable water, avoiding or
eliminating dangerous byproducts and reducing energy consumption. In this frame, a
promising technique is photocatalysis, i.e., the decomposition of harmful compounds in
water or in air through the light-driven activation of a semiconductor material, which acts as
catalyst. Among all the possible materials, the most interesting one, for its availability and
intrinsic properties, is titanium dioxide. Since the possibility of fabricating nanostructures
increases the catalysts performances by providing larger specific surface area, and thus
higher number of active sites for pollutants degradation, in the present thesis work the
photocatalytic degradation of AO7, an organic dye, was carried out on TiO2 nanotubes,
which represent a good compromise among light harvesting capability, charge carriers
mobility and specific surface area. Anodic oxidation was chosen as production technique, in
order to allow the direct formation of nanotubes on a substrate, therefore avoiding a
subsequent passage of catalyst immobilization or, more onerous, of catalyst recovery from
the fluid.
Chapter 1 introduces the subject of photocatalysis, showing the role of nanostructuring and
how nanotubes are produced, in particular exploiting anodizing in organic electrolytes as in
the case of this experimental work.
The importance of controlling the reaction environment puts the basis of chapter 2, in which
several photoreactors are analyzed. The final choice was directed towards a flat bed batch
photoreactor with immobilized catalyst, offering a more scalable and simple template for
experimental tests. A key task in photoreactor design is the correct modelling of the radiative
path that light cross once emitted from the light source and hits the catalyst. For the
development of such model we used an optical engineering software called “FRED”, which
allows understanding the irradiance profile of a given light source over the active catalyst
surface in a determined reactor configuration: the software is also introduced and described
in Chapter 2.
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Chapter 3 treats in details the experimental methodology adopted: the preparation of the
samples, the fabrication of reactors and the photocatalysis tests, performed by following dye
discoloration through absorbance variations. Absorbance data are then converted into dye
concentration by using the absorbance-concentration linear correlation experimentally
observed. The reactor optimizations investigated span from the choice of catalyst annealing
conditions to enhance its reactivity, to the variation of the light source position inside the
reactor and the effects of solution composition, i.e., presence of different salts typical of
wastewaters, on the dye degradation rate.
All the results of the abovementioned experimental investigations are collected in chapter 4,
which also contains the irradiation simulation results generated by FRED, i.e. chart and
statistical data. As a further proof of the validity of the radiative simulation, we have
developed a kinetic model of the photodegradation process in Matlab environment, obtaining
a good correlation with experimental data.
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ABSTRACT
Proiezioni riferite all’anno 2050 effettuate dal database “The World Bank” stimano una
popolazione mondiale di circa 9,750,357,000 unità. Di conseguenza l’inquinamento ed i
rifiuti prodotti durante la vita quotidiana aumenteranno esponenzialmente. I ricercatori sono
molti attivi nello sviluppo di nuove tecnologie rivolte alla purificazione dell’aria e alla
potabilizzazione dell’acqua senza pagare il caro prezzo della generazione di prodotti di
lavorazione nocivi o elevati consumi energetici. La tecnologia più promettente è sicuramente
la fotocatalisi, ovvero un processo chimico attraverso il quale si verifica la decomposizione
di sostanze nocive presenti nell’acqua o nell’aria grazie alla stimolazione luminosa di un
semiconduttore che agisce da catalizzatore. Fra tutti i possibili materiali semiconduttori il
più interessante per fruizione e proprietà intrinseche è il biossido di titanio. Dato che la
possibilità di utilizzare nanostrutture incrementa le prestazioni del catalizzatore aumentando
la sua superficie specifica e di conseguenza il numero di siti attivi per la degradazione degli
inquinanti, nel presente progetto di tesi la fotodegradazione di un colorante organico (AO7)
è stata effettuata scegliendo i nanotubi di TiO2, permettendo un buon compromesso fra
capacità di raccolta della radiazione luminosa nonché il trasporto dei portatori di carica e
porosità superficiale.
Il capitolo 1 introduce il lettore nell’argomento della fotocatalisi, mostrando il ruolo delle
nanostrutture e di come i nanotubi sono stati prodotti, ovvero sfruttando l’anodizzazione in
un elettrolita organico.
Nel capitolo 2 viene esplicata l’importanza di un adeguato controllo dell’ambiente di
reazione, reso possibile dall’uso di opportuni reattori. La scelta del reattore è ricaduta su un
fotoreattore a letto piano con catalizzatore immobilizzato, permettendo una buona scalabilità
e un semplice utilizzo durante la procedura sperimentale. Un aspetto fondamentale nel
design di un fotoreattore è la corretta modellazione del percorso che la luce effettua partendo
dalla fonte emettente, arrivando sulla superficie attiva del catalizzatore. Per lo sviluppo di
questo modello è stato utilizzato un software rivolto all’ingegneria ottica chiamato “FRED”,
che permette di determinare la distribuzione dell’irradianza sulla superficie del catalizzatore.
Il capitolo 3 tratta in dettaglio le metodologie sperimentali adottate, descrivendo la
preparazione dei campioni, la fabbricazione di due fotoreattori e i test di fotocatalisi, in cui
la degradazione del colorante è studiata attraverso la variazione di assorbanza della
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soluzione. I valori di assorbanza ottenuti sono poi convertiti in concentrazioni residue
sfruttando la correlazione lineare tra assorbanza e concentrazione ottenuta
sperimentalmente. Vari test finalizzati ad ottimizzare i reattori sono stati effettuati, tra cui la
scelta delle migliori condizioni di rinvenimento per i campioni prodotti, la variazione della
posizione della fonte luminosa e l’effetto di diversi ioni introdotti nella soluzione usata
nell’esperimento di fotocatalisi.
Tutti i risultati sono stati elencati nel capitolo 4, contenente anche i report grafici e statistici
generati dal software FRED. Come ulteriore prova della qualità del modello radiativo e della
cinetica utilizzata, è stato sviluppato in ambiente Matlab un modello cinetico del processo
di fotodegradazione, ottenendo una buona correlazione con i dati sperimentali.
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1 TITANIUM and TITANIUM DIOXIDE 1.1 GENERAL OVERVIEW Titanium is one of the most abundant elements on our planet and nowadays it is used in
several fields; such as for aerospace, automotive and biomedical components, chemical and
oil industry and architecture.
The properties that allow such an extremely wide range of applications are high mechanical
resistance combined with low density, low thermal conductivity and expansion coefficient,
exceptional corrosion resistance and biocompatibility.1
However, today the 90% of titanium-containing minerals are consumed to produce titanium
dioxide.2
TiO2 belongs to the family of transition metal oxides, it exists mainly in three different
crystalline forms: rutile (tetragonal), anatase (tetragonal) and brookite (orthorombic).
The first one is the most stable form and therefore it is the most common one, whereas
anatase and brookite are metastable; anatase turns into rutile when heated above a critical
temperature that varies in the range 400°C-1200°C depending on the grain size, ambient
conditions, and impurities.3,4
However, for nanoscale materials, a large number of experimental and theoretical
investigations conclude that at crystallite sizes smaller than approx. 10–30 nm, anatase
becomes the most stable phase.5
Rutile is widely used as white pigment in different fields, from construction to food
industries.
Anatase is preferred for all the applications related to photocatalytic activity, gas sensing,
and solar cells, due to its higher electron mobility and catalytic properties compared to the
other TiO2 phases. It is also used in corrosion-protective coatings and in bioengineering,
improving biocompatibility of bone implants. These aspects will be discussed in more detail
in the following paragraphs.
In the last years, TiO2 has become the most studied photocatalyst because of its high
efficiency, non-toxicity, chemical and biological stability, and low cost.2,3,6,7
Brookite is very difficult to be produced and rarely it is obtained as a pure phase, although
in recent years the interest in this structure type has increased and pure brookite has
demonstrated to be an interesting candidate in photocatalytic applications.8
2
Their structures can be discussed in terms of (TiO26-) octahedron. The three crystal structures
differ by the distortion of each octahedral and by the assembly patterns of the octahedral
chains.
As shown in figure 1.1, in rutile the octahedral units are connected by their edges; in anatase,
the vertices are connected, and in brookite, both vertices and edges.9
Figure 1.1 TiO2 crystal structure of rutile (a), anatase (b) and brookite (c).10
The differences in lattice structures between anatase and rutile cause different densities and
electronic band structures, leading to different band gaps, in particular 3.02 eV for rutile and
3.20 eV for anatase.10
Based on molecular orbital considerations titanium oxide molecule reside its formation and
stability on the overlapping of d-orbitals of titanium and p-orbitals of oxygen as we can
notice from the molecular orbital diagram of figure 1.2:
a)
b) c)
3
Figure 1.2 Molecular orbital diagram of TiO2.11
Applying concepts of molecular symmetry and LCAO (linear combination of atomic
orbitals) theory, the five d-orbitals of Ti combine with atomic orbitals of the same symmetry
of oxygen atoms leading to the formation of bonding, non-bonding and antibonding
molecular orbitals. In particular frontier orbitals, i.e. HOMO (highest occupied molecular
orbital) and LUMO (lowest unoccupied molecular orbital) are defined by the non-bonding
π orbital and the antibonding π* orbital respectively. Typical values for the band gap of TiO2
depends on the crystal structure, showing 3.2 eV for anatase and 3.02 eV in case of rutile
phase as previously said.
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1.2 INTRODUCTION TO PHOTOCATALYSIS
1.2.1 PHOTOCATALYTIC PROCESS
Photocatalysis is defined as the acceleration of a chemical reaction by the presence of light.
In homogeneous photocatalysis, the reactants and the catalysts exist in the same phase; the
heterogeneous one has the photocatalyst in a different phase from the reactants and this latter
case is the one treated in this work.
The first application of heterogeneous photocatalysis is reported in 1972 when Fujishima
and Honda discovered the photochemical splitting of water into hydrogen and oxygen in the
presence of TiO2.17
In recent years, interest in photocatalysis has focused on the use of semiconductor materials;
in particular TiO2 has shown great potentiality as photocatalyst for the removal of organic
and inorganic species from aqueous or gas phase systems in environmental clean-up,
drinking water treatment, industrial and health applications.18
In heterogeneous photocatalysis of aqueous organic compounds, providing a suitable light
radiation, the pollutants can be degraded to their corresponding intermediates and further
mineralized to carbon dioxide and water:
Organic Contaminants Intermediates CO2 + H2O
Aromatic compounds can be hydroxylated by the reactive OH• radicals that lead to
successive oxidation and eventual ring opening. The resulting intermediates, usually
aldehydes and carboxylic acids will be further mineralized to obtain innocuous products
(CO2 and H2O).15,19
The overall photocatalysis process can be divided into five main steps:15,20,21
1. Mass transfer of the organic contaminant in the liquid phase to the TiO2 surface.
2. Adsorption of the organic contaminant onto the photon activated TiO2 surface (i.e.
surface activation by photon energy occurs simultaneously in this step).
3. Photocatalysis reaction for the adsorbed phase on the TiO2 surface.
4. Desorption of the intermediate from the TiO2 surface.
5. Mass transfer of the intermediate from the interface region to the bulk fluid.
TiO2/hv
5
Therefore, the reactants at first have to diffuse from the fluid phase to the surface of the
catalyst, where they are adsorbed. Once adsorbed, they can react with the catalyst.
Afterward, the products are desorbed from the catalyst surface and diffuse into the fluid
phase.
In terms of rate determination, the overall rate of reaction is equal to the slowest step. When
the mass transfer steps are very fast compared with the reaction steps, the reactants
concentrations in the immediate vicinity of the active sites are almost the same of those in
the bulk liquid phase. If this happens, the mass transfer steps are not rate limiting and do not
affect the overall rate of photocatalytic reaction.
A general scheme of photocatalysis (fig. 1.3) and a detailed list of the reactions that occur in
the photon activated TiO2 semiconductor are shown below:15
Figure 1.3 Photo-induced formation mecvhanism of electron-hole pair in a semiconductor TiO2 particle with the presence of water pollutant (P).17
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Photoexcitation: TiO2 + hv ® e– + h+ (1.1)
Charge-carrier trapping of e–: e–CB ® e–TR (1.2)
Charge-carrier trapping of h+: h+VB ® h+TR (1.3)
Electron-hole recombination: e–TR + h+VB (h+TR) ® e–CB + heat (1.4)
Photoexcited e– scavenging: (O2)ads + e– ® O2•– (1.5)
Oxidation of hydroxyls: OH– + h+ ® OH• (1.6)
Photodegradation by OH•: R–H + OH• ® R’• + H2O2 (1.7)
Direct photoholes: R + h+ ® R+• ® Intermediate(s)/Final degradation products (1.8)
Protonation of superoxides: O2•– + H+ ® HO2• (1.9)
Co-scavenging of e–: HO2• + e– ® HO2- (1.10)
Formation of H2O2: HO2- + H+ ® H2O2 (1.11)
The mechanism of photocatalysis involves multiple processes, the first one is the generation
of an electron-hole (e–/h+) pair when radiation with energy equal or greater than the band
gap hits the surface of the semiconductor (eq. 1.1).
Subsequently the pair is separated into a free electron and a free hole that can be trapped in
the bulk region or at the surface of the photocatalyst (eqs. 1.2, 1.3).
The presence of electron scavengers is necessary to prevent rapid electron-hole
recombination and ensure an efficient photocatalytic process.
In the absence of electron scavengers the recombination between electron and hole takes
place both at the surface and in bulk with the release of heat. (eq. 1.4).
In aerated aqueous solutions oxygen prevents the recombination of electron-hole pair, while
allowing the formation of superoxides radical (O2•–) (eq. 1.5).22–25
This O2•– radical is extremely reactive and can be further protonated to form the
hydroperoxyl radical (HO2•) and subsequently H2O2, as shown in equations 1.9 and 1.11
respectively.
7
The HO2• radical formed was also reported to have scavenging property (eq. 1.10) and thus,
the co-existence of these radical species prolong recombination time in the entire
photocatalysis reaction.
These reactions prove that the presence of both water molecules and oxygen are fundamental
for the good success of the photocatalytic process. Without the presence of water molecules,
the highly reactive hydroxyl radicals (OH•) could not be formed and the photodegradation
of liquid phase pollutants would slow down significantly or would not take place at all;
without O2 instead the recombination of electron-hole pairs would not be inhibited which
leads to a poor efficiency of the process.15
The positive holes either oxidize pollutants directly (eq. 1.8) or react with hydroxyl ions to
produce OH• radical (eq. 1.6), that degrades the pollutant as shown in eq. 1.7. 26
Both oxidation (between holes and the donor molecules) and reduction (between electrons
and acceptor) reactions take place at the surface of semiconductor photocatalyst.
As previously mentioned in the case of TiO2 the band gaps of rutile and anatase are 3.02 eV
and 3.20 eV respectively, which correspond to the need for UV light irradiation to activate
the process. This is the main limitation of TiO2, because UV in natural sunlight represents
only 5-8% of the solar spectrum: to overcome this problem and increase the catalytic
efficiency with solar light, a red-shift of light absorption is needed. In order to obtain such a
modification a precise control of the stoichiometry is necessary, producing more effective
photocatalyst made of different phase of the same material (as in the case of rutile and
anatase for TiO2) or even different metal oxides having different energy states.
Another possible solution is the doping with interstitial or substitutional elements which
allow to introduce intermediate states in the bandgap and/or narrow the band itself.
Doping of TiO2 with transition metal ions (for example V, Cr, Mn, Fe and Ni) as well as
with Ag, Au and Ru, have been demonstrated to red-shift the TiO2 absorption band from the
UV into the visible region, resulting in a great increase in the efficiency of solar-light
photocatalysis.2,7,18
Titanium dioxide is widely used in a variety of applications covering environmental and
energy fields, such as sterelization, water purification, hydrogen evolution and
photoelectrochemical conversion. In the present study we are interested about the titania
photocatalytic activity and its ability in converting solar energy into chemical energy
necessary to oxidize or reduce organic pollutants adsorbed onto its surface. The wide band
8
gap of titanium dioxide allows, upon UV light absorption, the formation of photogenerated
charge carriers i.e. electrons and holes. The electrons present in the conduction band have
the possibility of reducing air containing oxygen molecules thus leading to O2•- formation.
On the other hand holes present in the valence band have a very high oxidizing power, able
to directly react with the organic pollutant or to generate OH• radicals from water molecules.
1.2.2 FACTORS INFLUENCING PHOTOCATALYTIC ACTIVITY
For what concern performances of the photocatalytic activity, there are many factors
influecing the process, as: specific surface area, porosity, pore structure, pore volume,
crystalline phase and structural dimensionality. Researchers generally work on these
paramenters to improve the photoactivity, by choosing nanostructures allowing a proper
compromise among light harvesting ability, charge mobility and surface to volume ratio,
favouring also the desired crystalline phase applying thermal treatments.
In such structure, the absorption of a light radiation with proper wavelength leads to the
formation of an electron-hole pair, from that time on the charges can undergo two different
processes:
• Transferring to the external environment;
• Internal transition leading to recombination or charge trapping.
Regarding photodegradation purposes it is evident the importance of transferring carriers
from the catalyst to the electrolyte, thus reducing/oxidazing pollutants. The feasibility of
such reactions is established by thermodynamic considerations based on relative position
between band edges and redox potential levels of species in contact with the catalyst (fig.
1.4).
Generally to allow electron transfer from conduction band edge of TiO2 to the LUMO of a
molecule, we need the latter lower in energy with respect the former, the reverse holds true
for holes transferring, where the HOMO of the oxidized species lies above the valence band
of the semiconductor.
It is more frequently found in literature the conduction band of anatase to be more negative
in energy with respect to the one of rutile, thus favoring molecular hydrogen formation.
9
For what concern valence edges, both phases are able to promote molecular oxygen
generation and even hydroxyl radicals formation.
Figure 1.4 Relative positions of various redox couples and work functions of various metals relative to the band edges of TiO2.13
As previously said, the photocatalytic performances of anatase are considered superior
compared with the ones of rutile; possible explanations are given below:4,9,12–14
• Even if anatase has a larger band gap than rutile and this reduces the percentage of
sunlight that can be absorbed, it may raise the valence band maximum to higher
energy levels relative to redox potentials of adsorbed molecules. This increases the
oxidation power of holes and facilitates electron transfer from TiO2 to adsorbed
molecules.
10
• Surface properties play an important role in the adsorption of molecules and
subsequent charge transfer; adsorption on anatase is higher than in rutile.
• Anatase exhibits an indirect band gap that is smaller than its direct band gap while
for rutile they are quite similar. Semiconductors with indirect band gap generally
exhibit longer charge carrier lifetimes compared to direct gap materials. A longer
electron-hole pair life in anatase than in rutile would make it more likely for charge
carriers to participate in surface reactions.
• Charge transport may differ for different polymorphs. In addition to the exciton
lifetime, exciton mobility needs to be taken into account. Only excitons that
efficiently diffuse can reach the surface within their life time. One measure for
exciton mobility is the polaron effective mass (the lower is the effective mass, the
higher is the exciton mobility), generally a higher effective mass is reported for rutile
than for anatase.
Furthermore, not only the two polymorphs show varying photoactivity, but the different
crystallographic orientations of the same material may exhibit different activities.15
Recent studies demonstrate that a mixture of anatase and rutile is more active than the pure
phases.4,14 The best results are given for a mixture composed by 70-75% of anatase and the
remaining part of rutile.9
Even if band gaps of both anatase and rutile phases are fixed and equal to 3.2 and 3.02 eV
their frontier orbitals can have 5 combinations of relative positions between valence and
conduction bands of both polymorphs, as shown in figure 1.5.
11
Figure 1.5 Possible relative bands position for anatase and rutile.18
The enhanced activity arises from the increased efficiency of the electron-hole separation
due to the multiphase nature of the particles and the lower band gap of the mixture given by
rutile, which leads to higher absorption of visible light compared to pure anatase.
However, thermodynamic considerations are not enough to assess the real occurrence of
reactions and their contribution to the overall photodegradation process.
For that reason it is mandatory to include kinetic considerations which can be developed
considering two different approaches, the time scale and the chemical approach.
1.2.3 TIME SCALE APPROACH
Factors influencing the kinetics from this point of view are charge separation, charge
mobility and lifetime of the photogenerated carriers. Those properties must be compared
with “losses”, i.e. recombination events and charge trapping. Usually losses have a time
scale comparable to the lifetime of photogenerated charge carriers, so it is important for
electrons or holes to readily find an acceptor/donor species able to be reduced/oxidized.
Generally an electron (or a hole) upon excitation in the conduction band (or valence band),
tends to migrate within a distance proportional to its diffusion coefficient and from that time
on several situations can occur, based on time scale considerations:
12
• Bulk trapping followed by recombination (irreversible loss, i.e. no contribution to
photodegradation);
• The carrier reaches the surface and forms a redox couple thus, leading to
reduction/oxidation of the incoming pollutant;
• Surface trapping followed by charge transfer to a species present in the electrolyte,
so it is evident that surface trapping is not detrimental, since it allows to store a carrier
until it reacts with other species adsorbed on TiO2 surface;
• Direct recombination.
1.2.4 CHEMICAL APPROACH
The kinetics of electron/hole pairs reaction can be correlated to macroscopic parameters such
as adsorbed concentration of reactant and light harvesting.
The photodegradation rate can be expressed by the equation 1.12:
𝑟 = 𝑘ГІ( (1.12)
Where:
r = reaction rate;
Г = reactant concentration;
І = light intensity;
α = paramenter ranging from 0 to 1 (1 for small light intensity, dropping to zero for
increasing intensity).
The concentration of the reactant adsorbed on the TiO2 surface can be assumed to follow a
Langmuir-Hinshelwood kinetics as expressed by the equation 1.13:
Г)* = Г+)* + (𝐾/0𝐶))* (1.13)
Where:
Г+ = surface concentration of the adsorbed species at full coverage;
𝐾/0= adsorption equilibrium constant;
13
C = liquid phase reactant concentration
However several authors like Lucarelli et. al16 have demonstrated that the assumption of
Langmuir-Hinshelwood kinetics holds only for low pollutant concentration.
1.3 PHOTOCATALYST MORPHOLOGY
1.3.1 SUSPENDED NANOPARTICLES
The photocatalytic activity of a TiO2 system mainly depends on its intrinsic properties, such
as the crystal phase, specific surface area and crystallinity. Generally, large surface area is
favorable to improve photocatalytic activity.27,28
Consequently, in order to achieve the maximum turn-over rate for the photocatalytic
reactions, the highest surface area is desired. This is the reason why in the first years of
research on the photocatalytic process in aqueous solutions, TiO2 suspended particles were
used. To further increase the surface to volume ratio in subsequent years TiO2 nanoparticles
were used.
Besides the large surface area that means a higher number of active sites, the nanoparticles
have specific advantages in the enhancement of light absorption due to the large fraction of
surface atoms which allow interband electron transition. The particles size at which the
interface enhancement of absorption becomes significant is around 20 nm.3,29
Nowadays TiO2 Degussa P25 is the most used commercial nanopowder, it is a mixture
formed by 80% of anatase and 20% of rutile, with an average particle size of 30 nm. It is
used as a standard material in photocatalytic studies due to its high activity and commercial
availability.30–32
The main drawback of treatment of contaminated wastewater with suspended nanoparticles
is that after the depuration process other treatments are required, aimed to recover highly
dispersed and suspended catalyst particles from the treated water and from the implants.
To avoid this problem nanoparticles can be immobilized on a substrate, the main drawback
is that the fixation on an inert substrate reduces the amount of catalyst active sites.
Another possibility explored in recent years is the replacement of the powder with various
TiO2 nanostructures which can be grown directly on a substrate.
14
1.3.2 IMMOBILIZED NANOPARTICLES AND NANOSTRUCTURES
As said above, porous TiO2 or TiO2 immobilized on other substrates can be easily separated
from reactants.33
In various works the production of photocatalytic membranes and the immobilization of
TiO2 on different supports using different techniques have been investigated.6,15
Immobilization can be carried out on a transparent (glass or fused silica)34,35 or on an opaque
substrate (activated carbon or metals).36,37
An ideal support must satisfy the following criteria:
• Strong adherence between catalyst and support;
• Non-degradation of the catalyst reactivity by the attachment process;
• Offer a high specific surface area;
• Have a strong adsorption affinity towards the pollutants.
In last years, TiO2 nanostructures materials, such as tubes, wires, dots, pillars, and fibers,
have been produced for applications in photocatalysis.16 Among them, the materials with
tubular structure have been considered the most suitable way to achieve larger enhancement
of surface area without an increase in the geometric area.
But this is not the only advantage of nanotubes, in fact by diminishing dimensions to the
nanoscale, not only the specific surface area increases significantly, even more than one
order of magnitude, but also the electronic properties may change favorably (quantum size
effects, strong contribution of surface reconstruction, or surface curvature); all these factors
contribute to increase the efficiency of the photocatalytic process.2,38
The study of Macak et al.39 proves that nanotubes are significantly more efficient than a
particulate P25 layer, in fact despite a smaller surface area, the first show a much higher
photocatalytic activity.
The optimized geometry of the tubes allows a significantly shorter carrier diffusion path in
the tube walls, lower trapping and recombination kinetics of electron–hole pairs in
comparison with P25 or other nanoparticle systems, where these pairs have to travel between
single nanoparticles.
15
Moreover, the tubular geometry provides a short (straight) diffusion path for pollutant
molecules from the solution to the active surface area, whereas the convoluted porous
structure in the P25 case contains a broad spectrum of diffusion paths (including very long
diffusion lengths).39
In the following pages TiO2 nanotubes will be studied in details, with particular attention on
the production by anodizing process and the effect of the variation of the process parameters.
1.4 PRODUCTION The production of 1D structures can be achieved by different ways, first hydrothermal and
sol-gel routes were reported around 1979 to form nanotubes and nanorod powders. Hoyer et
al.40 were the first who produced TiO2 nanotubes by electrochemical deposition into an
ordered alumina template.
New methods were later developed such as photo etching, or other template assisted methods
including atomic layer deposition (ALD) into templates, new sol-gel techniques and
hydrothermal methods with or without templates.
TiO2 nanoparticles or nanorods can be also prepared by using micelle templates of
appropriate surfactants above their critical micelle concentration, the surfactant molecules
aggregate and disperse in a liquid to give so-called spherical or rod-like micelles, which are
used as template for TiO2 preparation. As explained by Roy et al.38 , nanotube formation is
mostly carried out using water containing reverse micelles with a cylindrical exterior
surface. The titanium precursor can then react at the micelle surface, and after removal of
the surfactant, a nanotube structure is obtained. Precursor usually consists in TiCl4 or other
titanium alkoxide solutions.
As mentioned above, another possible process to produce nanotubes is the
hydro/solvothermal method, which was first reported by Kasuga et al.41,42 . In this method,
bulk TiO2 powder is treated with NaOH solution in autoclave at 100–150°C, followed by an
acid treatment in HCl. The formation of the tube geometry is based on exfoliation of TiO2
crystal planes in the alkali environment and stabilization as Ti–O–Na+. This step is followed
by a rolling of the nanolayer sheets into tubes during cooling or in the HCl treatment.
In atomic layer deposition (ALD), surfaces of templates are coated conformably one atomic
layer after the other by using alternating cycles of exposure to a titania precursor followed
by purging and hydrolysis.38
16
The deposition on alumina template allows to obtain nanotubular or rod structures vertically
aligned to the substrate, which is advantageous for photocatalytic efficiency, but a critical
template-removal step is necessary.
The titanium precursor solution ’template-based’ processes result in single tubes while
‘template free’ methods lead to agglomerates of tubes or bundles that are dispersed in
solution, in both cases a wide distribution of tube geometries and lengths is obtained.
To make the structures suitable for the production of photocatalytic devices, the tubes are
usually compacted into layers on a substrate. One critical problem may rise during this phase,
in fact this process leads to an arbitrary orientation of the nanotubes, which eliminates many
advantages of the one-dimensional directionality (for example providing a 1D electron path
to the electrode).43
Therefore, the discovery of anodic formation of self-organized TiO2 structures in the last
decade has again strongly stimulated TiO2 based materials research.
In fact, anodizing performed in electrolyte containing fluoride ions, allows to produce
ordered nanotubes layer by imposing a voltage between the cathode (usually made of Ti)
and the piece that has to be covered. The anodization technique is described more in detail
in the next pages.
The different morphologies of the nanotubes synthetized by different methods are shown in
figure 1.6 which briefly summarizes the main characterists of various production processes.
The superior order and alignment of nanotubes produced by anodizing is evident, compared
to the other production processes.
17
Figure 1.6 Overview of different morphology and properties of TiO2 nanotubes synthesized by different methods.46
18
1.4.1 ANODIZING
Figure 1.7 General scheme of anodization process.
The anodization technique consists of polarizing titanium by imposing a current flow
between the titanium specimen and a counter electrode (fig. 1.7); this causes the metal atoms
to oxidize to Ti4+ ions (eq. 1.14) which can combine with oxygen anions to form directly an
oxide layer on the surface of the metal (eq. 1.17).
The metal ions can also react with hydroxyl anions to form a hydrated oxide (eq. 1.15) that
further transforms in oxide by a condensation reaction (eq. 1.16).
Oxygen and hydroxyl anions are both provided by the presence of water in the electrolyte.
Regarding the growth of the oxide layer, both an inward and an outward oxide growth are
usually considered, due to field-assisted ion migration under high field conditions, with a
slight predominance of O2- charge carriers across the oxide to reach the metal surface where
Ti4+ cations are produced.
Further oxide growth is controlled by field-aided ion transport (O2- and Ti4+ ions) through
the growing oxide. If the system is under a constant applied voltage, the field within the
oxide is progressively reduced by the increasing oxide thickness, the process is self-limiting.
19
4Ti ® Ti4+ + 4e- (1.14)
Ti4+ + OH- ® Ti(OH)4 (1.15)
Ti(OH)4 ® TiO2 + 2H2O (1.16)
Ti4+ + 2O2- ® TiO2 (1.17)
Anodizing is usually performed under galvanostatic conditions, up to a determined cell
voltage or the passage of a determined charge, or by potential sweep.
In both cases, a period of maintenance and stabilization can be applied at a constant cell
voltage: this step is particularly important when the production of nanotubular oxides is
desired.
The process parameters which most affect the properties of the growing oxide are the
electrical parameters (current density, cell voltage, stabilization time), the electrolyte
properties (composition, pH, and temperature) and the composition of the metal itself and
its surface conditions.
Based on oxide characteristics, three main types of anodic oxidation processes can be
identified:
• “Traditional” anodizing;
• Anodizing in a fluoride containing electrolytes (causing the growth of nanotubular
oxides);
• High-voltage anodizing, also called anodic spark deposition (ASD) or plasma
electrolytic oxidation (PEO) or micro-arc oxidation (MAO).
Figure 1.8 reports an overview of possible results obtained using a titanium anode as a
function of applied cell voltage.1,2
20
Figure 1.8 Map of anodising techniques as a function of applied voltages, and related range of oxide thickness.1
1.4.1.1 NANOTUBES BY ANODIZATION
The electrochemical anodization approach can lead to the growth of nanotubes that are self-
organizing, aligned perpendicular to the substrate surface, with a well-defined and
controllable tube length.
As the tubes are directly grown on the metal surface, they are already electrically connected
to the substrate and easy to handle.
Another advantage of the electrochemical anodization technique is that it allows virtually
any shape of titanium (and other metals) surfaces to be coated with a dense and defined
nanotube layer and it is thus an extremely versatile (and thus easy to scale up) structuring
process.38,44
In 1999 Zwilling et al.45 detected that anodization of Ti in a fluoride containing electrolyte
resulted in the formation on the metal surface of a self-organized porous structure, which
was the base for the nanotubes growth.43
The first fabrication of titania nanotube arrays via anodic oxidation of titanium foil in a
fluoride-based solution were reported in 2001 by Gong et al.46 .
Further studies focused on the effect of previous treatment and the variation of anodizing
parameters on the nanotube morphology, length and pore size, and wall thickness.47–50
In all cases the nanotubes obtained by anodization are amorphous, a subsequent annealing
treatment is usually performed to obtain crystalline phases.
21
In order to provide partial dissolution of the oxide and subsequent nanotube array formation
(fig. 1.9) a fluoride ion containing electrolyte is necessary.51
Figure 1.9 Effect of fluorides containing electrolytes on formed TiO2 layer.54
Electrolyte composition and its pH play a critical role, they determine both the rate of
nanotube array formation and the rate at which the resultant oxide is dissolved, thus
determining the maximum the length, which is defined by the equilibrium between the two
processes.
In fact, the anodization of titanium in fluoride ion containing electrolytes can be described
by a competition between the oxide formation at the metal oxide interface (eq. 1.18) and
chemical dissolution of the oxide as soluble fluoride complexes at the outer interface (eq.
1.19).52
Ti + 2H2O ® TiO2 + 4H+ + 4e- (1.18)
TiO2 + 6F- + 4H+ ® [TiF6]2- + 2H2O (1.19)
The hexafluoride complex can be also generated by the reaction between the metallic ions
transported through the growing oxide and the fluoride ions in the solution (eq. 1.20):
Ti4+ + 6F- ® [TiF6]2- (1.20)
22
Therefore, the presence of F- ions has two fundamental effects: the first one is the formation
of water-soluble [TiF6]2- complexes according to reactions 1.19 and 1.20, the second one is
given by the small ionic radius that makes them suitable to enter the growing TiO2 lattice
and to be transported through the oxide by the applied field in competition with O2-.
It has been observed that fluorides may migrate at a rate twice as high as O2- ions through
oxide lattices.38
Figure 1.10 shows how the complex formation ability leads to a permanent chemical attack
(dissolution) of formed TiO2 and prevents Ti(OH)xOy precipitation, because as Ti4+ ions
arrive at the oxide/solution interface reaction 1.20 occurs, and then the formation of
Ti(OH)xOy is inhibited.53
Figure 1.10 Schematic representation of the Ti anodization (a) in absence of fluorides (results in flat layer), and (b) in presence of fluorides (results in the tube growth).54
Figure 1.11 shows the different steps of nanotubes growth, the small pits formed due to the
localized dissolution of the oxide (fig. 1.11 b) act as pore forming centers to the growth of a
disordered worm-like structure underneath the original compact layer (fig. 1.11 c).
The pitting of the oxide allows the passage of current, effectively creating easy paths for the
development of nanotubes because of the presence of a higher electric field beneath the pores
or localized acidification at the base of the tubes, leading to a higher dissolution rate at the
pore bottom.51
The transition from irregular to regular pores (fig. 1.11 d) can be ascribed to the fact that the
different pores compete for the total available current. Only if the current in a pore is
23
sufficiently high it will “survive”; thus under a critical value the pore growth is “shut down”.
After this natural selection process a self-organized situation is established.54
Figure 1.11 Schematic representation of the TiO2 nanotube array formation: (a) compact layer growth to the maximun thickness, (b) breakdown of the barrier layer film, (c) growth of the pores resulting in a worm-like structure, (d) more ordered structure partially covered with the thin layer, and (e) regular self-organized porous TiO2 structure.56
During the growth of the layer in electrolyte containing fluorides the current-time curve
deviates from the classical high-field growth as we can observe from fig 1.12.
Once reached the desired voltage and potentiostatic conditions onset, in the first stage (phase
I) the current decreases exponentially as a result of the oxide layer formation.
After the initial decay, the current increases again (phase II) with a time lag that is shorter,
the higher the fluoride concentration; this rise of current is probably caused by the formation
of porosity on the substrate that increases the surface area of the electrode.
In region III pore growth is initiated, the individual pores start interfering with each other,
and start competing for the available current. This leads under optimized conditions to a
24
situation where the pores equally share the available current, and self-ordering under steady
state conditions is established.
The current reaches a virtually constant value reflecting the establishment of a steady-state
situation between dissolution and formation of oxide, current value increases with increasing
fluoride concentration.38,53,55
Figure 1.12 a) Current-time curve and b) respective growth stages during nanotubes formation.54
The “first generation” of TiO2 nanotube array was grown in HF electrolytes or acidic HF
aqueous mixtures; an example of the typical morphology is shown in Fig.1.13 A.
The tubes showed strong irregularities and inhomogeneity along their walls and the structure
of the layer was not highly organized.
The so obtained nanotubes were approximately 100 nm in diameter with a limited thickness
that would not exceed 500–600 nm.
The “second generation” was obtained when buffered neutral electrolytes containing NaF or
NH4F instead of HF were used and the importance of the pH of electrolyte was taken into
account (see paragraph 1.5.1.2).
25
The “third generation” nanotubes were grown in non-aqueous electrolytes, in fact fluoride
ions in water-based solutions are far more aggressive than in organic media, which is why
the growth of the nanotubes in water is limited to lengths of a few microns.51
On the contrary, using optimized organic electrolyte systems, such as ethylene glycol, almost
ideal hexagonally arranged nanotube arrays can be grown, with thickness of several
hundreds of micrometers and tube diameters ranging from µm to hundreds µm (Fig. 1.13
C).
Standard anodization carried out in glycerol electrolytes (Fig. 1.13 B), allows the formation
of tubes with extremely smooth walls and a tube length exceeding 7 µm.53
Other electrolytes have been investigated, for example using CH3COOH; remarkably small
tube diameters could be obtained.3,38
Figure 1.13 SEM images showing TiO2 nanotube layers grown by different anodization processes of Ti. (A) Typical morphology obtained in acidic fluoride or HF electrolytes, (B) glycerol/fluoride electrolytes, (C) ethylene glycol/fluoride electrolytes. The insets show top-views (open tubes), bottom views (closed ends) and side walls in detail.54
By changing the operational parameters of the anodization process the size, the geometry
and the composition of the obtained nanotube can be controlled, proper modifications allow
to the photocatalytic efficiency to be increased.
26
The most important and thus the most studied parameters are the anodization time and the
applied voltage with particular attention on the composition, the pH, and temperature of the
electrolyte.
1.4.1.2 EFFECT OF ANODIZING PARAMETERS ANODIZATION TIME It is proved in different studies that the anodization time has great influence on the length of
the formed nanotubes, increasing the duration of the process, longer nanotubes can be
obtained.38,51,56
The figure 1.14 shows that, even in electrolyte with different water contents, once the
initiation phase is overcome, the tubes grow longer with time until the etching action of
fluorides establishes a steady-state situation between tube formation at the bottom and
etching at the top of the tube.
Figure 1.14 TiO2 nanotube-layer thickness with anodization time for different electrolytes (anodization voltage for ethylene glycol electrolyte held at 60 V, and 40 V for other electrolytes): ■ water-based acidic, ▲ water based neutral, ☐ glycerol, ￮ glycerol/H2O 50:50, * ethylene glycol.38
27
APPLIED VOLTAGE The anodizing voltage has important effect on both the nanotubes diameter and length, as
studied in detail by Sun et al. 57
Pore diameter of the nanotubes increases linearly with field strength while the tube length
and the growth rate increase exponentially, as shown in figure 1.15.
Figure 1.15 (Left) Effect of the relative field strength En-Eo (n=0~6) on nanotube length and pore diameter.57 (Right) Voltage dependence of the tube diameter for different electrolytes: ￮ water-based, ▼ glycerol/H2O 50:50, ☐ glycerol, ▪ ethylene glycol.38
The applied field influences ion migration in electrolyte and ion transport in anodic barrier
layer; higher field strength results in higher ion flux in the electrolyte and thus the growth of
the tube can continue for longer time as a result of higher fluoride ion concentration at the
bottom of the tube.57
Typical potential values for nanotube production vary in the range from 20 to 80 V.
ELECTROLYTE
The electrolyte composition and properties are fundamental for the nanostructures
formation, affecting their dimension, morphology and shape.
As mentioned before, the organic electrolytes allow to obtain longer nanotubes than the ones
obtained in water-based electrolytes thanks to the different activity of fluoride ions (more
aggressive in water-based solutions).
28
This fact is clearly depicted in the figure 1.16 that represents the plot of the nanotube length
as a function of the anodization time for the anodization in a water-based and in the
glycerol/NH4 F mixture at 20°C.
Figure 1.16 The tube length as a function of anodization time obtained directly from cross-section measurements of samples formed during different times in glycerol +0.5 wt.% NH4F and 1M (NH4)2SO4 + 0.5 wt.% NH4F at 20 V at 20°C.58
The F- concentration is another very important factor in the composition of the electrolyte;
it has to be kept low in order to minimize dissolution, but high enough to ensure the growth
of the nanotubes.51
Typically the fluoride ions concentration in electrolytes varies from 0.3 to 0.5 wt%, affecting
the growth rate of the self-ordered nanostructures but also the pH of the solution.
The latter has crucial effect on the dissolution rate of the oxide layer and consequently on
the length of the nanotubes.
As shown in the figure 1.17 B, acid pH causes faster dissolution of the oxide limiting the
length of nanotubes.59
As mentioned before, the thickness of the nanostructured layer is essentially the result of an
equilibrium between electrochemical formation of TiO2 at the pore bottom and the chemical
dissolution of this TiO2 in an F- ion containing solution.
The dissolution rate can be tuned by modifying the dissolution current; using a buffered
neutral solution as electrolyte and adjusting the anodic current flow to an ideal value, acid
can be created where it is needed (pore bottom). Higher pH values are established at the pore
29
mouth as a result of migration and diffusion effects of the pH buffer species (fig. 1.17
A).43,58,60,61
Figure 1.17 A (a) Schematic representation of the dissolution reactions and mechanisms; (b) the pH profile within a pore; (c) the dissolution-rate profile within a pore wall. B Experimental determination of the dissolution rate, Rdiss, of the anodic TiO2 depending on the pH value results are taken from XPS sputter profiles of 20 V anodic oxide immersed for different times in 1M (NH4)2SO4 + 0.5 wt.% NH4F solution with different pH values.61
Also the temperature of the electrolyte has various effects on the morphology and the
dimension of the obtained nanostructure.
Regonini et al.51 reported that in aqueous solutions at temperature lower than 2°C the growth
of the nanostructures is inhibited, whereas in organic electrolytes the temperature range most
favorable for the growth of nanotubes is between 0 and 40 °C.
It has also been shown that, while in aqueous media the diameter on the nanotubes does not
depend on the anodizing temperature, in organic electrolytes at a higher anodizing
temperature the diameter is larger.
Lower temperatures correspond to higher viscosity and thus both ion migration and
especially the dissolution of TiO2 and Ti by F- ions are reduced. For the same reason the
oxide layer thickness increases with temperature, as studied and plotted by Macak et al.58
The effect of temperature on nanotubes length and diameter is plotted in figure 1.18.
A B
30
Figure 1.18 An evaluation of the tube diameter and length as a function of the temperature for glycerol electrolytes. The average tube diameters are 40±5, 50±6 and 60±7 nm and lengths are 800±0 nm, 2.2±0.1 µm and 3.4±0.1 µm for 0, 20 and 40°C.58
Another factor to be taken into account, especially with organic electrolytes, is the ‘ageing’
of the electrolyte. It is reported in different works38,51 that by reusing for many times the
same solution, its electrical conductivity increases due to the combined increase of the TiF62-
content and the reaching of a steady water content in the electrolyte (for example, ethylene
glycol is hygroscopic and tends to adsorb water from environmental air).
Higher conductivity enhances the growth rate of nanotubes while higher content of TiF62-
reduces the dissolution rate; the result is an increase in tube length as shown in figure 1.19.
Figure 1.19 Effect of aging on the conductivity of an organic electrolyte and length of the grown nanotubes.51
31
1.4.2 ADVANCED STRUCTURES
Nowadays, even more advanced geometries of TiO2 nanotubes can be prepared by
modifying the electrochemical conditions during anodization.
By applying voltage steps, compact layers underneath the tubes, stacked tube layers and
branched tubes can be fabricated on Ti and various other metals (fig. 1.20 a top).38,43
Using regular voltage cycling a ‘bamboo-type’ morphology of TiO2 nanotubes can be grown
in HF-containing ethylene glycol electrolyte (fig. 1.20 a middle).
The distance between the bamboo rings can be controlled by adjusting the holding times at
the respective anodization potential.
This kind of structure shows enhanced mechanical integrity and higher specific surface
compared to classic nanotubes, for this reason it has been investigated for application in solar
cells and display with enhanced electrochromic switching. Other reports focused on use as
a photocatalyst or in sensing, and due to the high biocompatibility of TiO2, interactions of
biological cells with the tubular surface or enhanced bone formation on the material were
studied.43,60,62
Using a subsequent etching treatment of the bamboo structure, the tube walls can be etched
off while the reinforced compact parts are left behind, this leads to the formation of a TiO2
nanolace (nanogrid) structure (fig. 1.20 a below).
Membrane morphologies as the one shown in fig. 1.20 b can be obtained by previous
formation of nanotubes and subsequent selective dissolution of the substrate and bottom
opening.
In these last years also the investigation of synthesis, structural characterization, physical
properties, and applications of TiO2 nanotubes fabricated by anodizing on some specific
substrates, such as conductive glass, plastic, quartz or silicon wafer have drawn especial
attention.63
A ‘superlattice’ nanotube array can be synthetized from multilayer metal stacks as shown in
Figure 1.20 c. Such superlattice composed of modulated TiO2/Ta2O5 heterojunctions with
modulated tube walls show phonon confinement which can strongly enhance the optical,
electrical, electronic and surface chemistry properties of nanometer-sized materials.43,64
32
Figure 1.20 SEM and STEM imagines of advanced TiO2 morphologiesc: (a) on the top tube-stacks and branching of tubes, in the middle bamboo type nanotubes and at the bottom TiO2 nanolace. (b) SEM image showing TiO2 membrane open on top and bottom, optical image of a membrane and a scheme of nanotubular flow through photocatalytic membrane. (c) 'Superlattice' structures of a TiO2 /Ta2O5 nanotube arrays from a Ti/Ta multilayer substrate.43
33
1.5 APPLICATION OF NANOTUBES IN THE PHOTOCATALYTIC PROCESS
In the previous part the effects of the anodizing parameters on nanotubes structure are
described, now their impact on photocatalytic efficiency of TiO2 will be investigated.
The dimensional properties of the nanotubes are important for the process of
photodegradation of pollutants as well as the environmental conditions (pH, temperature,
presence of ions) and the available light intensity; but also other processes, previous or
subsequent the anodizing, such as polishing and annealing have a great influence on the
photoactivity.
1.5.1 NANOTUBES DIMENSION AND SHAPE
The effect of tube length on the photocatalytic activity has been studied by several authors,
where figure 1.21 is a useful representation. Most of them found that at higher nanotube
length corresponds a higher photoactivity, because by increasing thickness of anodic TiO2
nanotube layers the specific surface area increases and consequently the activity should be
improved.
Figure 1.21 Degradation curves of AO7 for latest generation of TiO2 nanotubular arrays grown in ethylene glycol (EG) based electrolyte with different tube length.43
34
However, there are different investigations that report a maximum in the photocatalytic
activity for limited tube layer thicknesses and/or the absence of an influence of the tube
length.39,43,55 A possible explanation can be attributed to the fact that layers thicker than ~20
µm show only a weak adherence to Ti metal substrate, and thus lower efficiency.
Similar discrepancies also exist for the influence of tube diameter, small diameter nanotubes
generally show a higher photocatalyitic efficiency due to a higher specific surface area, but
they are more difficult to be produced and it is hard to control the diameter independently
from the tube length.
Another crucial factor is the morphology of the nanotubes that depends mainly on the type
of electrolyte; it is found that the tubes grown in organic electrolytes containing water are
more active than tubes grown in pure ethylene glycol electrolytes. This can be ascribed to
the ripple formation on the tube walls for water grown tubes which may act as efficient
charge carrier traps.30,43,55
1.5.2 SURFACE PREPARATION
The effect of a polishing treatment is studied in details by Lu et al.47 by performing the
anodization process on surfaces having different polishing conditions.
Results demonstrate that, as expected, when anodization is performed on rough surfaces the
corresponding nanotube morphologies and the obtained bottom oxide barrier layer are non-
uniform.
For mechanically polished samples, even if the nanotube size is non-uniform, a more flat
and thicker nanotube oxide barrier layer can be grown with a higher photocatalytic activity
with respect to the non-pretreated ones.
1.5.3 ANNEALING
The oxide layer grown during the anodization is amorphous, in order to obtain the desired
crystal structure a subsequent annealing process is necessary.
Figure 1.22 shows a comparison of XRD patterns of nanotube layers without annealing and
after annealing at different temperatures and time. For TiO2 nanotubes, anatase formation is
observed at temperatures around 300°C or higher
35
At temperatures higher than 500°C the rutile phase appears, at even higher temperatures the
rutile formation increases, causing possible modifications in the shape of nanotubes, even
their collapse into particles.
Substrate free tubes or tubes on glass can be annealed to temperatures up to ∼750°C before
total conversion to rutile within the tube walls occurs.43,55
Figure 1.22 X-Ray diffraction patterns for TiO2 nanotubular arrays grown on Ti metal (15 µm long) at room temperature and annealed at different temperatures 350°C, 500°C, 700°C, and 900°C.43
If the annealing of the tubes is carried out on metallic Ti at temperatures above 500°C in O2
containing atmospheres, substantial thermal oxidation of the substrate occurs.
This leads to the formation of a compact rutile layer underneath the nanotubes (fig. 1.23)
with a consequent detrimental effect on the photocatalytic activity.
36
Figure 1.23 SEM image of rutile layer formed underneath the nanotubes after annealing at temperature higher than 500°C in O2 containing atmosphere.43
Another effect is that in tubes annealed at temperatures higher than 450°C, some cracks in
the tube walls can occur, which can slow down electron transport.
For these reasons, for substrate free tubes or tubes grown on glass the maximum
photodegradation efficiency is reached by annealing at 650°C while as we can notice from
figure 1.24, in order to obtain the maximum result from nanotubes immobilized on Ti
substrate, the annealing process must be carried out at a temperature that varies in the range
450°-500°C (figure 1.25).39,43,55
Figure 1.24 Influence of annealing temperature (and atmosphere) on the photocatalytic degradation of AO7 using TiO2 nanotubes of thickness ∼ 1.5 µm.43
37
Figure 1.25 Photocatalytic decomposition rates of AO7 for immobilized nanotubes.39
1.5.4 ENVIROMENTAL CONDITIONS
The pH, the temperature and the dissolved oxygen in the system are fundamental parameters
for the photocatalytic activity.
Any variation in the operating pH is known to affect the isoelectric point or the surface
charge of the photocatalyst used.
Many reports have used the point of zero charge (PZC) of TiO2 to study the pH impact on
the photocatalytic oxidation performance.65–67
The PZC is a condition where the surface charge is zero or neutral, in the case of TiO2 it lies
in the pH range of 4.5 and 7.0. At PZC, the interaction between the photocatalyst and water
contaminants is minimal due to the absence of any electrostatic force.
When operating at pH < PZC, reaction shown in equation 1.21 occurs, the surface of the
catalyst becomes positively charged and gradually exerts an electrostatic attraction force
towards the negatively charged compounds. Such polar attractions between TiO2 and
charged anionic organic compounds can intensify the adsorption onto the photoactivated
TiO2 surface for subsequent photocatalytic reactions.
At pH > PZC, equation 1.22 occurs and the catalyst surface will be negatively charged and
repulse the anionic compounds in water.15,23,68
At pH < PZC: TiOH + H+ ® TiOH2 (1.21)
At pH > PZC: TiOH + OH- ® TiO- + H2O (1.22)
38
In contrast to the PZC effect, it is also reported that at higher pH, more OH• radicals are
produced, leading to higher photocatalytic degradation.69
Regarding the effect of temperature on the photocatalysis process, most investigations state
that an increase in photocatalytic reaction temperature (>80°C) promotes the recombination
of charge carriers and decreases the adsorption of organic compounds onto the TiO2 surface,
inducing a negative effect on the photocatalytic activity.15
Even at very low temperature (∼0°C) photodegradation is unfavoured due to the increase
of the apparent activation energy needed for the process to take place.
The optimum reaction temperature for photomineralization is reported to be in the range of
20° and 80°C.70
The presence of ionic species in water solution can affect the photocatalytic degradation by
modifying the number of photogenerated electrons, enhancing or inhibiting the electron-hole
recombination and the hydroxyl radical scavenging.
The interaction of holes and hydroxyl radicals with inorganic ions generates inorganic
radicals which can be adsorbed on TiO2 surface causing a reduction of the photocatalytic
activity.23
In order to maintain and control the desired environmental conditions the photocatalytic
process, as many other chemical processes, is carried out in a reactor.
39
2 CHEMICAL REACTOR DESIGN
A chemical reactor is a device providing the possibility to control the environment where a
desired chemical reaction occurs. Generally their shape is similar to a tank or a pipe,
depending if they are designed to operate in batch or continuous mode. Usually the reactants
are in liquid or even gas phase, often catalysed onto a solid catalyst. For devices operating
in batch mode, the key process variables such as Temperature, Pressure and chemical
concentrations are varying in time so the design equation will be in a differential form, while
in continuous reactors the operational conditions are typically run at steady-state. The
analysis of chemical reactors is usually reduced to the consideration of 3 ideal models
differing in mode of operation and quality of mixing: batch reactor (fig. 2.1 A),
Continuous Stirred Tank Reactor (CSTR) (fig. 2.1 B) and Plug Flow Reactor (PFR) (fig. 2.1
C).71
Figure 2.1 A) Batch reactor, B) CSTR, C) PFR
. The batch reactor, working in discontinuous mode, is considered a thermodynamically
closed system, while the other two ideal cases, working in continuous condition, are referred
to be open ones. Generally the batch reactor is more suited for industrial processes involving
complex chemical reactions or valuable products, on the other hand the CSTR and PFR are
preferred in case of large scale production of relatively simple chemical reactions. For what
concerns kinetics, the batch reactor has an intrinsically unsteady nature, while reactors
working in a continuous fashion operate in an approximately steady-state regime. The PFR
is very different from the discontinuous batch reactor, since in the former no mixing is
allowed. However, we can consider the PFR as composed by a sum of infinitesimal volumes
A) B) C)
40
behaving as a batch reactor, spending units of time 𝑡4(eq. 2.1) inside the plug flow reactor
volume 𝑉6 .71
𝑡4 =789:
(2.1)
𝐹7 = 𝑣𝑜𝑙𝑢𝑚𝑒𝑡𝑟𝑖𝑐𝑓𝑙𝑜𝑤𝑟𝑎𝑡𝑒.
Furthermore, in the case of a catalyzed reactor some other parameters play a major role such
as the nominal area of the catalyst, the internal mass transfer of reactants and products,
strongly depending on the catalyst topology and fluidodynamic considerations inside the
reaction volume. The mathematical modelling of the 3 already mentioned reactors can be
approached by a molar balance, giving the possibility to evaluate the overall reaction rate of
the process occourring in our reactor choosing a suitable reaction volume.
A general molar balance which can be applied to every kind of reactor is shown in figure
2.2 and expressed by eq. 2.2:
𝐹GH − 𝐹G + 𝐺G =KLMKN
(2.2)
where:
𝐹GH = molar rate by which the j specis enters the system (OPQR
);
𝐹G = molar rate by which the j specis leaves the system (OPQR
), equal to𝐶G𝑄, where
𝐶G = concentration of the A specis (OPQOT );
Q = volume throughput (OT
R);
𝐺G = rate of generation/decomposition inside the reaction volume (OPQR) ;
KLMKN
= accumulation rate of the A species
We can express the rate of decomposition/production as (eq. 2.3):
FA FA GA
Figure 2.2 Schematic representation of a generic reactor.
41
𝐺G = 𝑟G𝑉 (2.3)
where 𝑟G = overall reaction rate inside the reaction volume V VOPQROTW.
The above molar balance is a general formulation of an ideal reactor with input and output
channels, allowing the destruction/formation of the species A inside its reaction volume.
To complete the equation and therefore have a complete understanding of the reaction
kinetics and reactor efficiency we need to find an expression for the reaction rate, showing
its dependance on the reactant concentration. This task is subject of chemical kinetics,
allowing us to derive some common reaction-rate expressions which can be solved
numerically but also analytically under the hypothesis of isothermal reactor and in presence
of few reactants.
2.1 APPLICATION OF MOLAR BALANCE TO REACTORS
2.1.1 BATCH REACTOR
Figure 2.3 Batch reactor.
A batch reactor (fig. 2.3) consists in a vessel, filled with our reaction fluid, with the
application of mechanical stirring. In this way, under the hypothesis of a perfect mixing
thanks to agitation, we assume a perfect homogenization of temperature and chemicals
inside the reaction volume.
42
This idealized reactor is described under the following assumptions:
• Closed-system: no addition/removal of reactants/products (i.e. 𝐹GH = 𝐹G = 0 );
• Unsteady-state conditions: concentration of reagents varies in time;
• The vessel is perfectly mixed, this assumption is satisfied considering stirring (i.e. 𝑟G
is spatially constant);
• Concentrations of chemicals and temperatures are uniform in space but not constant
in time.
Under the following semplifications the design equation of the batch reactor reduces to eq.
2.4:
-𝑟G𝑉 =KLMKN
(2.4)
Being the number of moles inside the reaction volume of the A species equal to 𝐶GV we can
express its time derivative as (eq. 2.5):
KLMKN
= KYMKN𝑉 + 𝐶G
K7KN
(2.5)
In the case of constant reaction volume V it semplifies to equation 2.6:
KLMKN
= KYMKN𝑉 leading to eq. 2.7: (2.6)
KYMKN
= −𝑟G (2.7)
where the minus indicates that the A component is a reactant.
As we can notice in the case of a batch reactor with constant volume, the reaction rate is
directly proportional to the time derivative of the concentration of the reactant. Then by
making chemical kinetics considerations, assuming isothermal condition and presence of
few reactants in our volume, we can derive an expression of the reaction rate (eq. 2.8)
analytically solvable as for a first-order irreversible reaction:72
A B with r = k𝐶G (2.8)
By inserting it inside our design equation we obtain a differential equation (eq. 2.9):
KYMKN
= −𝑘𝐶G (2.9)
k
43
which can be solved analytically prescribing initial condition for concentration of the
reactant A at time t = 0: 𝐶G(𝑡) = 𝐶GH
Leading to the following solution (eq. 2.10) describing the evolution with time of the
concentration of the reactant A (fig. 2.4):
𝐶G(𝑡) = 𝐶GH𝑒)ZN (2.10)
Figure 2.4 Exponential decay of A concentration at different k values.73
By rearranging the above equation to a logarithmic form (eq. 2.11):
ln V YMYM]
W = −𝑘𝑡 (2.11)
we get a useful graphical representation of the evolution in time of the concentration of A
for the determination of the rate constant k, being the slope of the straight line of fig. 2.5.
Figure 2.5 Concentration evolution in time in a semi-log plot.73
44
2.1.2 CONTINUOUS STIRRED TANK REACTOR
Figure 2.6 (Left) CSTR, (Right) CSTR scheme
The CSTR (fig. 2.6) is a vessel containing a stirring system where flowing is ensured by
inlet and outlet channels. It is considered an ideal reactor, since agitation allows to consider
a perfect homogenization of chemicals. In this way the composition inside the vessel is
supposed to be equal to the one at the outlet of the reactor.
Assumptions:
• Continuous addition/removal of reactants/products (i.e. accumulation rate is zero);
• The introduced reactants immediately mix with the fluid already present in the
vessel;
• Uniformity of chemicals and temperatures;
• Composition of the outcoming stream is the same of the fluid contained in the vessel;
• Steady-state condition, reaction rate is uniform and constant.
Under those hypothesis the CSTR design equation reduces to the steady-state case (eq. 2.12):
𝐹GH − 𝐹G + 𝑟G𝑉 = 0 (2.12)
Rearranging the equation the reaction volume is obtained from eq. 2.13:
𝑉 = 9M])9M)6M
(2.13)
45
However, for the transient regime leading to the steady-state concentration, considering the
equation 2.14 containing the accumulation term, again assuming first order irreversible
reaction, we can take into account constant reaction volume and equal inlet and outlet flow:
KYMKN= *
N(𝐶GH − 𝐶G) + 𝑟G (2.14)
Again with the help of the initial condition 𝐶G(𝑡 = 0) = 𝐶GH, the equation can be solved
analytically applying separation of variables. However being the solution quite complex and
since this case does not apply to the present experimental work, we avoid its representation
here.
2.1.3 PLUG FLOW REACTOR (PFR)
Figure 2.7 Plug Flow Reactor
The plug flow reactor (fig. 2.7 and 2.8) is a tubular reactor properly tailored to allow a
velocity profile of the fluid as flat as possible, avoiding recirculations, i.e. making possible
the hypothesis that each portion of matter spends the same time inside the tube.
Under plug flow reactor assumption the following sentences are verified:
• No radial variation of concentration, temperature or rate of reaction;
• All fluid elements have the same residence time inside the reactor;
• The velocity profile only modifies its shape in correspondance of the tube walls.
The material balance for the PFR provides us the following result (eq. 2.15):
KYMKN
= − KYM^K7
+ 𝑟G (2.15)
which reduces to the following equation 2.16 considering the steady-state regime where all
the time derivatives are considered to be zero:
46
K9MK7
= 𝑟G (2.16)
Figure 2.8 PFR scheme.
Since the composition of the fluid varies point to point along the tube axis we restrict our
analysis on the infinitesimal volume element dV indicated in the figure 2.8, this is basically
the reason why our design equation is written in a differential form.73
2.2 PHOTOCHEMICAL REACTORS Photochemical reactors are sophisticated devices in which the evaluation of the governing
equation is never an easy task, requiring numerical simulation with the use of experimental
results to find proper constants for the model. Generally, light is used to activate a solid
catalyst (usually a semiconductor) able to directly oxidize/reduce a species present in the
reaction volume or even accelerate the formation of reactive products such as hydroxyl
radicals able to degrade the pollutant. Based on position and motion of the catalyst, we can
introduce a first classification of photoreactors which can be adopted for both continuous
and discontinuous mode of operation.
2.1.4 FLAT-PLATE WITH IMMOBILIZED CATALYST PHOTOREACTOR
Flat-plate reactors (fig. 2.9) have gained popularity since they allow to exploit a high surface
area to volume ratio and limit hindrance of the device, also favouring a good scalability.
Furthermore the semplicity of the reactor allows a homogeneous distribution of the radiative
flux coming from the light source generating also a low pressure drop along the structure
due to the fluid friction on reactor walls. An immobilized reactor technology permits also to
avoid the presence of the system for the recovery of the catalyst, which is mandatory in the
case of a slurry reactor.
47
Figure 2.9 Flat plate reactor.74
Let’s summarize briefly advantages and limitations of an immobilized flat-bed reactor:
• Homogeneous light distribution;
• No catalyst recovery system;
• Low pressure drop along the device;
• High porosity of the catalyst;
• Main limitation is the low external mass transfer needed to allow long enough contact
time with the catalyst: this is a preponderant limiting factor in absence of stirring or
even for low flowing rate (laminar flow).
2.1.5 SLURRY PHOTOREACTORS
In a slurry photoreactor (fig. 2.10) the catalyst is not immobilized, it works as a dispersed
phase inside the reaction volume. This approach was choosen to decrease the limitation of
the external mass transfer, common with immobilized photoreactors, by using a dispersed
catalyst, so allowing to have a large portion of catalyst in contact with the reactant.
Figure 2.10 Slurry photoreactor.
Uv source
Liquid outlet Liquid inlet
Film coated catalyst
48
The main drawbacks of this technology are:
• The catalyst must be recovered by a recirculation system, otherwise it follows the flux
leaving the reactor empty;
• The radiant distribution is not homogeneous since the dispersed phase creates a non-
negligible scattering of light;
• At high catalyst load, particles can aggregate, thus decreasing catalyst-to-reactant contact
area and inducing also an anomalous light distribution.
2.2 PHOTOREACTOR MODELLING As a general information, a photoreactor can be modelled by considering 4 different sub-
models:
• A model for the radiation emission;
• A model for absorption and scattering of light inside the reaction volume;
• A fluid-dynamic model;
• A kinetic model.
However, particular type of photoreactor considered implies special considerations based on
its nature. For example, in a slurry photoreactor the attention must be focused towards a
correct model for light absorption and scattering since light will undergo a large number of
scattering events along its path, while in the case of an immobilized device external and
internal mass transfer deserve more attention.
Photoreactor design greatly differs from common chemical reactor design already described,
we can understand the reasons by looking at the main limitations:
• Radiative flux: fluid volume, composition and position of the catalyst surface play a
crucial role, since the number of photons absorbed onto the catalyst has a great
influence on the overall reaction rate;
• External mass transfer: it could be a limiting factor once the pollutant finds
difficulties to arrive near the catalyst surface, causing no charge carriers transfer from
the catalyst to the organic molecule present in solution, thus favouring recombination
and so a considerable decrease of the photodegradation efficiency;
49
• Internal mass transfer: this parameter strictly relates to the morphology and topology
of the catalyst material. It depends in fact on surface porosity and film thickness, i.e
on the ability of the pollutant to travel through the catalyst pores and find free active
sites where it can be oxidized/reduced.
2.2.1 RADIATIVE MODEL
Modelling light distribution inside a reaction volume is not an easy task. Generally, the
approach is to solve the Radiative Transfer Equation (RTE) thus obtaining the intensity of
the light beam over a certain region of space. The RTE equation comes from energy
conservation considerations, in which we account for the power dispersed by the beam
thanks to extinction phenomena, like absorption and scattering, and the power acquired
thanks to emission of bodies present along the radiative path. Neglecting the emission term,
which is negligible since photocatalysis is carried at low temperatures, the general
formulation of the RTE equation (eq. 2.17) has the following shape:
K_`(R,b)
KR= −𝛽d𝐼d(𝑠, 𝛺) +
*hi𝜎d ∫ 𝑝(𝛺m → 𝛺)𝐼d(𝑠, 𝛺m)𝑑𝛺m
hi+ (2.17)
Where:
s= generic 1-D coordinate
𝐼d= spectral radiance (/pqRN/pqR6ROr );
𝛽d= extinction coefficient = 𝜎d + 𝜅d;
𝜎d= scattering coefficient (𝑚)*);
𝜅d= absorption coefficient (𝑚)*);
p (Ω’ Ω) = phase function (function expressing the probability for a photon to be
scattered from Ω’ to Ω direction.
By integrating the spectral radiance over the solid angle Ω we get 𝐺d i.e. the spectral incident
radiation (eq. 2.18):
𝐺d(𝑥, 𝑦, 𝑧) = ∫ 𝐼d(𝑠, 𝛺)bwhibw+ 𝑑𝛺 (2.18)
50
Then, by multiplying 𝐺d by the spectral absorption coefficient 𝜅d we obtain a quantity called
Local Volumetric Rate of Photon Absorption (LVRPA) (eq. 2.19), a parameter of
fundamental importance for the correct design of a photoreactor, since its distribution along
the reaction volume allows to better understand the geometrical configuration, leading to the
maximization of photon absorption onto the catalyst surface.
𝐿𝑉𝑅𝑃𝐴 = 𝐺d𝜅d ( /pqRN/pqROT ) (2.19)
For what concerns the determination of 𝜅d, a UV-VIS spectrophotometer can be used (fig.
2.11) giving us the knowledge of the absorbance of the medium inside the photoreactor.75
Figure 2.11 UV-VIS spectrophotometer apparatus scheme.
The instrument calculates the absorbance of a solution, measured inside a quartz cell. Then,
by assuming negligible reflection, a simple equation (eq. 2.20) can be used to convert
absorbance into trasmittance:
A = -log (T) (2.20)
Once trasmittance is known, 𝜅d can be calculated from equation 2.21:
𝑘d =|.~+~�𝑙𝑜𝑔( *
�`) (2.21)
As already briefly anticipated, 𝐺d can be obtained by solving the RTE equation, to do so we
can exploit several methods as:
51
• Monte Carlo method: refers to a class of computational simulations where their
principal function is to use randomness in probabilistic problems, allowing to treat
very complex model otherwise not soluvable;
• P-1 radiation model: the P-1 method belongs to the so called “differential
approximation”, the basic idea is to represent the radiation intensity as a rapidly
converging series of spherical harmonics where P-1 stands that we consider only zero
order or first order moment of the intensity;
• Discrete ordinate method: usually in radiative transfer theory we use an intensity
function, describing radiation, dependent on spatial and angular coordinate. The
research of the solution of the problem, involves calculations over integro-
differential equations, however an exact solution still does not exist. The discrete
ordinate method allows to solve the radiative transfer problem by assuming the
discretization of the space and angular variables;
• Two flux method: the two-flux method is very common when treating isotropically
scatteringmedia to desribe radiative transfer phenomena assuming isotropic radiation
intensity; However, it is demonstrated that for complex geometries, as in the one showed in figure
2.12, a Monte Carlo simulation is preferred over the other choises.
To carry a Monte Carlo simulation we need to know the following parameters:
• Absorption and scattering coefficient of the catalyst (TiO2);
• Refractive index of the fluid inside the reactor;
• Photoreactor boundary conditions as the radiation power emitted by the light source and
the optical properties of the reactor walls;
• A scattering phase function, giving an indication of the scattering angle assumed by
photons hitting an interfase.
At the end of the simulation we obtain a plot of the spatial variation of the LVRPA over the
reactor volume (considering now as a general case an annular slurry photoreactor with the
light source along the axis of the cylinder) as we can notice in figure 2.12.
52
Figure 2.12 LVRPA distribution along the reactor cross section and axis. a) LVRPA distribution over a reactor cross section; b) LVRPA distribution along the reactor axis.
Based on the visual interpretation of the results it is possible to infer wether the shape of the
reactor should be modified, allowing a more homogeneous distribution of the LVRPA values
along the radial and axial coordinates. It is worth to mention the impossibility of measuring
directly the LVRPA experimentally, in fact we can have only an indication of it by fitting
the irradiance plot or using an actinometer.
Nowadays, the progress in software development gives us the opportunity to work with
programmes containing implementations allowing to avoid the coding of the equation
described in section 2.3.1, saving time and errors. One of them is “FRED”, an optical
engineering software dedicated to complex ray tracing from a light source with the
opportunity of carrying irradiance measurements over a defined surface. Thanks to a free
academic license kindly provided by “Photon engineering LLC” we have developed our
model according to optical parameters of the materials involved, geometrical information
and light power input directly obtained by the light source producer. However, all the
technical details about the radiant simulation will be provided in the Results section, now
the aim of the next paragraph is to reveal the physics behind the simulation carried out using
FRED.
2.2.2 PHYSICS BEHIND “FRED OPTICAL ENGINEERING” SOFTWARE
Over the last three decades a sort of revolutionary approach was developed in order to code
software allowing radiant field propagation simulation. The new approach stands its basis
on the practice to consider an arbitrary optical wave field as the superposition of coherent
53
gaussian beams. A beam of radiation is defined as “gaussian” if the intensity profile drawn
over a plane perpendicuar to the direction of propagation is equal to a gaussian distribution.
This approximation sounds good for light sources emitting with a narrow spectrum i.e. lasers
or LEDs, for which the plane wave assumption is a too rough approximation.
The propagation of gaussian rays was studied in 1969 by Arnaud and Kogelnik by
considering the so called “complex ray tracing”.
Basically we define our gaussian beam as composed by 9 rays as we can notice from the
figure 2.13 where, for sake of semplicity, only five of them are represented being the other
4 specular.
Figure 2.13 Gaussian beam decomposition.76
Arnaud showed that we can trace a gaussian mode by representing its real and imaginary
part by single geometrical rays. The imaginary components correspond to the so called
“secondary waist ray”, while the real components are displayed as the “secondary
divergence ray”. The base ray expresses the direction of propagation of the beam and counts
as 1 ray for the forward and backward direction, leading to a total of 9 geometrical lines able
to represent a “complex ray”.
The main parameters involved in the definition of a gaussian beam are described below
where also a graphical representation is described in fig. 2.14:
54
Figure 2.14 Gaussian beam.76
The beam diameter, w, is expressed by equation 2.22 which describes the position of the
beam profile with respect to distance z, in the direction of propagation.
w(z)=𝑤+�1 + (���)| (2.22)
The beam profile is defined as the position where the amplitude of the beam is decreased by
a factor of 1/e. It is quite intuitive that at z=0 the intensity of the beam shows a maximum
being w(z)=𝑤+ minimized, allowing for a higher concentration of radiation.
The Rayleigh distance ZR (eq. 2.23) is the length in correspondance of which w(z) is
increased, due to diffraction, by a factor of √2 from the waist at z=0;
𝑍� = i��r
d (2.23)
where λ is the wavelength.
The radius of curvature (eq. 2.24), R, is infinite in correspondance of the waist where the
profile of the beam is still almost flat.
R(z)= z[1+(���)|] (2.24)
As z increases, the divergence of the profile increases, the quantity ruling this is the
divergence angle θ, defined in eq. 2.25:
tan(θ) = K�K�= 𝑤+
(�/��)
���r��r
(2.25)
For what concerns the propagation, in gaussian optics we generally refer to the so called
“paraxial approximation”. The beam is considered to propagate with only a small divergence
55
angle i.e. the paraxial ray, called “secondary divergence ray” in figure 2.13, is tilted with
respect to the optical axis by a small θ angle. The gaussian beam still can be considered to
have mutually orthogonal electric and magnetic fields, however we can think their
components to vary only along a direction of propagation perpendicular to the optical axis,
here assumed to be the z direction.
Our quasi plane wave can be expressed in the space (x, y, z) by the following equation 2.26:
E (x, y, z) = u (x, y, z) 𝑒)�Z� (2.26)
being u a complex scalar function here representing the non plane wave part of the gaussian
beam, E the electric field associated to the radiation propagating along the z direction and k
the wavenumber defined as |id
. By introducing our gaussian wave inside the Helmholtz
equation, we obtain the so called “reduced equation” (eq. 2.27):
�r���r
+ �r���r
+ �r���r
− 2𝑗𝑘 ����= 0 (2.27)
Under the paraxial approximation we can consider the third term as negligible with respect
to the others, finally obtaining the “paraxial wave equation” (eq. 2.28):
�r���r
+ �r���r
− 2𝑘𝑗 ����= 0 (2.28)
Generally we consider as the cutoff parameter for the validity of the theory a value of the
angle θ of about 6 degrees.
Solutions of the above equation are the gaussian beam modes (eq. 2.29):
𝑢(𝑟, 𝑧) = 𝑢+���(�)
𝑒𝑥𝑝 �− � 6�(�)
�|− 𝑗 �𝑘𝑧 + 𝑘 6r
|�(�)− 𝜑(𝑧)�� (2.29)
This equation expresses the amplitude variation of the electric field of a gaussian beam,
while the irradiance distribution can be thought as (eq. 2.30):
𝐼(𝑟, 𝑧) = ��||𝑢(𝑟, 𝑧)|| = 𝐼+ V
���(�)
W|𝑒𝑥𝑝 �−2 V 6
�(�)W|� (2.30)
where:
c = speed of light in vacuum;
ε = vacuum permittivity;
I0 = irradiance at the waist
56
which integrated over a plane perpendicular to the direction of propagation gives us the total
radiant power P, carried by the gaussian beam (eq. 2.31):
𝑃 = i��r
|𝐼+ (2.31)
Finally it is worth to mention that the complex raytrace representation allows us to propagate
a beam exploiting ordinary geometrical optics methods, decreasing a lot the computational
power required for the simulation.77,78
57
3 METHODOLOGY
In the previous chapters the most important principles lying behind the configuration of a
photocatalytic reactor, i.e., photocatalyst production and reactor design, were introduced.
The work described hereon focuses on the experimental setup of a photocatalytic reactor for
wastewater treatment and on the definition of a model able to simulate irradiation
chacteristics inside the reactor and calculate its efficiency, therefore providing an instrument
to better refine operating parameters and eventually improve the overall reactor efficiency.
In the following section all the experimental processes are explained in details.
Two types of test were performed, being the first one a preliminary test to obtain information
for the design and the production of the reactor where the second test takes place.
In both cases, the experiment consists of immersing a TiO2 sample in an aqueous solution
containing Acid Orange 7 (AO7) and measure its absorbance as indicative parameter to
calculate the photocatalytic degradation induced by a light radiation.
The two series of tests differ by the experimental apparatus (design, size of cells and light
sources).
In the first part the production of the samples and reactors configuration will be described,
the following sections are dedicated to the analysis of the photocatalytic activity of TiO2
nanotubes measuring the degradation of AO7 in different conditions.
The influence of sample-light source distance, stirring and solution pollution were
investigated. As for the last part, various compounds in different concentrations were added
to the AO7 solution (NaCl, NaHCO3, NaH2PO4, NaNO3, Na2SO4, CaCl2, H2O2) and their
effect on photocatalytic activity was evaluated.
3.1 SAMPLE PREPARATION
3.1.1 POLISHING AND CLEANING
As already mentioned, surface texture and roughness have a great influence on the growth
of the nanotubes layer. This is the reason why all the samples are subjected to a polishing
treatment with a P600 SiC paper which has a double purpose, it is aimed to remove the oxide
58
layer formed on titanium in air and to produce a regular surface promoting the growth of
more aligned nanotubes during the anodizing process.
The polished sample is subsequently immersed in ethanol and ultrasonication treatment is
performed for five minutes; after further cleaning with distilled water and drying with air
flow the sample is anodized.
The aesthetical effect of the polishing and cleaning treatment on the sample is shown in
figure 3.1.
Figure 3.1 Sample pictures: a) before cleaning; b) after polishing; c) after sonication.
3.1.2 ANODIZING
In the first type of experiment 2 cm ´ 3 cm Ti samples have been used, while for the second
one 3 cm ´ 3 cm are used, maintaining a constant thickness of @ 0.5 mm.
In both cases the anodizing process is carried out in an organic electrolyte consisting in 533.3
g of ethylene glycol (C2H6O2), 3.7 g of ammonium fluoride (NH4F) and 18 g of water (H2O).
The anodization is performed with a custom pre-industrial anodizing plant by LTC Caoduro.
A titanium net is used as cathode.
A voltage applied is 45 V is applied with potential dynamic conditions (voltage ramp) and
ramp duration 2”, then it is maintained constant for 30 min.
With a such small ramp time the voltage application can be approximated to be
instantaneous.
Subsequent washing with distilled water and drying are necessary to eliminate any trace of
organic electrolyte from the sample.
(a) (b) (c)
59
3.1.3 ANNEALING
The last step of sample production is the annealing treatment, it is performed in a GEFRAN
1200 oven at temperatures of 450°C and 500°C for one or two hours. The samples are put
inside the oven when it has already reached the desired temperature and after the annealing
time the samples are pulled out and let cool down at room temperature in contact with air.
The final appearance of the sample is shown in the figure below (fig. 3.2):
Figure 3.2 Anodized and annealed 3 cm x 3 cm sample.
3.2 PHOTOCATALYTIC REACTORS The initial series of tests is performed by leaving the samples for six hours immersed in 25
ml of a AO7 aqueous solution 2.5 ´ 10-5 M in beakers made by SCHOTT DURAN glass
with a capacity of 100 ml and radiating them with a UV source.
The source of light is a OSRAM lamp, with a power of 300 W, radiating the entire spectrum
of sunlight (fig. 3.3).
Figure 3.3 Emitting spectrum of OSRAM lamp.
60
Five beakers are placed under the lamp in five points irradiated with the same light intensity
of 3000 µW/cm2 measured using a KONIKA MINOLTA radiometer.
The beakers are covered with pure quartz plates to avoid evaporation of the solution, quartz
is used because it is transparent to the UV-vis radiation and so it does not absorb energy.
Four beakers contain both TiO2 samples (3 cm ´ 2 cm) and AO7 solutions while the fifth
one, containing only the solution, is used as reference.
The main problems of this apparatus are the heat generated by the lamp that affects the
photocatalytic process, the lack of a stirring system and the poor accuracy of the system. In
fact small movements of the lamp and/or beakers position cause significant differences on
the results due to proximity of a high power light source of big dimensions, and therefore
the large influence of exact distance on actual light intensity. To overcome these problems
two identical batch reactors are fabricated (fig. 3.4).
Figure 3.4 Images of reactors at the beginning (left) and at the end (right) of the photodegradation test with AO7.
The reactor is composed by the same type of SCHOTT DURAN beaker used before, a TiO2
sample 3 cm ´ 3 cm and a UV LED NICHIA NCSU033B with radiation peak at 365 nm
(see specifications in table 1 and table 2).
61
The beaker is covered with a 3D printed PLA piece which also supports the LED, while two
strips of Ti grid are used to maintain the sample lifted from the bottom of the reactor where
the stirrer is activated by a VELP SCIENTIFICA ARE magnetic stirring system.
The LED poles are welded to two different cables using a Sn alloy (Sn 99.3%; Cu 0.7%) to
provide the power supply, the working conditions for the LED are set to 3.8 V and 500 mA
provided by a AIM PLH120 generator.
For the test performed using the two batch reactors here described, the duration (six hours)
and the solution used (AO7 aqueous solution 2.5 ´ 10-5 M) are the same of the previous
experimental apparatus, but this time each beaker contained 40 ml of solution. Moreover, as
above mentioned, 3 cm ´ 2 cm samples are used.
Table 1 LED absolute maximum ratings (T = 25°C).
Item Symbol Absolute Maximum Rating Unit
Forward Current IF 700 mA
Pulsed Forward Current IFP 1000 mA
Allowable Reverse Current IR 85 mA
Power Dissipation PD 3.08 W
Operating Temperature TOPR -10 ~ 85 °C
Storage Temperature TSTG - 40 ~ 100 °C
Junction Temperature TJ 130 °C
Table 2 LED initial electrical/optical characteristics (T = 25°C).
Item Symbol Condition Typ. Max Unit
Forward Voltage VF IF = 500 mA 3.8 - V
Radiant Flux Φe IF = 500 mA 450 - mW
Peak Wavelength 𝜆p IF = 500 mA 365 - nm
Spectrum Half Width ∆𝜆 IF = 500 mA 9.0 - nm
Thermal Resistance R𝜃JS - 4.4 7.3 °C/W
62
3.3 PHOTOCATALYTIC ACTIVITY As already said the organic dye used in this work is Acid Orange 7 (AO7) figure 3.5, that is
one of the most widespread in textile industry.
The initial concentration of AO7 2.5 ´ 10-5 M is obtained by diluting in 496.3 ml of distilled
water 3.7 ml of a more concentrated (3.4 ´ 10-3 M) AO7 solution.
Figure 3.5 Resonant forms of AO7.79
To evaluate the photocatalytic efficiency of TiO2 nanotubes in the degradation of AO7, the
absorbance of the organic dye is measured at regular intervals (every hour for six hours) by
a spectrophotometer SPECTRONIC 200.
Degradation is then calculated from the variation in absorbance of the solution, this is
possible because concentration and absorbance of the dye are proportional, through Beer-
Lambert equation.
In fact, absorbance (A) (eq. 3.1) is a direct measure of how much light is absorbed by the
sample.
It is defined as:
𝐴 = − 𝑙𝑜𝑔 __�
(3.1)
63
The Beer-Lambert law establishes a direct relation between the concentration of a certain
solution and its absorbance; the higher is the concentration, the higher is the absorbance as
expressed by equation 3.2:
𝐴 = 𝑙 × 𝜀 × 𝐶 (3.2)
𝑙= optical path length
𝜀= molar absorptivity of absorbing chemical species
𝐶 = concentration of absorbing chemical species
If 𝑙 and 𝜀 are considered constant, it is possible to plot a calibration curve for the absorbing
species that allows to determine its concentration only by measuring the absorbance.
Figure 3.6 shows the calibration curve of AO7 absorbance, obtained by measuring it at
different known concentrations.
Figure 3.6 Calibration curve of AO7.
Also the absorption of AO7 on TiO2 surface is evaluated because it influence the apparent
photocatalytic activity: in fact, the amount of dye absorbed by the sample or adsorbed at its
surface would not appear in absorbance measure, but it is not degraded, hence it should be
subtracted from the measured absorbance decrease before converting it into
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 1 2 3 4 5 6 7
ABSO
RBAN
CE
AO7 CONCENTRATION [10-5 M]
64
photodegradation. To do so, a sample is left immersed in the solution for several hours in
the dark and the variation of solution absorbance is calculated every hour.
AO7 absorbance spectrum is depicted in figure 3.7, it shows two bands in visible region,
with a maximum peak at 487 nm and a smaller band at 430 nm, due to the hydrazone form
and azo form of AO7, respectively.
Other two bands in the ultraviolet region located at 230 and 310 nm are attributed to the
benzene and naphthalene rings of AO7, respectively;80 these two peaks are less important in
our study because we analyze the absorbance spectrum only in the 400 nm - 500 nm range,
where the instrumental readings are more accurate.
Figure 3.7 Absorbance spectrum of AO7.81
As previously said, the variation of experimental parameters can affect the photocatalytic
efficiency of the TiO2 nanotubes and the consequent AO7 degradation.
A preliminary test in the initial experimental apparatus was performed to find the optimal
annealing parameters for the photocatalytic process, 2 cm ´ 3 cm samples annealed for 1 or
2 hours at 450°C or 500°C are used. The other tests were carried using the two batch reactors.
65
3.3.1 EFFECT OF LIGHT SOURCE-SAMPLE DISTANCE
The effect of distance variation between the UV LED and the sample active surface on the
photocatalytic activity is measured by performing five degradation tests in the batch reactors
using five different PLA LED supports which allow to modulate the source height.
The distance ranges from 3.5 cm to 5.5 cm with intervals of 0.5 cm.
3.3.2 EFFECT OF STIRRING
In order to measure the effect of solution stirring on the photocatalytic degradation, various
test without stirring or in presence of stirring at different velocities are performed; more
precisely the VELP SCIENTIFICA ARE magnetic stirring system has been set on 0 for no
stirring (static), power 1 for low stirring and power 3 for high stirring. At power 1 the fluid
velocity can be assumed as a laminar flow while at power 3 corresponds a turbulent one.
The temperature of the solutions is monitored using a Delta Ohm pH meter 8705 (with a TP
870 probe) before and during the tests.
3.3.3 EFFECT OF POLLUTANTS
The effect of various pollutants at different concentrations in the AO7 2.5 ´ 10-5 M solution
is also measured; the list of the compounds, their concentration and the pH of the solution at
the maximum pollutant concentration are reported in table 3.
The pH of the solutions is measured using a Delta Ohm pH meter 8705 (with a mettler
TOLEDO probe): for AO7 2.5 ´ 10-5 M solution it is 5.95.
66
Table 3 List of the studied pollutants, their concentrations and pH.
Pollutant Concentration pH
NaCl 250 ppm, 500 ppm,1000 ppm 6.17
NaHCO3 500 ppm, 750 ppm, 1000 ppm 8.19
NaH2PO4 125 ppm, 250 ppm, 500 ppm 6.12
NaNO3 375 ppm, 750 ppm, 1500 ppm 6.05
Na2SO4 125 ppm, 250 ppm, 500 ppm 6.08
CaCl2 50 ppm, 100 ppm, 200 ppm 6.17
H2O2 75 ppm, 150 ppm, 300 ppm 6.63
3.4 DESIGN OF THE RADIATIVE MODEL USING
“FRED OPTICAL ENGINEERING” SOFTWARE
As already anticipated, FRED allows to simulate a real radiative experiment as the one
carried in laboratories using a UV led as the light source. The aim of this section is to show
the procedure adopted to set up the simulation, following the order prescribed by the tree
folder menu of the software (figure 3.8). In fact the software requires several info to proceed
with the simulation:
• definition of the optical source;
• drawing of the geometries involved in the simulation;
• choise of the surface to carry analysis;
• definition of the materials composing the object already drawn;
• application of proper coatings over every surface crossed by the radiation;
Once all these properties were defined the simulation was run and results were collected in
graphs and tabs containing statistical reports.
67
Figure 3.8 Tree folder menù of FRED software.
3.4.1 OPTICAL SOURCE
The design of the optical source is of fundamental importance in order to carry out a
simulation as close as possible to the real experiment. To meet this aim we started by
importing the CAD file of the led, kindly provided by the producer (figure 3.9).
Figure 3.9 CAD of the light source NICHIA NCSU033B.
However, the CAD file is a simple geometry without functionality, so the light source must
also be added, as it is needed by FRED to properly propagate light beams. This was
accomplished by filing a new optical source in the software. The most important
configurations to be added are the following (fig. 3.10):
68
Figure 3.10 FRED window dialog for optical source definition.
• Location/Orientation: this command allows to decide the position of the light source with
respect to the global reference system or any other entity inside the program;
• Positions/Directions: this is the most important parameter to properly design the radiation
source, since it allows to decide the positions of rays, coming from an initial grid, simulating
the light emitting material.
Among all the possible choises rays orientation can be entered:
- manually, deciding number and orientation of rays by tailoring a surface light emitting
grid;
- by digitalizing a directivity curve from data sheets provided by the manufacturer, as in
figure 3.11;
Figure 3.11 Lambertian emission of the UV LED NICHIA NCSU033B.82
69
- using a pre-existing ray set directly supplied by the producer (figure 3.12). This procedure
automatically selects the power emitted from the light source, which in our case is 0.426 W,
coherent with the one declared on the data sheets and equal to 0.450 W.
Figure 3.12 Final aspect of the light source considering dynamic generation of rays.
• Wavelength: this command allows to select the range of wavelengths emitted by the device.
Also here there are different procedures to solve this task:
- insert manually each wavelength;
- using the command “randomly according to spectrum”, which generates ray wavelengths
according to a digitalized curve of the emission spectrum of the LED as depicted in figure
3.13.
Figure 3.13 Spectrum of the UV LED NICHIA NCSU033B.
70
3.4.2 GEOMETRY
FRED allows to construct simple geometries providing a set of primitive objects like planes,
cubes, torus, coils, or even to import a CAD file in case of more complex geometries
required. For our purpose we have designed the shape of the reactor using SOLIDWORKS,
following the technical drawing provided by the manufacturer of the beaker (figure 3.14):
Figure 3.14 Technical drawing of the beaker Schott Duran 100 ml.
All other geometries were drawn using basic tools provided by FRED. The solution was
modelled with a simple rod with proper dimensions, being a fluid contained inside a beaker.
3.4.3 ANALYSIS SURFACE
An analysis surface is a tool providing a user-defined planar grid onto which we can perform
several calculations. It has two basic functions:
• it filters rays according to user-defined prescriptions;
• it bins ray into pixel.
An analysing surface will contain a number of rays depending not only on the applied filter
used, but also on its dimension, which is fundamental to intersect and analyze all the possible
beams. For our purpose the analysis surface was tailored as the titanium sample i.e. the
71
surface of the catalyst activated by radiation. So being the dimensions of the surface known,
there is only freedom in deciding how many pixels must be putted inside the surface of
analysis (figure 3.15):
Figure 3.15 Sizing of the analysis surface.
3.4.4 MATERIALS
The material folder gives the possibility to assign to each geometrical entity a proper
material. FRED still contains a wide catalog of materials of all types, however there is also
the possibility to create new materials providing the desired optical properties (figure 3.16):
Figure 3.16 Material creation window.
72
The user can introduce real and imaginary parts of the refractive index for a user-defined
range of wavelengths, which in the present case case covers the entire spectrum of emission
of the light source. The absorption definition can be avoided once the imaginary refractive
index is already assigned, being the absorption coefficient defined by equation 3.3:83
𝛼 = hi§d¨
(3.3)
where ρ = density of the absorbing material, λ = wavelength and κ = extinction coefficient.
For what concerns this project the following materials were created:
• TiO2 rutile: we have designed this material to model the barrier layer present beneath the
nanotubes array. For that purpose we have introduced the λ dependance of the real and
imaginary part of the rutile refractive index, digitalizing two plots obtained from the database
of Filmetrics.com (fig. 3.17 and fig. 3.18);
n
Figure 3.17 Rutile real refractive index n versus lambda.
κ
Figure 3.18 Rutile extinction coefficient κ versus lambda.
1500 λ [nm] 1000 500
6.0 5.0 4.0 3.0 2.0 1.0
3.0
2.0
1.0
λ [nm]
73
• TiO2 nanotubes: this material was tailored according to the paper of Grimes and Mor 84
providing the evolution with λ of the nanotubes refractive index, which is entered into the
software upon curve digitalization (fig. 3.19):
Figure 3.19 TiO2 nanotubes refractive index vs λ.84
Unfortunately no extinction coefficient was present in literature, however the absorbance
versus λ diagram (fig. 3.20) can be easly converted into the absorption coefficient using eq.
3.4 and then inserted into the software:
Figure 3.20 TiO2 nanotubes absorbance vs λ, where open side refers to the mouth.85
74
The absorption coefficient was calculated following equation 3.4.
α = (|,~+~ª)«
(mm-1) (3.4)
where:
α = absorption coefficient;
A = absorbance;
t = sample thickness;
• Pyrex: FRED already contains optical data about the borsilicated glass composing our
reactor;
• Titanium: already present into FRED catalog
• Solution: during the experimental procedures we have used several solutions, however the
simulation was carried out using an aqueous solution of AO7 at 2.5´10)𝑀. Since no data
about its refractive index were present in literature, we have used a Mettler Toledo Refracto
30 PX (fig. 3.21) to determine the real part of the refractive index (n) of the solution at room
temperature, which was measured to be 1.3332 at 22.1°C.
𝑛GH°(2.5´10)𝑀) = 1.3332 at 22.1°C
Figure 3.21 Refractometer Mettler Toledo Refracto 30PX.
75
3.4.5 COATINGS The presence of materials between the light source and catalyst must also be acknowledged,
as it may interfere with light propagation. The coating folder contains default coatings or
even user-defined ones applicable to any surface of the drawn geometries. If the material
selection allows us to tailor refractive index, i.e. material properties of the object involved
in the light path, the coating selection determines how much an incoming intensity of
radiation will be splitted into reflection and trasmission components. Three coatings were
defined and introduced in the simulation:
• Pyrex glass: the command “general sampled coating” was selected to model the walls of
the reactor made of Pyrex glass (fig. 3.22).
Figure 3.22 Pyrex coating designed as a “general sampled coating” command.
This procedure allows the user to introduce the reflection coefficient (or the trasmission
coefficient), of the material selected, for different wavelengths. In this case the transmission
coefficient curve of the beaker, provided by the manufacturer Duran Schott, was digitalized
(figure 3.23).
76
Figure 3.23 Trasmittance of Pyrex glass.
Then by assuming R = 1 – T the reflection coefficients for all the wavelengths of interest
were determined
• coating for solution-air interface: the solution-air interface was modelled assigning a
“general sampled coating” to the solution surface. Then the reflectance curve (fig. 3.24) of
the interface air-solution was evaluated by the online tool called “reflectance calculator”
provided by filmetrics, knowing the refractive index of the solution.
Figure 3.24 Reflectance curve of air-solution interface.
Then the reflectance curve was digitalized to obtain the trasmittance by selecting T = 1 – R
for all the points digitalized from the plot.
• coating for the catalyst: for what concerns the design of the catalyst, we have selected the
command “thin film layered coating” allowing us to construct a multi-layered material. In
77
fact the oxide layer, grown above the titanium surface, is composed by a compact barrier
layer with thickness of about 200 nm plus a nanotubes array with an average length of about
6000 nm. This type of coating however is quite different from the previous one (“general
sampled coating”) since it is not necessary to introduce reflectance or trasmittance as a
function of λ because it is required to define thickness and materials composing the multi-
layered structure only (figure 3.25).
Figure 3.25 Thin film layered coating command window.
The order of stacking of the two layers is unambiguous, being easly identified by the
magnitude of the refractive index of the two materials involved, where the substrate material
is the one with the highest index of refraction i.e. the rutile phase modelling the barrier layer.
3.4.6 RAYTRACE PROPERTIES
This function is of fundamental importance during the simulation since it governs the ray
propagation once the rays emitted from the led meet an interface along their path.
The advantage of this command is the possibility to split the raytrace simulation, asking for
the desired component of rays only. For example, if only the trasmitted beam is of interest,
select the command “trasmit specular”, in this way FRED will output that component only,
thus saving computational power and time. It is worth to mention to take care in using this
command since it overrides prescriptions imposed in the coating folder. However, in the
present case, the command “allow all” was adopted for every surface present in the
simulation in order to allow propagation of all rays until extinction.
78
3.4.7 ANALYSIS COMMANDS
Before starting the simulation it is worth to mention some analysis tools which can be printed
after the simulation is completed:
• Irradiance spread function: this tool allows to generate an irradiance plot using rays filtered
by the already defined analysis surface. Moreover it provides statistical information about
irradiance distribution and average value;
• Positions spot diagram: this command generates a spot diagram showing rays positions
when intersecting the analysis surface;
• Ray summary: it outputs a ray count and total incoherent power for all rays touching every
surface created in the simulation;
• Intensity spread function: it generates a representation of the intensity distribution over the
analysis surface.
79
4 RESULTS
In this chapter the obtained experimental results will be discussed and compared with results
obtained by computational simulation.
Figure 4.1 and 4.2 shows respectively the SEM and XRD analysis of a TiO2 anodized
sample, from the first image it can be observed a homogeneous nanotubes layer with
thickness ~ 5 µm. From the XRD spectrum it is possible to see a high content of crystalline
anatase phase which means high photocatalytic efficiency, the expected presence of rutile in
the bottom compact layer at the nanotubes-metallic substrate is not detected by the
instrument.
Figure 4.1 SEM image of the titanium dioxide sample used during photodegradation experiments
Figure 4.2 XRD results of the titanium dioxide sample
80
Table 4 shows the results of adsorption tests carried in batch condition with no light source
applied onto the solution, the initial absorbance is measured at the beginning of the test and
the final absorbance after three hours.
Table 4 Absorbance values for different solutions at different time for adsorption tests in dark.
Solution Initial Absorbance Final Absorbance
AO7 (2.5 × 10)𝑀) + NaCl (1500 ppm) 0.510 0.510 AO7 (2.5 × 10)𝑀) + NaNO3 (750 ppm) 0.503 0.495 AO7 (2.5 × 10)𝑀) + NaNO3 (1500 ppm) 0.502 0.506 AO7 (2.5 × 10)𝑀) + CaCl2 (100 ppm) 0.502 0.495 AO7 (2.5 × 10)𝑀) + Na2SO4 (125 ppm) 0.500 0.494 AO7 (2.5 × 10)𝑀) + Na2SO4 (500 ppm) 0.504 0.504 AO7 (2.5 × 10)𝑀) + NaHCO3 (500 ppm) 0.503 0.492 AO7 (2.5 × 10)𝑀) + H2O2 (75 ppm) 0.501 0.495 AO7 (2.5 × 10)𝑀) 0.510 0.514 AO7 (1.25 × 10)𝑀) 0.252 0.255 AO7 (0.625 × 10)𝑀) 0.130 0.130
From the data collected it is evident the negligible adsorption of AO7 on the samples
surfaces, in spite of the large surface area presented by nanotubes and available for
adsorption used.
Since data collected after three hours show a discrepancy from data collected at zero time
lower than 2%, it can be deduced that since no adsorption is present all the results obtained
in the following tests in presence of UV light are generated from pure photodegradation
processes and the latter is not affected by the adsorption phenomena.
In order to show in a clear way photocatalytic activity, a graphical representation is used;
the typical trend of absorbance measurement during the photodegradation test is shown in
figure 4.3
81
Figure 4.3 Typical trend of absorbance during the photodegradation test.
Since Acid Orange 7 (AO7) photodegradation process follows the first order kinetics and
Beer-Lambert law directly correlates absorbance and concentration of the solution, it is
convenient to represent tests results in Abs. vs time plots, which can then be processed to
obtain the logarithm of the ratio between the current concentration ([AO7]) and the initial
concentration of AO7 ([AO70]) vs time.
As already said in paragraph 2.1.1., in this kind of plot the slope of the obtained line is the
apparent reaction rate constant k.
The equation from 4.1 to 4.4 show the mathematical steps that allow to use this approach,
starting from the reaction rate equation:
−K[GH°]KN
= 𝑘[𝐴𝑂7] → K[GH°][GH°]
= −𝑘𝑑𝑡 (4.1)
Integrating both sides of the equation:
∫ K[GH°]
[GH°][GH°][GH°�]
= −∫ 𝑘𝑑𝑡NN�
→ ∫ *[GH°]
[GH°][GH°�]
𝑑[𝐴𝑂7] = −𝑘 ∫ 𝑑𝑡NN�
(4.2)
Considering 𝑡+ = 0: 𝑙𝑛[𝐴𝑂7] − 𝑙𝑛[𝐴𝑂7+] = −𝑘𝑡 (4.3)
l𝑛 V [GH°][GH°�]
W = −𝑘𝑡 (4.4)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 1 2 3 4 5 6 7
ABSO
RBAN
CE
TIME [h]
82
Figure 4.4 shows the typical trend of AO7 degradation induced by photocatalytic properties
of TiO2 nanotubes irradiated with UV LED.
Figure 4.4 Typical photodegradation test curve for AO7.
The coefficient of determination R2 of the obtaind curve is higher than 0.99, this
demonstrates that the photocatalytic process can be accurately approximated with a first
order reaction.
Also in the following results the obtained R2 are almost always > 0.99, this proves the
accuracy of the measurements and confirms the expected theoretical results.
The relatively low R2 can be attributed to the complete degradation of the dye in the solution,
this leads to a measured absorbance equal to 0 and consequently the logarithm on the y-axis
tends to –∞, this is the aspect that distorts the linear trend.
R² = 0.9983
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
83
4.1 OPTIMIZATION OF ANNEALING PARAMETERS As said in section 1.6.3, both the annealing time and temperature have influence on the
photocatalytic efficiency of TiO2 nanotubes by affecting their crystalline structure.
The first series of tests performed under the solar lamp was aimed to find the optimal
annealing conditions.
Starting from the results found in literature, two different annealing temperatures are tested:
450°C and 500°C. For each temperature two treatment durations are studied: 1 h and 2 h.
The results are shown in figure 4.5 and 4.6.
Figure 4.5 Degradation curves of AO7 for samples annealed at 450°C; 1 hour and 2 hours.
450°C, 1hy = -0.198xR² = 0.993
450°C, 2hy = -0.209xR² = 0.9953
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
84
Figure 4.6 Degradation curve of AO7 for samples annealed at 500°C; 1 hour and 2 hours.
The apparent rate constants are shown in figure 4.7:
Figure 4.7 influence of annealing parameters on apparent rate constants.
The annealing treatment performed at 500°C creates in the material an optimal mixture of
anatase and rutile, which has a positive effect on the photocatalytic activity of TiO2
nanotubes.
500°C, 1hy = -0.214xR² = 0.9904
500°C, 2hy = -0.225xR² = 0.9923-1.6
-1.4
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
85
The effect of annealing time is related to the quantity of crystalline phase formed during the
process, the longer is the treatment time the higher is the amount of conversion from
amorphous oxide to crystalline phase and this involves a higher photocatalytic efficiency of
the sample.
Once obtained this result, only samples annealed for 2 hours at 500°C are used.
All the results in the following paragraphs are referred to samples treated this way.
4.2 DISTANCE EFFECT The effect of distance between the UV LED source and the photocatalytic surface is
measured by performing five degradation tests in the reactors using five different PLA LED
supports which allow to modulate source height.
The distance ranges from 3.5 cm to 5.5 cm with intervals of 0.5 cm; the effect on the
photodegradation rate and the relative apparent rate constants are shown in figure 4.8.
Figure 4.8 Degradation curves of AO7 for different source-sample distances.
As expected, if the source of UV light is closer to the sample, the degradation rate is faster
because a higher amount of energy is available for the photocatalytic process.
In figure 4.9 the quasi-linear correlation between the distance and the apparent rate constant
is reported.
3.5 cmy = -0.147x
4.0 cmy = -0.132x
4.5 cmy = -0.12x
5.0 cmy = -0.117x
5.5 cmy = -0.104x
-1
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
86
Figure 4.9 Effect of source-sample distance variation on the apparent rate constant.
In figure 4.10 the trend of LED irradiance vs the distance is studied using FRED, also in
this case the plot highlights a quasi-linear correlation in the range of investigation here
presented, from this result it can be derived that the reaction rate constant k and the irradiance
of the lightsource are directly proportional.
Figure 4.10 Irradiance vs sample-UV source distance
From this point, the tests of the following paragraphs are performed by imposing the distance
of 3.5 cm between the source and the sample.
y = -0.0203x + 0.2154
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
2.5 3 3.5 4 4.5 5 5.5 6 6.5 7
k[s
-1]
DISTANCE [cm]
87
4.3 STIRRING EFFECT The continuous stirring during the whole test has important effects on the photodegradation
efficiency, in fact it provides a homogeneous concentration of AO7 in the solution and thus
promotes the reaction of the organic pollutant on the sample active surface and the removal
of the by-products after the photocatalytic process occurred leaving the active sites free.
Another aspect that must be take into account is the reduction of the boundary layer, the
thickness of the latter is smaller the more the mass of the solution is stirred.
In static flow condition the boundary layer is thicker (~ 0.1 mm), and thus the presence of
oxygen on the TiO2 surface is lower (the consumed oxygen is not replaced); this leads a
higher electron-hole recombination rate due to lack of electron scavenger (O2), with
consequent negative effect on the overall photocatalytic process.
In laminar and in turbulent flow conditions the boundary layer is thinner, the transport of the
species is easier and a higher content of oxygen available to avoid the recombination, the
beneficial effects on stirring on the photocatalytic process can be seen in figure 4.11:
Figure 4.11 Degradation curves of AO7 in static, laminar and turbulent flow conditions.
Staticy = -0.1472xR² = 0.9983
Turbulenty = -0.5858x R² = 0.9675
Laminary = -0.4483xR² = 0.9924
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0 1 2 3 4 5 6 7
ln([A
O7]
/AO
7 0]))
TIME [h]
88
The obtained apparent rate constants are depicted in figure 4.12
Figure 4.12 Influence of stirring power on apparent rate constants.
For stirring power greater than 3 turbulence and bubbles are formed in the solution.
The improvement of photodegration by stirring is remarkable, even at the slightest power
the reaction rate increases by almost 300%.
Stirring also prevents possible overheating of the solution caused by the light source, in fact
the continuous mixing homogenizes the temperature and promotes heat dissipation.
The use of LED as source also helps to avoid this problem because the heat emitted by a
LED is very low compared with that emitted by a traditional lamp.
Moreover the lamp used in the first setup having a large IR component caused a high
overheating and evaporation of the solutiom.
In a test performed in the reactor with a stirring power of 3, with initial solution temperature
of 23°C, the maximum temperature detected is 29°C but such an increase has no effect on
the photocatalytic activity.70
89
4.4 REUSE OF SAMPLES AND REPEATABILITY OF TEST
The samples are also analyzed in relation to deactivation, i.e., to check their ability to be
reused without losing photocatalytic efficiency. Possible problems caused by occlusion of
active sites may occur if the catalyst has been already used, with a detrimental effect on
degradation rate.
In order to evaluate the catalyst deactivation, the same sample is subjected to a
photodegradation test in the reactor for five times; sample cleaning using distilled water is
performed between one test and another.
The results obtained are reported in figure 4.13:
Figure 4.13 Degradation curves of AO7 for the same sample subjected to 5 consecutive photodegradation tests.
Figure 4.13 clearly shows that the obtained degradation curves almost coincide, this proves
that catalyst deactivation does not occur, this allows the sample to be reused several times
without loss of photocatalytic efficiency.
The great similarity of the five curves also implies a good repeatability of tests performed in
the reactors, which is fundamental for the comparison and the study of the results.
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0 1 2 3 4 5 6 7
ln ([
AO7]
/[AO
7 0])
TIME [h]
90
4.5 EFFECT OF SOLUTION COMPOSITION The role of various inorganic pollutants present in the wastewater to be treated on the
photocatalytic process is still unclear.19,86,87
In fact some salts and relative formed ions can alter the pH of the AO7 solution with
consequences on the dye adsorption by the TiO2, or they can react directly with electrons or
holes inhibiting the photocatalytic efficiency, or they can generate reactive compounds
which favour the degradation of the organic pollutant. For this reason solution modified with
several compounds, which cause the solution to be polluted with some specific ions are
studied.
The studied compounds are listed below:
• NaCl
• NaHCO3
• NaH2PO4
• NaNO3
• Na2SO4
• CaCl2
• H2O2
All the degradation tests are carried out with stirring at power 3 to maintain a homogeneous
concentration of pollutants in the whole solution.
As already mentioned, before the beginning of the test the pH of all the solutions is
measured, the results are reported in table 3 in section 3.3.3.
Almost all the pH values should not affect the surface interactions between the pollutant and
TiO2 which has a PZC between 4,5 and 7 (see section 1.6.4), only in the solution containing
NaHCO3 has pH greater than 7 the catalyst surface will be negatively charged and thus TiO2
should repulse anionic compounds.
The different concentrations employed are determined by the quantities of inorganic
pollutants that are tipically detected in wastewaters.88
The first salt to be studied is NaCl, three solutions are prepared with different concentrations
of NaCl (250 ppm, 500 ppm and 1000 ppm) and a fourth one without NaCl (used as reference
also for the degradation test with other ions).
Results of photodegradation tests are graphically reported in figure 4.14.
91
Figure 4.14 Effect of NaCl on degradation curves of AO7.
It is clear that the presence of NaCl has a positive effect on the photocatalytic process, it is
negligible at lower concentrations (250 ppm and 500 ppm) but it become consistent at higher
ones (1000 ppm) almost doubling the reaction rate (figure 4.15).
Figure 4.15 apparent rate constants at different NaCl concentration.
No NaCl
NaCl 1000ppm
NaCl 500ppm
NaCl 250 ppm
-8
-7
-6
-5
-4
-3
-2
-1
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
0.533
0.6210.572
1.011
0.09
0.19
0.29
0.39
0.49
0.59
0.69
0.79
0.89
0.99
1.09
k [s
-1]
No NaCl NaCl 250 ppm NaCl 500 ppm NaCl 1000 ppm
92
The effect of NaHCO3 is shown in figure 4.16, it is added in the solutions with
concentrations of 500, 750 and 1000 ppm.
Figure 4.16 Effect of NaHCO3 on degradation curves of AO7.
Figure 4.17 shows the variation of k with the NaHCO3 concentration; as previously said at
NaHCO3 concentration of 1000 ppm the pH is higher than the PZC of TiO2 and thus surface
interactions between nanotubes and AO7 are inhibited, this can be a possible explanation for
the degradation rate reduction.
Figure 4.17 apparent rate constants at different NaHCO3 concentration.
NaHCO3 1000ppm
NaHCO3 750ppm
NaHCO3 500 ppm
No NaHCO3
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
0.533 0.548
0.672
0.57
0.09
0.19
0.29
0.39
0.49
0.59
0.69
0.79
k [s
-1]
No NaHCO3 NaHCO3 500 ppm NaHCO3 750 ppm NaHCO3 1000 ppm
93
The dissociation of NaH2PO4 in the solution produces the HPO42- bivalent ion and a small
amount of H2PO4- monovalent ion, their effect is shown below in figure 4.18 while the k
values are shown in figure 4.19.
Figure 4.18 Effect of NaH2PO4 on degradation curves of AO7.
According to literature,80,89 NaH2PO4 has a negative effect at low concentrations due to the
reaction of HPO42- with holes and hydroxyl radicals to form less reactive radical, but the
reason why it enhances the photocatalytic activity at high concentration is still not clear.
Figure 4.19 apparent rate constants at different NaH2PO4 concentration.
No NaH2PO4
NaH2PO4 500ppm
NaH2PO4 250ppm
NaH2PO4 125 ppm
-7
-6
-5
-4
-3
-2
-1
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
0.533
0.222
0.377
0.893
0.09
0.19
0.29
0.39
0.49
0.59
0.69
0.79
0.89
0.99
k [s
-1]
No NaH2PO4 NaH2PO4 125 ppm NaH2PO4 250 ppm NaH2PO4 500 ppm
94
NaNO3 has a positive effect on the degradation of AO7 (figure 4.20) that increase with the
increasing of the concentration.
Figure 4.20 Effect of NaNO3 on degradation curves of AO7.
The enhancement effect can be explained by the generation of highly reactive NO3• radicals
from the reaction of NO3- ions and h+. As shown in figure 4.21 at the maximum pollutant
concentration, an increase by almost 200% of k cn be reached:
Figure 4.21 apparent rate constants at different NaH2PO4 concentration.
No NaNO3
NaNO3 1500ppm
NaNO3 750 ppm
NaNO3 375 ppm
-8
-7
-6
-5
-4
-3
-2
-1
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
0.5330.577
0.652
1.008
0.09
0.19
0.29
0.39
0.49
0.59
0.69
0.79
0.89
0.99
1.09
k [s
-1]
No NaNO3 NaNO3 375 ppm NaNO3 750 ppm NaNO3 1500 ppm
95
The effect of Na2SO4 on photodegradation rate can be considered negligible, as shown in
figure 4.22 the degradation curves of solutions with different concentrations of this pollutant
are almost the same of the starting AO7 2.5 × 10-5 M solution, consequentely also the
apparent rate constants shown in figure 4.23 are almost the same.
Figure 4.22 Effect of Na2SO4 on degradation curves of AO7.
Figure 4.23 apparent rate constants at different Na2SO4 concentration.
All the pollutants studied till now dissociate to give Na+ cations, so the obtained results can
be ascribed to the respective formed anions; now the effect of CaCl2 is investigated.
No Na2SO4
Na2SO4 500 ppm
Na2SO4 250 ppm
Na2SO4 125 ppm
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
0.533 0.529
0.571
0.448
0.09
0.19
0.29
0.39
0.49
0.59
0.69
k [s
-1]
No Na2SO4 Na2SO4 125 ppm Na2SO4 250 ppm Na2SO4 500 ppm
96
The influence of calcium chloride (figure 4.24 and 4.25) is not so clear, it has a slightly
negative effect at 50 ppm and 200 ppm while it enhances the photodegradation at an
intermediate concentration. Yet, differences are moderate. Given the positive effect exerted
by Cl- ions in NaCl salt, it is possible to ascribe the negative influence to calcium ions.
Figure 4.24 Effect of CaCl2 on degradation curves of AO7.
Figure 4.25 apparent rate constants at different CaCl2 concentration.
No CaCl2
CaCl2 200 ppm
CaCl2 100 ppm
CaCl2 50 ppm
-5
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
0.533
0.475
0.641
0.471
0.09
0.19
0.29
0.39
0.49
0.59
0.69
k [s
-1]
No CaCl2 CaCl2 50 ppm CaCl2 100 ppm CaCl2 200 ppm
97
The results obtained with hydrogen peroxide (H2O2) are shown in figure 4.26 and 4.27.
Figure 4.26 Effect of H2O2 on degradation curves of AO7.
Figure 4.27 apparent rate constants at different H2O2 concentration.
No H2O2
H2O2 300 ppm
H2O2 150 ppm
H2O2 75 ppm
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0 1 2 3 4 5 6 7
ln([A
O7]
/[AO
7 0])
TIME [h]
0.533
0.1420.156
0.24
0.09
0.14
0.19
0.24
0.29
0.34
0.39
0.44
0.49
0.54
0.59
k [s
-1]
No H2O2 H2O2 75 ppm H2O2 150 ppm H2O2 300 ppm
98
Such results are quite difficult to be explained, in fact the addition of H2O2 produce hydroxyl
radicals according to equation 4.1 which have a positive effect on the photocatalytic
activity. The possible reason of the reduction of degradation efficiency can be attributed to
the formation of less reactive radicals (HO2•) as shown in eq. 4.2, but this not explain why
at maximum concentration (300 ppm) the inhibition effect decreases.
H2O2 + e− → OH• + OH− (eq. 4.1)
H2O2 + OH• → HO2• + H2O (eq. 4.2)
In the following paragraphs the experimental results will be compared with those obtained
using FRED ans MatLab simulations.
4.6 FRED SIMULATION RESULTS After having completed the set up of the optical simulation using FRED Optical engineering
software, the test was finally run, in which the software propagates rays coming from the
light source until they extinct their power. The rendering of the simulation can be seen in
figure 4.28.
Figure 4.28 Graphical representation of the simulation (the solution in orange, rays in yellow).
As already anticipated FRED provides several analyzing tools, among which the most
relevant are the irradiance spread function, the intensity spread function, the position spot
diagram and the ray summary, which will be addressed in the following pictures:
99
• Irradiance spread function (fig. 4.29)
Figure 4.29 3-D Irradiance spread function evaluated on the analysis surface coinciding with the catalyst surface.
Figure 4.30 2-D representation of the irradiance spread function.
Figure 4.31 Irradiance profile with respect to a section parallel to the x axis.
100
Figure 4.32 Irradiance profile with respect to a section parallel to the y axis.
Fig. 4.29 to 4.32 describe the distribution of radiation over the catalyst surface. The
magnitude of the irradiance is not the same over the entire analyzing surface, showing higher
values, in between 1.55 and 1.66 W/dm2, in correspondence of the middle of the catalyst
surface where rays from the light source hit perpendicularly the sample. On the corner of the
titanium sample the irradiance shows low values, between 0.6 and 0.72 W/dm2 so in order
to improve light harvesting a possible solution is to design an illumination system able to
homogenize as much as possible the light distribution over the entire catalyst surface. FRED
offers also a statistical analysis in conjunction with those plots, allowing to quantify the total
power irradiating the sample and the average irradiance per square millimiter, together with
max and min values and their position (figure 4.33)
Figure 4.33 Statistical report of the irradiance spread function.
101
• Intensity spread function: As already anticipated the intensity spread function is a measure of how much radiative flux
is concentrated on a certain solid angle, hence it is measured in W/Steradian. As for the
irradiance analysis, FRED allows to carry out an intensity distribution over a proper analysis
surface (figures 4.34 and 4.35).
Figure 4.34 3-D intensity distribution over the catalyst surface.
Figure 4.35 2-D intensity distribution over the catalyst surface.
102
The intensity distribution confirms information provided by the irradiance evaluation,
namely the necessity to adjust the light source to allow a homogeneous covering of the
catalyst surface, which appears highly inhomogeneous on the corners where the intensity is
about 11% of the intensity in the middle of the sample.
• Positions spot diagram
Figure 4.36 Example of position spot diagram.
The aim of this tool is to show the beam footprint onto the analysis surface (fig.4.36). As we
can notice the shape of the beam on the analysis surface is an ellipsis as it was immediately
after its generation near the led. The reason of this correlation is basically the absence of
scattering events along the path covered by the radiation, plausible in case of a flat plate with
immobilized catalyst batch reactor.
• Ray summary
Figure 4.37 Raytrace summary.
103
In figure 4.37 we can see a statistical report plotted by FRED at the end of the simulation,
where 999940 is the number of rays generated by the light source, becoming higher
(1000225) at the end of their path considering ray anchestry. FRED takes 52.6 seconds to
carry out the simulation, halting automatically rays below a certain threshold of energy.
4.7 FRED RESULTS VALIDATION As a further proof of the quality of the results obtained with the optical simulation, we have
set up a Matlab model of the reaction kinetics with the aim of finding the kinetic constant
already determined with the experimental procedure carried out in laboratory. The general
idea was to set up a differential equation describing the kinetics of the photodegradation
process solved using a Matlab buil-in function which solves differential equations, ODE45,
leaving unknown the value of the target parameter k (i.e. the kinetic constant). This was then
determined by a graphical procedure originally implemented in Matlab where the solution
of the differential equation was fitted to experimental data. To model the evolution in time
of the residual concentration of the reactant we have selected a decay model based on a first
order kinetics of reaction (equation 4.3), therefore a variation in reactant concentration
affects reaction rate as was actually observed in photocatalytic degradation processes.90
KYMKN
= −𝑘 × 𝐶G × 𝐼 × (G7) (4.3)
which can be rearranged as equation 4.4 where Kobs is the experimental value of the kinetic
constant.
KYMKN
= −𝐾P¶R ×𝐶G (4.4)
being:
𝐶G = concentration of the reactant A (AO7 2.5 × 10)𝑀 );
k = kinetic constant ( OT
·∗R);
I = average irradiance ( ·Or);
A= nominal surface area of the catalyst (𝑚|);
V= reactor volume (𝑚~);
𝐾P¶R = 𝑘 × I × VG7W (s-1);
104
The Matlab analysis was performed on a single selected experiment carried out in laboratory,
where the abovementioned parameters where quantified as follows:
I = 1.11 ( ·KOr );
A = 0.09 ( 𝑑𝑚|);
V = 0.04908 ( 𝑑𝑚~);
In particular the average irradiance was estimated with FRED simulation. The model was
composed by three functions and two scripts.
• Function called “myModel” (fig. 4.38)
This function defines the differential equation used to model the photodegradation process
Figure 4.38 Matlab code to define the ode describing the reaction.
• Function called “mySim” (fig. 4.39)
Figure 4.39 Second block of matlab code called “mySim”.
The block of Matlab code appearing in figure 4.39 has the aim of finding the initial reaction
constant guess, i.e. k0 which is the starting parameter of k used initially to solve the
differential equation. The initial guess was a random number reasonably close, as order of
105
magnitude, to the Kobs measured experimentally. Writing in the command window of Matlab
the name of the function followed by the value of the initial guess (fig. 4.40), the software
overlaps to a plot of experimental data a trending line corresponding to the solution of the
differential equation, having as k the guess value. Then after several iterations it is possible
to find (fig. 4.41) the most correct value of k0 for which the differential equation better
interpolates experimental data, i.e. 0.07. This optimized initial guess is then further corrected
as described in next paragraphs.
Figure 4.40 Call of the “mySim” function from the command window.
Figure 4.41 Experimental data (red circles) vs simulation trend (blu line) with k0=0,06 on the right and k0=0,07 on the left. Note the better fitting using 0.07.
106
• Function called “myObj”:
Figure 4.42 Objective function definition.
The objective function (fig. 4.42) is the function that tries to minimize the sum of the squared
differences between the predicted values obtained by solving the differential equation and
the actual values expressed by the experimental data.
• Script called “myConf_Int”:
This script (fig. 4.43) fixes boundary values of k selecting a confidence interval where the
objective function will be minimized. We have assigned as the lower boundary value 0.05
while for the upper 0.15, so the final estimation of k will be in between those two values, in
accordance with our starting initial guess.
Figure 4.43 Script for the creation of a confidence interval where the k parameter will be estimated.
107
• Script called “myEstimate”:
Figure 4.44 Script containing the functions fminsearch and fmincon used to minimize the function “myObj”.
In this last code (figure 4.44) we notice the two functions “fminsearch” and “fmincon”
which can be used individually to minimize the function called “myObj”. Among the two,
fmincon necessitates the initial guess k0 = 0.07 and boundary values of the confidence
interval setted to 0.05 and 0.15. Upon function calling from the command window the
program prints the final result of reaction constant estimated by this Matlab code with the
help of the FRED simulation as we can notice from figure 4.45 and 4.46.
Figure 4.45 Final result of the matlab simulation printed on command window where k is evaluated with both fminsearch and fmincon Matlab functions.
108
Figure 4.46 Final plot experimental data (in red) and simulated profile (in blue).
Figure 4.47 Final result of the simulation.
As we can notice from figure 4.47, the Kobs value found with this model (0.1450) is in good
agreement with the one evaluated from experimental results i.e. 0.1472 with an error of
approximately 1%. Thanks to the good agreement of the experimentally found Kobs and the
counter part obtained with the already mentioned model, it was demonstrated the validity of
the assumption of first order kinetics and also the quality of the average irradiance calculated
using FRED.
109
5 CONCLUSION In this work the photocatalyic degradation of Acid Orange 7 using TiO2 is investigated,
starting from nanotubes production by anodizing titanium in organic electrolyte and
subsequent annealing treatment. Two different experimental setups are used during the entire
work, the first one has a lamp irradiating the whole solar spectrum as light source and it is
used to evaluate the photodegradation of AO7 in aqueous solution by using TiO2 samples
annealed at different temperature for different time.
The best results are obtained with samples annealed at 500°C for 2 hours thanks to the higher
crystallinity and the optimal mix of anatase and rutile phases.
Once obtained the best annealing parameters, the successive tests are performed by using
optimized samples and the second setup is assembled, the source is a UV LED that allows
higher repeatability of tests and avoid overheating and evaporation of the solution (critical
with solar lamp).
Different photocatalytic tests are performed and the effects of various parameters are
investigated; the experimental results are compared with the ones obtained using two
simulation software; the first one called FRED is used to model and simulate the irradiance
of the UV source, while MatLab is used to calculate the trend of degradation reaction kinetic.
The first order kinetic of AO7 photodegradation process is experimentally proved by
irradiating the TiO2 sample immersed in the dye solution.
A linear dependence from the UV source-sample distance is measured for the photocatalytic
efficiency by performing different tests by varying the height of the LED.
The effect of stirring of the AO7 solution is studied, a positive effect on degradation rate is
detected in both laminar and turbulent flow regimes leading to total degradation of the dye
in 6 hours and an increase in reaction rate by 300%.
Since dye-containing wastewater usally contains not only organic contaminants like AO7,
but also salts and other various inorganic pollutants, the effect of different inorganic
compounds on photocatalytic efficiency is investigated.
Almost all the studied compounds have a positive or a slightly negative effect on the
photodegradation process, the only one that causes a significant decrease in photocatalytic
activity is H2O2, probably because of the formation less reactive radicals.
110
The experimentally obtained results fit very well with the mathematical results, the
experimental apparent constant rate value k and the one calculated using the software show
an error ~ 1%, this demonstrate that the virtual simulation model is applicable to the real
one.
111
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