High-Throughput Structure/Function
Screening of Materials and Catalysts
under Process Conditions using
Synchrotron Radiation
A thesis submitted to the University of Manchester for the degree of
Doctor of Philosophy in the Faculty of Science and Engineering
2008
Nikolaos Tsapatsaris
Department of Chemical Engineering and Analytical Science
CONTENTS
List of Figures .............................................................................................................7
List of Tables .............................................................................................................14
List of Tables .............................................................................................................14
Abstract .....................................................................................................................15
Declaration ................................................................................................................16
Acknowledgements ...................................................................................................17
Abbreviations ............................................................................................................18
1.
Introduction and Scope of Research ...............................................................19
1.1.
Importance of Catalysis ...............................................................................19
1.2.
Combinatorial Chemistry and Catalysis ....................................................20
1.3.
High Throughput Catalysis .........................................................................21
1.4.
High Throughput Characterisation ............................................................22
1.5.
X-ray Absorption Fine Structure Spectroscopy ........................................23
1.6.
High Throughput In Situ XAS.....................................................................24
1.6.1.
Reactor Design and Reactor Infrastructure .............................................24
1.6.2.
Data Mining - Information Extraction ....................................................25
1.7.
Summary .......................................................................................................27
1.8.
Collaborators.................................................................................................28
1.9.
Additional Information ................................................................................28
2.
High Throughput Technologies .......................................................................29
2.1.
Pharmaceutical Applications .......................................................................30
2.2.
Materials Science ..........................................................................................32
2.2.1.
Superconducting Materials .....................................................................32
2.2.2.
Magnetoresistant Materials .....................................................................33
2.2.3.
Dielectric and Ferroelectric Materials ....................................................34
2.2.4.
Luminescent Materials............................................................................36
2.2.5.
Organic Reactions ...................................................................................37
2.2.6.
Polymers and Pigments...........................................................................38
2.3.
High Throughput in Catalysis Science .......................................................39
2.3.1.
Library Design ........................................................................................41
2.3.2.
Heuristic Rules........................................................................................41
2
2.3.3.
Expert Systems .......................................................................................42
2.3.4.
Design of Experiments ...........................................................................42
2.3.5.
Monte Carlo Simulations ........................................................................43
2.3.6.
Artificial Neural Networks .....................................................................43
2.3.7.
Genetic Algorithms .................................................................................43
2.4.
Library Synthesis in Catalysis .....................................................................44
2.4.1.
Impregnation and Sol Gel .......................................................................44
2.4.2.
Deposition Techniques ...........................................................................45
2.4.3.
HT Pulsed Electrodeposition ..................................................................46
2.4.4.
Split Pool Synthesis ................................................................................47
2.4.5.
Towards Rational Synthesis ...................................................................49
2.5.
Techniques for Catalyst Screening ..............................................................50
2.5.1.
Library Design ........................................................................................50
2.5.2.
Control of Gas Distribution and Composition ........................................52
2.5.3.
Temperature Control ...............................................................................52
2.5.4.
Ancillary Control ....................................................................................53
2.5.5.
Performance Measurements and Structural Characterisation .................54
2.5.6.
Measurements of Activity using Temperature .......................................55
2.5.7.
Measurements of Activity using Optical Methods .................................55
2.5.8.
Resonant Enhanced Multiphoton Ionisation (REMPI) ...........................56
2.5.9.
Gas Chromatography ..............................................................................56
2.5.10.
Mass Spectrometry .................................................................................57
2.5.11.
Infrared Spectroscopies ..........................................................................58
2.5.12.
X-ray Diffraction ....................................................................................59
2.5.13.
X-ray Absorption Spectroscopy .............................................................60
2.5.14.
X-ray Fluorescence .................................................................................62
2.6.
Library Optimisation: Data Mining and Knowledge Extraction .............65
2.6.1.
Knowledge Extraction ............................................................................65
2.6.2.
Catalyst Descriptors ................................................................................66
2.6.3.
Knowledge Extraction using XRD .........................................................67
2.6.4.
Information Harvesting using XAS and 2D Correlation ........................67
3.
3.1.
Structural Probing in Catalysis using X-rays ................................................71
X-ray Diffraction ..........................................................................................73
3
3.2.
The Photoelectric Effect ...............................................................................75
3.3.
X-ray Absorption Spectroscopy ..................................................................77
3.3.1.
Acquisition of XAS in Transmission Mode ...........................................81
3.3.2.
Acquisition of XAS in Fluorescence Mode ............................................82
3.3.3.
Acquisition of XAS in Total Reflection Mode .......................................83
3.3.4.
Acquisition of XAS using Total Electron Yield .....................................83
3.3.5.
Data Analysis - Normalisation and Background Subtraction of XAS ...84
3.3.6.
Data Analysis – Fourier transformation .................................................87
3.3.7.
Data Analysis – Modelling .....................................................................87
3.3.8.
Data Analysis –X-ray Near Edge Structure ............................................89
4.
Importance of Gold in Catalysis ......................................................................92
4.1.
Introduction...................................................................................................92
4.2.
Carbon Monoxide Oxidation with Gold .....................................................93
4.3.
In Situ Spectroscopies on Gold ....................................................................95
4.4.
Au Additives and Enhancement of CO Oxidation Activity ......................98
4.5.
Non-linear Phenomena in CO Oxidation ...................................................99
4.5.1.
Bi-stability and Oscillations in Ideal Surfaces ......................................100
4.5.2.
Isothermal and Non-Isothermal modes .................................................100
5.
High Throughput Ex Situ XAS ......................................................................102
5.1.
Array Planning and Design........................................................................102
5.2.
Catalyst Synthesis .......................................................................................102
5.3.
Library Screening .......................................................................................106
5.4.
X-ray Status Measurements with Silicon Photodiodes............................107
5.5.
X-ray Absorption Data Analysis ...............................................................108
5.5.1.
Manual Fitting of EXAFS Data ............................................................110
5.5.2.
Automatic Batch Fitting of EXAFS data ..............................................113
5.6.
6.
Advantages of 2D XA Spectra Representation ........................................120
Medium Throughput In Situ XAS.................................................................123
6.1.
System Components....................................................................................123
6.2.
Reactor Array .............................................................................................124
6.3.
Gas Supply System .....................................................................................126
6.4.
Multi – Inlet Quadrupole Mass Spectrometry .........................................128
6.5.
Positioning Control .....................................................................................129
4
6.5.1.
Hardware Infrastructure ........................................................................129
6.5.2.
Positioning Protocol of Parker Drives ..................................................131
6.5.3.
The Labview Programming Environment ............................................132
6.5.4.
Positioning Control Program ................................................................133
6.6.
Sample Preparation and Reaction Protocol .............................................136
6.7.
Station Setup ...............................................................................................138
6.8.
Medium Throughput Mass Spectrometry ................................................139
6.8.1.
Catalyst Performance ............................................................................139
6.8.2.
Oscillations During the CO oxidation over Au ....................................142
6.9.
Medium Throughput In Situ XAS Experiments ......................................146
6.9.1.
In Situ Catalyst Activation ....................................................................146
6.9.2.
Quantification of Spectral Quality ........................................................148
6.10.
7.
Conclusion ...............................................................................................151
High Throughput In Situ XAS .......................................................................153
7.1.
Control and Acquisition Software (CaAS) for X-ray Absorption
Spectroscopy ............................................................................................................154
7.1.1.
Main Program Functions ......................................................................156
7.1.2.
Software Operation using McLennan Motor Drives ............................159
7.2.
Gas and Temperature Control with Profibus ..........................................170
7.3.
Gas and Temperature Control ..................................................................172
7.4.
MS Valve Distribution Module..................................................................176
7.5.
MS Analysis Software.................................................................................179
7.6.
HT in situ X-ray Absorption Spectroscopy ..............................................181
7.6.1.
Introduction...........................................................................................181
7.6.2.
Catalyst Preparation ..............................................................................181
7.6.3.
Reduction Procedure and Sample Conditioning ...................................182
7.6.4.
XAS Experimental Setup ......................................................................183
7.6.5.
Catalyst Performance ............................................................................184
7.7.
Assessment of XAS Data Quality ..............................................................186
7.8.
XAS Analysis of Selected Samples ............................................................191
7.8.1.
XANES Analysis of the Copper Component .......................................191
7.8.2.
XANES Analysis of the Gold Component ...........................................196
7.8.3.
EXAFS Analysis ...................................................................................199
5
8.
Additional Work .............................................................................................203
8.1.
9.
2D Correlation Analysis Using Labview...................................................203
Final Considerations and Future Work .......................................................210
9.1.
Introduction.................................................................................................210
9.2.
Optimising Catalyst Synthesis ...................................................................210
9.2.1.
Precursor Drying ...................................................................................211
9.2.2.
Precursor Washing ................................................................................211
9.3.
Automated Catalyst Screening ..................................................................212
9.3.1.
Faster XAS Characterisation ................................................................212
9.3.2.
Automatic Alignment with X-ray Beam ...............................................213
9.3.3.
Automatic EXAFS Analysis .................................................................214
9.3.4.
EXAFS Data Archiving ........................................................................215
10.
Conclusion ...................................................................................................217
References ................................................................................................................218
Appendixes ..............................................................................................................232
1.
Optimisation in Catalysis ...............................................................................232
1.1.
Monte Carlo Simulations ...........................................................................232
1.2.
Neural Networks .........................................................................................234
1.3.
Genetic Algorithms in Catalysis ................................................................237
2.
Generalised 2D Correlation Analysis............................................................241
2.1.
Introduction.................................................................................................241
2.2.
Continuous Data Formalism ......................................................................241
2.3.
Formalism for Discrete Data .....................................................................243
2.4.
Analysis of Synchronous 2D Correlation Spectra ...................................245
2.5.
Analysis of Asynchronous 2D Correlation Spectra .................................246
2.6.
Complexity in Interpretation .....................................................................247
3.
Publications .....................................................................................................249
Word Count: 60449
Page Count: 251
6
LIST OF FIGURES
Figure 1.1. A 1 dimensional cut-out of the potential surface of an exergonic reaction
with and without a catalyst. The potential energy denotes the energy of the reactants
due to vibrations of chemical bonds and free electrons (∆Go the difference of the
standard Gibbs free energy between reactants and products).1 ..................................19
Figure 2.1. The HTT framework. The basis of novel product research and
development in science and industry.65 ......................................................................29
Figure 2.2. Left: Early implementation of a fully automated protein synthesis and
optimisation robot.69 Right: High throughput metalo-protein expression from the
mycobacterium genome.70 ..........................................................................................31
Figure 2.3. Left: 128 combinations of possible superconducting materials before
sintering (adapted from Hanak.83). Right: Schematic representation of material
gradient deposition with sputtering using two materials (A, B).61 .............................33
Figure 2.4. Left: Dielectric constant of various concentrations of BST with dopants.
La and Ce exhibit the highest dielectric coefficient, Right: Loss Tangent
measurements, lower values observed in W-doped candidates.90 ..............................35
Figure 2.5. Left: compositional variation on the silicon wafer deposited thin film
luminescence candidates, Right: CCD visible colour image during ultraviolet
irradiation of the library at 254nm. Y2O3: Eu exhibits the highest efficiency.94 ........36
Figure 2.6. The high throughput technology methodology when applied to HT
catalysis.65 ...................................................................................................................39
Figure 2.7. Micro-jet liquid dispensing unit for preparing catalysts. Alumina beads
are about to be impregnated with different concentrations of Pd-Pt-In.103.................45
Figure 2.8. Variable nanoparticle nucleation as a result of varying the pulsed
electrodeposition period. A deposition time of 0.5 s produces 10 nm particles.121 ....46
Figure 2.9. Schematic representation of the “S&P”synthesis method. Adapted from
Klein et al.124 ..............................................................................................................48
Figure 2.10. Left: The 96 well version of the SBS standard, reproduced from
ANSI/SBS 1- 2004, specifications. Right: Implementation of a 96 well reactor from
Watanabe.130 ...............................................................................................................51
Figure 2.11. Basic ancillary electrical control scheme. Arrows denote communication
pathway between components. ...................................................................................53
Figure 2.12. Left: Principle of MS detection, Right: Cyclohexane MS fragments.144
....................................................................................................................................57
Figure 2.13. Series of infrared spectra of CO adsorbed on a Cu-ZSM5 catalyst pellet
at three temperatures during heating. As temperature increases water desorbs.22......59
Figure 2.14. Clustered XRD patterns deduced from the individual catalysts. Cluster
1-4 denotes progressively more ordered structures (Adapted from150). .....................60
Figure 2.17. XRD and NEXAFS detailed spectra of 3 phosphor candidates.154 ........62
Figure 2.18. Left: 2D XRF image at an incident energy of 6570 eV just above the Mn
K edge with an exposure time of 1 s. Right: MnO K edge (6545 eV) shifts due to
changes in oxidation state.155 ......................................................................................63
Figure 2.20. A possible descriptor vector containing all the attributes that might be
related to the performance of a catalytic formulation.13 .............................................66
Figure 2.21. Left: 2D Synchronous correlation spectra from the first and last XANES
spectra from the TPR of Co-MCM41 catalyst in H2 (Right). The positive maxima
(red hue), correlate to the existence of two features (namely a pre-edge 7714 eV and
white line 7724 eV). Adapted from 157. ......................................................................69
7
Figure 3.1. Beamline layout of the newly built Diamond Light Source (Didcot, UK).
It is a 3rd generation synchrotron that can deliver micrometre-sized beams of
synchrotron radiation from far-infrared to X-ray photons. .........................................73
Figure 3.2. X-ray photons scattered by atoms in an ordered lattice of spacing d
interfere constructively and destructively as a function of incidence angle, as given
by Bragg’s law.164 .......................................................................................................74
Figure 3.3. X-ray diffraction patterns of cobalt in a microporous catalyst framework.
The patterns track changes in the formation of the crystalline phases from the
amorphous gel, while temperature increases.166 .........................................................75
Figure 3.4. Summary of the possible final states of an atom after being irradiated
with high energy photons. Left: Photoelectron emission, Auger decay, fluorescent
decay. ..........................................................................................................................76
Figure 3.5. Left: XPS spectrum, of Au-Co in iron oxide catalysts, over 1000eV,
Right: Higher resolution spectrum for identification of the exact binding energies
corresponding to different oxidation states of cobalt a) CoO, b)Co3O4, Au/Co3O4.167
....................................................................................................................................77
Figure 3.6. Left: No absorption (μ) from the atom, Middle: Onset of edge step,
showing maximum photon absorption, Right: Absorption steadily reducing since the
atom becomes more transparent at higher energies.171 ...............................................78
Figure 3.7. Variation in the X-ray absorption spectrum caused by interference of the
photoelectron wave vector caused by a nearby scatterer.172 .......................................79
Figure 3.8. Experimental setup for collection of absorption spectra using
transmission or fluorescent measurements. Adapted from 172. ...................................82
Figure 3.9. Transmission absorption spectrum of a pure Au catalyst (not normalised).
....................................................................................................................................84
Figure 3.10. Spline fitted on the raw absorption spectrum of a pure Au foil and the
extraction XAFS oscillations in energy. .....................................................................86
Figure 3.11. Fine structure χ(k) spectrum converted from χ(Ε) in Figure 3.10. A
Fourier transform window is chosen (e.g. 4.35 – 7.35) to exclude noise. ..................86
Figure 3.12. The Fourier Transform of k*χ(k) calculated from Figure 3.11. The
higher amplitude denotes a first shell of scattering atoms at an approximate distance
of 2 Å from the absorber. ............................................................................................87
Figure 3.13. Top Left: Normalised XAFS spectrum of an alumina impregnated
AuCl4 precursor catalyst, Top Right: Conversion into k space and Bottom: single
scattering, first shell model fitted on experimental data (N = 3.92, σ2=0.0015, R= 2.3
ű0.23). ......................................................................................................................88
Figure 3.14. Mo K-edge X-ray absorption spectrum extending 1500 eV after the
absorption edge. Near edge region (XANES) and extended fine structure
(EXAFS).184 ................................................................................................................90
Figure 3.15. XANES spectra corresponding to different oxidation states of Fe (Left)
and H2 adsorption coverage (Right).184 ......................................................................91
Figure 4.1. TEM micrograph of a very active Au on TiO2 supported catalyst. Note
that the average particle size is below 2 nm.196 ..........................................................93
Figure 4.2. Possible reaction mechanism for Au on TiO2 catalysts. It is suggested that
CO and O2 activation takes place at the perimeter and surface of the Au cluster.217 .95
Figure 4.3. XANES derived data which indicate the activation of O2 molecules by
the Au cluster of a TiO2 supported catalyst. It is proposed that the active phase of Au
is primarily zero-valent.248 ..........................................................................................96
Figure 4.4. Quasi periodic oscillations during the CO oxidation over a Pt (110)
surface. The partial pressure of CO decreases from a to c.277 ....................................99
8
Figure 4.5. Oscillations in the CO oxidation response of a Pt/Al2O3 catalyst. The
catalyst was in pellet form; an example of a non-isothermal system.298 ..................101
Figure 5.1. Synthetic pathway of wet impregnation for creating catalysts containing
different concentration of metals. Impregnation in alumina followed by drying,
washing, oxidation (if required) and reduction in H2.221 ..........................................103
Figure 5.2. Different concentrations of equal volumes of each metal chloride are
mixed and added to 200 mg powdered alumina (D1011, BASF, mesh size 200-450
m), according to the concentration matrix below. ..................................................104
Figure 5.3. Schematic representation of each well and its correspondent composition
of Cu, Pt and Au. The metal concentrations are: a = 0 wt%, b = 0.1 wt%, c = 1 wt%,
d = 5 wt% and e = 10 wt%. .......................................................................................105
Figure 5.4. Schematic representation of the impregnation steps. Left: Solutions were
created according to concentration matrix. Red, green, yellow hue indicates higher
Pt, Cu or Au concentration respectively. Centre: depositing 200 mg of alumina in the
liquid phase, Right: Final catalyst precursor array. ..................................................105
Figure 5.5. The developed system allowed the automatic acquisition of EXAFS
spectra from the 96 member array. Right: The robotic system was integrated in the
infrastructure of station 9.3, Daresbury STFC. Note the 96 cell array is tilted 45o to
allow for fluorescent photon detection. ....................................................................106
Figure 5.6. LIII X-ray absorption near edge spectra of selected catalysts. The spectra
were aligned to their corresponding absorption edges to allow comparison. (Pt LIII
11564 eV and Au LIII 11919 eV). Both metals are oxidised (edge positions shifts of:
+3 eV for Pt and + 2 eV for Au). ..............................................................................110
Figure 5.7, Corresponding spectra in κ space with their fitted model counterparts.
EXAFS region of samples D1 (aad), H8 (ddd) and E2 (ada). The plots show the
k2χ(k) function (lines) and the correspondent fit (dark shades). ...............................111
Figure 5.8. The radial distribution model (shaded lines) and experiment (lines). ....111
Figure 5.9. Colour coded real concentration matrices of Au (a) and Pt (b)
corresponding to each of the 96 well precursors. Yellow: 10%, orange 5%, red 1%,
blue 0.1%. Precursor E1 contains 0% AuCl3, and 0.1% PtCl4. ...............................116
Figure 5.10. Colour coded edge step amplitude matrices of Au (a) and Pt (b)
corresponding to each of the 96 well precursors. The correlation between edge steps
and concentrations can clearly be seen for the samples containing more Au. .........117
Figure 5.11. Colour coded first shell coordination matrices of Au (a) and Pt (b)
corresponding to each of the 96 well precursors. The average coordination numbers
of AuCl3, and PtCl4 are in good agreement with the crystallographic value of 4
(crystal structures: AuCl3164 and PtCl4302). ...............................................................118
Figure 5.12. Colour coded first shell absorber (Au or Pt) – scatterer (Cl) atomic
distance matrices of Au (a) and Pt (b) corresponding to each of the 96 well
precursors. The average atomic distances of AuCl3, PtCl4 over the entire array is 2.3
Å which are in good accordance with the crystallographic value of 2.21 Å. ...........119
(crystal structures: AuCl3164 and PtCl4302) ................................................................119
Figure 5.13. Colour map representations of Cu concentrations (a) and Cl
coordination numbers (b) for each catalyst precursor, calculated using the script
based HT analysis. (c) presents the Cu K-edge XANES spectra of precursors A7, E8,
E6, F6 and G6, and of a Cu metal foil.305 .................................................................120
Figure 5.14. Pt LIII-edge XANES spectra of samples E2 (just impregnated with
PtCl2) and E6, which contains Cu metal. Note the strong white line in this sample,
indicating the presence of Pt4+ generated by the reduction of Cu2+..........................122
9
Figure 6.1. System overview and integration in the existing infrastructure of station
9.3, Daresbury STFC. ...............................................................................................123
Figure 6.2. Mechanical drawing of the 8 fold well plate reactor used in the in situ
experiments. ..............................................................................................................124
Figure 6.3. Left: 8 – fold, sealed (Tested at 3 bar) steel reactor for in situ combined
XAFS/MS experiments. Right: Each of the eight compartments consists of one input
and one output and enables completely independent reactions to take place. ..........125
Figure 6.4. Detailed scheme of the high throughput infrastructure with
instrumentation used, communication protocols and multiple gas stream connections.
..................................................................................................................................126
Figure 6.5. User interface, developed in Labview®, for gas control of multiple
reactions. ...................................................................................................................128
Figure 6.6. Left: The PC control the drives (located underneath) and allows remote
positional control over 3 axis (XYZ stage in the background), Right: Parker servo
motor drive and wired power supply. .......................................................................131
Figure 6.7. Implementation of an identical function in four programming languages.
..................................................................................................................................133
Figure 6.8. The program flow of the positioning control system. The system can be
set in either fully automatic or manual operation. ....................................................135
Figure 6.9. User interface, developed in Labview®, for automated positioning
control over x, y, z, and theta angle. Movements are performed in synchronisation
with the acquisition of XANES spectra. ...................................................................136
Figure 6.10. TEM micrograph of a 4 wt% Au/Al2O3 and particle size distribution
(inlay). .......................................................................................................................137
Figure 6.11. Carbon monoxide conversions over an 8 hour period for the five most
active catalysts. The space velocity for each reactor was 6600 h-1 or 8250 ml g-1 h-1.
Note that Au/A and Au/T correspond to Au on Al2O3 and Au/TiO2 respectively. ...140
Figure 6.12. Carbon monoxide turnover frequencies TOF as a function of reactant
ratio and time. Note how the TiO2 based catalysts exhibited a higher efficiency in
converting CO to CO2 molecules in a leaner oxygen environment. .........................141
Figure 6.13. CO2 ion currents measured using MS during room temperature CO
oxidation over a Au/Al2O3 catalyst. Note the sudden onset of sustained oscillations
after 8.7 h during the first reaction cycle. The trace for the second reaction cycle was
obtained after interrupting the first cycle and exposing the catalyst to a stream of
10% CO in He at room temperature. The inset shows that oscillations can also be
recovered after interrupting the oscillatory reaction and flushing the reactor with He.
..................................................................................................................................142
Figure 6.14. Non linear CO conversion response of a 4wt% Au/Al2O3, recorded
using different reactant ratios. Sustained oscillations were observed at CO/O2 ratios
between 0.4 and 0.5. Note how the overall reaction rate decreases as the CO:O2 ratio
increases. ...................................................................................................................144
Figure 6.15. Dependence of CO conversion on gas phase stoichiometry. Filled circles
represent steady-state reaction rates under non-oscillatory and maximum reaction
rates under oscillatory conditions. Open circles denote minima of the oscillatory
reactions rate. The inset summarizes oscillation amplitudes (open squares) and
frequencies (filled squares) as a function of CO:O2 ratio. ........................................145
Figure 6.16. Au LIII XANES series of the Temperature Programmed Reduction of a
4 wt% Au/Al2O3 catalysts under a CO:He stream. Inset: Linear Combination
Analysis of the XANES series taken during reduction. At the end of the reduction
procedure the catalyst is composed almost entirely of zero-valent Au atoms. .........147
10
Figure 6.17. XAS spectra at various conditions. Left: Titania (TiO2) based catalyst
Right: Alumina (Al2O3) supported catalysts respectively. .......................................148
Figure 6.18. Absolute edge step values extracted from 56 XAS spectra at various
reactant conditions and plotted against corresponding nominal concentrations of the
Au catalysts. Top Inset: Standard deviation of edge step values for each catalyst.
Bottom inset: Comparison of the correlation coefficient (R2) for TiO2 and
Al2O3supported catalysts. Bright red squares and blue circles indicate TiO2 and
Al2O3 supported catalysts respectively. ....................................................................149
Figure 7.1. The overview of the last generation of an in situ HT XAS screening
system. ......................................................................................................................153
Figure 7.2. The User interface of the CaAS software. Functions are numbered and
correspond to explanations in the text. .....................................................................155
Figure 7.3. The initialisation of motor drive communications in CaAS. Numbers
correspond to explanations in the text. .....................................................................159
Figure 7.4. Setting up the movement control instructions in CaAS. Numbers
correspond to explanations in the text. .....................................................................160
Figure 7.5. Orientation setup in CaAS. Numbers correspond to explanations in the
text. ...........................................................................................................................161
Figure 7.6. The initialisation of automatic movements in CaAS. Numbers correspond
to explanations in the text. ........................................................................................162
Figure 7.7. Testing the automated movement at different orientations. ...................163
Figure 7.8. Synchronisation of CaAS with the beamline computer. Numbers
correspond to explanations in the text. .....................................................................163
Figure 7.9. Initialising the monochromator, XAS and XRD parameters. CaAS.
Numbers correspond to explanations in the text.......................................................165
Figure 7.10. The initialisation of the archiving facilities in CaAS. Numbers
correspond to explanations in the text. .....................................................................166
Figure 7.11. Manual insertion of meta-data information (“Current Cell Description”).
..................................................................................................................................166
Figure 7.12. Starting the automatic XAS, XRD data acquisition in CaAS. Numbers
correspond to explanations in the text. .....................................................................168
Figure 7.13. Absolute positioning in CaAS provides complete flexibility over the
positioning matrix. Numbers correspond to explanations in the text. ......................169
Figure 7.14. Profibus functionality faced with the OSI model. Layer 2 can be
accessed in different ways. DP, MPI, S7 Standardized Profibus messaging. S5
Messaging developed by Siemens France. ...............................................................170
Figure 7.15. The Applicom® PROFIBUS console. This program is responsible for
setting the PCI card and providing an OPC server that can be accessed from remote
client applications. ....................................................................................................171
Figure 7.16. The external and internal design of the Profibus controlled gas control
box with MFCs. The purple cable and red cable carry the Profibus and power signals
respectively. Two identical units were constructed. .................................................172
Figure 7.17. The external and internal design of the Profibus-controlled temperature
control box. A Mini 8 Eurotherm® controller was used. .........................................172
Figure 7.18 Switched input power units. Each can supply up to 1 kW of power. The
heating elements of the temperature controller are independently supplied by the top
unit. ...........................................................................................................................173
Figure 7.19. The solenoid valve control box. It contain transistor-based current
amplifiers. The box is controlled through 24 bit digital signals using a digital PCI
card from National Instruments® (Chapter 6). .........................................................173
11
Figure 7.20. The gas control interface was made in Labview. Communication with
the MFC’s is accomplished through the PROFIBUS protocol. ................................174
Figure 7.21. OPC client supplied by Applicom®. It enables remote access to all
items made available by the OPC server. The operation of 16 MFCs and 4
temperature control loops requires 48 OPC items. ...................................................174
Figure 7.22. The iTools program by Eurotherm®. It enables configuration of up to 4
PID control loops for the Mini 8 temperature controller used in the project. This
example contains one input, a control loop, one output and the associated settings of
the control loop. ........................................................................................................175
Figure 7.23. OPC client with a temperature graph (Eurotherm®). It was used to
verify the operation of the Mini 8 temperature controller. .......................................176
Figure 7.24. The prototype gas reactor cell enables 96 concurrent reactions which are
individually addressable by XAS and MS. 318 ..........................................................177
Figure 7.25. The distribution valve system. It is based on 10 Klohen® valve
modules. ....................................................................................................................178
Figure 7.26. The Labview based control system of the MS distribution valves.......179
Figure 7.27. Snapshots of the MS analysis software. The user has the ability to
visualise and concurrently analyse catalytic data of up to 96 samples. ....................180
Figure 7.28. Using the MS analysis program the user can immediately visualise in
2D and 3D and subsequently export the catalytic performance of the selected
catalysts (white denotes high activity). .....................................................................180
Figure 7.29. A 2D map of the CO conversion of 72 catalysts. One can identify that
catalysts containing Cu (A6 C6 E6, A2 C2, A9 C9 E9, and D8 F8) have considerably
higher activity. ..........................................................................................................185
Figure 7.30. X-ray absorption spectra from the Cu K edge of 24 samples (Columns
A, B). ........................................................................................................................187
Figure 7.31. X-ray absorption spectra from the Pt LIII edge from 24 samples
(Columns C, D). ........................................................................................................188
Figure 7.32. Repeat XANES spectra from the Pt LIII edge of 24 samples taken at
different beam exposure (Columns C, D). ................................................................189
Figure 7.33. The near-edge detail of X-ray absorption spectra from the Cu K edge of
24 samples (Columns A, B). .....................................................................................189
Figure 7.34. The Extended X-ray absorption spectra from the Au LIII edge from 24
samples (Columns E, F) (Reference catalysts H8, G8, H9, G9 etc). ........................190
Figure 7.35. The near-edge detail of the X-ray absorption spectra from the Au LIII
edge of 24 samples (Columns E, F). .........................................................................190
Figure 7.36. Normalised XANES spectra at the Cu K edge of reduced catalysts taken
at room temperature in He flow and standards322 from metallic Cu, Cu2O, CuO and
Cu(OH)2. Dotted lines A, B, C, D, E indicate the energy position of electronic
transitions characteristic of copper oxide species. Solid lines show the data and
dashed lines show the linear combination fitting results (Table 2). .........................192
Figure 7.37. Comparison of the catalyst activity with the Cu(OH)2 and CuO spectral
components (from LC analysis). ...............................................................................193
Figure 7.38. Normalised Cu K XANES spectra of the reduced catalyst containing
Cu: 1 wt% and Au: 1 wt% sequentially exposed to He (solid), O2 (long dash), CO:O2
(dot) and CO (short dash). Inset: Difference spectra between He and O2/ CO:O2 /CO
respectively. ..............................................................................................................194
Figure 7.39. Intensity of difference spectra (O2/ CO:O2 /CO - He) at Cu K edge
within the 1s  4pxy region of all catalysts containing Cu. .....................................195
12
Figure 7.40. Normalised XANES spectra at the Cu K edge in the pre-edge region.
The Cu: 1 wt% catalyst is under reaction conditions. The vertical lines A B C
indicate the energy position of electronic transitions characteristic of copper oxide
species. ......................................................................................................................196
Figure 7.41. AuL3 edge normalised XANES spectra of reduced catalysts in He flow
at room temperature. Reference spectra for Au is also shown. Dotted lines A and C
indicate the energy position of electronic transitions characteristic of Au. Dotted
lines B and D indicate a shift of the catalyst spectra with respect to Au foil. ..........197
Figure 7.42. Integrated area of AuL3 XANES spectra in the region of maximum
resonance (11919 eV -11945 eV). ............................................................................197
Figure 7.43. Cu-Au catalysts (Cu: 1 wt%, Au: 1 wt%) normalised XANES spectra at
the AuL3 edge under reaction conditions: reduced in He (solid), O2 (long dash),
CO:O2 (dot) and CO (short dash). The inset details the difference spectra between
O2/ CO:O2 /CO and the spectra in He at the 2p  5d region. ..................................198
Figure 7.44. Area of difference spectra (O2/ CO:O2 /CO - He) at the AuLIII edge
within the 2p  5d region of all catalysts containing gold. .....................................199
Figure 7.45. Fourier transformed k2(k) functions (lines) and the fit (points) of the
EXAFS spectra taken at the Cu K edge (a) and Au L3 edge. The spectra were fitted
using the first Cu-O (a) and Au-Au (b) backscattering paths centred at 1.9481 Å and
2.8842 Å respectively. A Hanning window determined the k-range and the
boundaries of the k2-weighted Fourier transform (FT). All spectra were fitted using
multiple k-weightings of 1, 2 and 3. .........................................................................201
Figure 8.1. The Front Panel of the 2D correlation analysis program. The program
allows 2D correlation analysis of XAS spectra. .......................................................203
Figure 8.2. Graph initialisation using the “Reinit To Default” property nodes. .......204
Figure 8.3. The first frame of the main flat sequence structure in the program. ......205
Figure 8.4. Second and last frames of the main flat sequence structure in the
program. ....................................................................................................................207
Figure 8.5. Setting up the 2D correlation spectra on the Labview® “Front Panel”. 207
Figure 8.6. Example of a Labview intensity graph. Note both axes are in energy
units...........................................................................................................................208
Figure A1.1. Langmuir-Hinshelwood kinetics and the extension of Ziff-GulariBarshad for describing CO oxidation on a catalytic surface. Σ1, Σ2 are neighbouring
sites. ..........................................................................................................................232
Figure A1.2. Left: Optimisation of catalytic active site distribution, Middle: Optimal
distribution for A/B 1:1 and Right: Optimal distribution when A/B 2:1. Adapted from
342
. .............................................................................................................................233
Figure A1.3. An example of a standard multilayer perceptron, a type of neural
network structure used in supervised learning. .........................................................235
Figure A1.4. Flow diagram representation of the Genetic Algorithm optimisation
method. .....................................................................................................................238
Figure A2.1. A black box representation of a perturbed system (Adapted 349,350). ..241
Figure A2.2. Schematic diagram of synchronous correlation spectrum (a, left) and
asynchronous correlation spectrum (b, right).3 .........................................................246
Figure A2.3, Asynchronous spectrum characteristic of a shifting band. This usually is
indicated by a “butterfly” pattern.353 ........................................................................248
13
LIST OF TABLES
Table 5.1. Measurements on commercial low cost photodiode arrays. ....................108
Table 5.2. Fitted values for coordination number N, nearest neighbour distance R and
Debye-Waller factor 2 obtained from the analysis of four spectra from selected
samples: E2 (ada), H8 (ddd), D1 (aad), taken at the Pt LIII and Au LIII edges. A
Hanning window determined the k-range and the boundaries of the k2 -weighted
Fourier transform (FT). Analysis of the spectra taken at the Au LIII and Pt LIII used a
k2 weighting in the data and fit plots. .......................................................................112
Table 6.1. The catalyst compositions of the 8 reactor array. The catalysts were
supported on TiO2 and Al2O3. ..................................................................................137
Table 6.2. Experimental Protocol (He = 100 ml/min, CO + O2 = 120 ml/min, catalyst
weight = 200 mg). .....................................................................................................138
Table 7.1.Typical subset of the archiving of data according to an xml schema. ......167
Table 7.2. The catalyst composition of the 96 member library. Samples in columns
A-F were synthesised by impregnation. ...................................................................182
Table 7.3. The average conversions of the catalysts during the screening experiment
(1.5 h). Catalysts with the same colour have identical compositions. ......................184
Table 7.4. Results from high throughput MS analysis revealed the activity of the
above catalysts. These were later selected for additional individual screening........186
Table 7.5. Linear combination (LC) fitting results of the Cu K XANES spectra of the
reduced catalysts, calculated using the ATHENA LC analysis function. ................193
Table 7.6. Fitted values for coordination number N, nearest neighbor distance R and
Debye-Waller factor 2 obtained from the analysis of EXAFS spectra from the
reduced catalysts taken at the Cu K and Au L3 edges. The spectra were fitted using
the first Cu-O, and Au-Au backscattering paths centred at 1.9481 Å and 2.8842 Å
respectively. A Hanning window determined the k-range and the boundaries of the
k2-weighted Fourier transform (FT). All spectra were fitted using multiple kweightings of 1, 2 and 3. In all cases the Debye-Waller factors were constrained to a
lower limit of 0.001 Å2. ............................................................................................200
14
ABSTRACT
Over the last decade high throughput technologies (HTT) have become the
established state of the art for materials discovery and optimisation in numerous
areas of fundamental and applied science, including industrial research and
development. The work presented here was undertaken to explore the potential of
implementing high-throughput (HT) in situ X-ray absorption spectroscopy (XAS)
with synchrotron radiation, addressing both technical hardware development and the
data processing bottleneck that high-throughput XAS may create.
The development of the infrastructure evolved in three stages: (i) a proof of concept
HT ex situ XAS apparatus for screening standard 96 well plates, (ii) a proof of
concept medium throughput in situ XAS apparatus using a 8-well reactor and a (iii)
HT in situ XAS 96 reactor system which comprises precise ancillary control,
multiple effluent gas analysis using Mass Spectrometry (MS) and XAS. The entire
system was linked directly to the beamline control server at different synchrotrons in
the UK and US and was responsible for the collection of XAS data. Labview was
used in all software control and analysis modules and enabled the scan of a variety of
library sizes, in several positions, angles, gas compositions and temperatures with
minimal operator intervention. The first steps towards analysing the comparatively
large data volume generated by this system have also been taken. HT MS analysis
software and XAS analysis scripts provided an efficient platform for quick analysis
of HT data using this system.
The system was evaluated in various XAS studies of the structural evolution of Au,
Cu, Pt mixed metallic catalysts. CO conversions with bimetallic
1 wt% Cu - 1 wt% Au was higher than any of the monometallic or trimetallic Au,
Cu, Pt combinations. For bimetallic Cu-Au systems, the presence of Cu-OH groups
in the vicinity of Au particles may favour the activation of oxygen and/or adsorption
of CO on the Au surface leading to an increase in catalytic activity. XANES studies
also showed that the active phase of Au in the mixed-metallic systems is present in
the ground state and confirm published results from previously studied pure Au
systems.
For the first time, rate oscillations in the conversion of CO over a Au/Al2O3
supported catalyst were discovered during medium-throughput screening of Au
catalysts. It is suggested that the observed rate oscillations could be rationalised in
terms of enhancements of the CO sticking coefficient on Au in the presence of subsurface oxygen species. The system is exhibiting oscillations which are surprisingly
reminiscent of oscillating Pt/Al2O3 supported catalysts suggesting mechanistic
similarities between the two systems.
15
DECLARATION
No portion of this work in this thesis, except that stated in Chapter 8, has been
submitted in support of an application for another degree or qualification at this or
any other university or other institution of learning.
Notes on copyright
(1) Copyright in text of this thesis rests with the Author. Copies (by any process)
either in full, or of extracts, may be made only in accordance with instructions given
by the Author and lodged in the John Ryland’s University Library of Manchester.
Details may be obtained from the Librarian. This page must form part of any such
copies made. Further copies (by any process) of copies made in accordance with
such instructions may not be made without the permission (in writing) of the Author.
(2) The ownership of any patents, designs, trade marks and any and all other
intellectual property rights except for the Copyright (the “Intellectual Property
Rights”) and any reproductions of copyright works, for example graphs and tables
(“Reproductions”), which may be described in this thesis, may not be owned by the
author and may be owned by third parties. Such Intellectual Property Rights and
Reproductions cannot and must not be made available for use without the prior
written permission of the owner(s) of the relevant Intellectual Property Rights and/or
Reproductions.
(3) Further information on the conditions under which disclosures and exploitation
may take place is available from the Head of Department of Chemical Engineering
and Analytical Science.
16
ACKNOWLEDGEMENTS
I would like to thank first and foremost my supervisor Sven Schroeder for his overall
support. I thank him for all his help throughout my PhD, for his vision and invaluable
scientific insight. Without him none of this would have been possible.
Our lovely and highly intelligent postdocs Angela Beesley and Norbert Weiher for
their amazing patience and great humour. I thank them for being the best postdocs
ever. Thank you guys for all the Chemistry lectures, help at the synchrotron, and
the pot noodles® at 6am in the morning in Daresbury.
Thanks also go to Murray Booth for his invaluable help in various synchrotron work
in the UK and US. Thank you Luanga Nchari, Aurelie Maubert and Helen Tatton for
your much needed help at various stages of the HTP project.
I am grateful to Moniek Tromp, Sergio Russu, Andrew Dent, Fred Mosselmans and
John Evans for their help and support and fun time we had at Station 9.3 and at
international conferences as part of the HTP project. I’d like to thank Ken Meecham
and Panos Mellas for their guidance and help with the IT Innovation packing scripts.
Many thanks to the beamline scientists Ian Harvey and Shu Hayama for their overall
support at Station 9.3, of SRS, Daresbury.
I am indebted to Nadia Leyarovska, the beamline scientist of station 12BM in the
Argonne National Laboratory, US for her tireless efforts.
I am grateful to all the staff of Daresbury SRS, Diamond Laboratories and Argonne
Photon Source APS. Without their continuous efforts it would have not been possible
to obtain an insight into our systems.
Thanks go to the technicians of our mechanical workshop in the Faraday building in
the University of Manchester for their effort on building promptly on every request.
Last but not least, I am indebted to Katja Reimann, my parents Maria and Lefteris
and my sister Christi for all the moral support, help and motivation they have always
given me.
17
ABBREVIATIONS

2D Correlation – Generalised 2D Correlation Analysis

CaAS – Control and Acquisition Software

CMR – Colossal magnetoresistance (when ΔR=aΔΒ2)

EDX – Energy Dispersive X-ray Analysis

EXAFS – Extended X-ray Absorption Fine Structure

FY – Fluorescence Yield

GA – Genetic Algorithm

HT – High Throughput

HTT – High Throughput Technologies

HTE – High Throughput Experimentation

HT XAS – High Throughput X-ray Absorption Spectroscopy

HT EXAFS - High Throughput Extended X-ray Absorption Fine Structure

LV – Labview®

NEXAFS – Near-Edge X-ray Absorption Fine Structure

NN – Neural Network

MCS – Monte Carlo Simulation

MS – Mass Spectrometry

TEM – Transmission Electron Microscopy

TEY – Total Electron Yield

UI – User Interface

VI – Virtual Instrument

XRF – X-ray Fluorescence

XAFS – X-ray Absorption Fine Structure

XANES – X-ray Absorption Near Edge Structure, Synonymous with NEXAFS

XAS – X-ray Absorption Spectroscopy

XPS – X-ray Photoelectron Spectroscopy
18
CHAPTER 1
INTRODUCTION
1. INTRODUCTION AND SCOPE OF RESEARCH
1.1. IMPORTANCE OF CATALYSIS
A catalyst is a mediator substance which opens intermediate reaction pathways with
lower activation energy Ea for a given chemical reaction.1 A catalyst temporarily
binds with the reactants and emerges entirely or largely unchanged in the end of the
reaction (Figure 1.1).2
Figure 1.1. A 1 dimensional cut-out of the potential surface of an exergonic
reaction with and without a catalyst. The potential energy denotes the energy of
the reactants due to vibrations of chemical bonds and free electrons (∆Go the
difference of the standard Gibbs free energy between reactants and products).1
The impact of a catalyst in a reaction is of major importance, since it not only makes
many reactions possible, it also improves their yield and selectivity, leading to a
tremendous impact on manufacturing, production and environmental costs.3
Catalysts and their biologically derived forms known as biocatalysts, convert
chemical precursors into the precise molecular shapes that are at the heart of many
products. In economic terms, the world turnover of production where catalysts are
19
CHAPTER 1
INTRODUCTION
being used is estimated at 3 Trillion$.4 Catalysts transform vast reservoirs of
chemical feed-stocks into products such as nylon and polyethylene polymers,
themselves the industrial starting point for thousands of products ranging from fizzy
drinks bottles and mountain-climbing rope to toys and textiles. Catalysts take part in
a multitude of chemical reactions in the synthesis of house-hold,5 agricultural,6,7 biochemical assays,8 petrochemical,9 automotive10 and pharmaceutical11,12 products.
1.2. COMBINATORIAL CHEMISTRY AND CATALYSIS
The fast and cost efficient discovery of optimised catalysis systems is of outmost
importance from an industrial point of view. In complement, catalysis science strives
to elucidate the mechanistic origin of superior catalysts. The combination of detailed
mechanistic studies with fast, efficient and proven industrial technologies is
envisaged to lead to the fast and cost-efficient creation of libraries of materials that
can potentially catalyse any given reaction.13,14
The discovery of a novel catalyst requires tedious synthesis and testing of promising
metal constituents and their combinations. Hence, the development of catalysts has
traditionally been a costly and inefficient process.
In effect, the catalyst synthetic conditions and concentrations of elements are
continuously refined until no further improvement in performance can be achieved.
This methodology is called combinatorial exploration of catalysts or otherwise
referred in a wider context as combinatorial chemistry.
More than 140 years ago Thomas Edison,15 the inventor of the light bulb, used the
combinatorial approach to find an appropriate material for a long lasting filament.
His research was based on systematic testing of thousands of different filaments until
he discovered one that lasted for more than 1500 hours and revolutionised the usage
of electric light.
Three decades later, in 1909, Alwin Mittasch16 became the father of combinatorial
catalysis by synthesising and screening thousands of catalysts and creating an
20
CHAPTER 1
INTRODUCTION
improved version of the Haber-Bosch ammonia synthesis process.17-19 The main
points of his methodology reproduced from his logbook are stated here:
(i) Catalyst research necessitates carrying out experiments with a certain number of
elements with numerous additives.
(ii) Testing at high pressure and temperatures (in situ) (as in Haber ammonia
synthesis experiments).
(iii) A very large number of test-series will be required.
Although the advantages of combinatorial methods were discovered a century ago,
Mittasch’s experiments were very slow and the development of a novel ammonia
synthesis catalyst lasted several years.
1.3. HIGH THROUGHPUT CATALYSIS
In the last three decades, advances in scientific instrumentation, in the fast automated
synthesis of materials and in the high speed parallel in situ testing, allowed the
increasing use of high throughput technologies (HTT). This has become the
collective term for the expanding range of advanced experimental and computational
tools and methodologies that enable very rapid, intelligent, parallel acquisition of
experimental data, increasing the productivity of research & development (R&D) by
orders of magnitude over traditional sequential approaches. There are now dedicated
journals such as: Journal of Combinatorial Chemistry, Combinatorial Chemistry,
Combinatorial Chemistry and High Throughput Screening.
It shall be noted, that the full exploitation of high throughput (HT) technologies has
been observed mainly in the pharmaceutical industry. New techniques helped
discover and optimise new drugs and bio-catalysts with immense economic benefits
(Chapter 2.1). Although Mittasch opened the road for combinatorial catalysis and
despite the improvements in technology and scientific equipment over the decades,
the catalysis community was apprehensive to use high throughput technologies. In
21
CHAPTER 1
INTRODUCTION
1980, however,20 the introduction of HT technologies, gave rise to a new era in
catalysis science. The improvements consisted mainly of parallelisation of catalytic
reactions and optimisation of the entire methodology from catalyst synthesis to
catalyst testing. A decade later, the first companies that enabled bespoke high speed
testing, discovery and optimisation of new materials were established.21
1.4. HIGH THROUGHPUT CHARACTERISATION
In the recent years HT methods have become more accepted in the catalytic
community and combinatorial experimentation proved a promising approach for the
discovery of new catalysts classes as well as for the optimisation of their yield and
selectivity.22-26 The development of automated characterisation methods has
facilitated the targeting, synthesis, characterisation and identification of increasingly
complex functional materials.27 There is an increasing need for methods that
incisively probe molecular and electronic properties22,28 of materials and help
determine relationships between their macroscopic function and molecular structure.
Integration of several analytical techniques under a single high throughput
experiment further enables detailed screening of candidate materials and can
significantly shorten product development time scales.22
Discovering a promising catalyst using a HT methodology typically requires
synthesis and in situ screening of sufficiently large, statistically significant libraries,
whereby each catalyst candidate is defined by a set of descriptors (e.g. its
constituents, structure, synthetic parameters) that relate to its activity, selectivity and
the reaction mechanism.29
HT catalyst screening methods reported in the literature have incorporated a
multitude of in situ or operando probes30 for determining structure/function
relationships, most notably reaction rate measurements,31 infrared spectroscopy,32
Raman spectroscopy,33 X-ray fluorescence,34 fluorescence microscopy,35 imaging
polarimetry,36 nuclear magnetic resonance (NMR)37 and X-ray diffraction (XRD).38
22
CHAPTER 1
INTRODUCTION
Especially XRD methods are currently one of the most widely explored methods for
the high-throughput screening of structure/function relationships,39,40 but their
application is intrinsically limited to crystalline samples with sufficient long-range
order. Investigations of non-crystalline phases, e.g., amorphous phases, glassy
materials, nanostructures, metal complexes or proteins in solution, as well as thin
films, interfaces and monolayers require different probes.
1.5. X-RAY ABSORPTION FINE STRUCTURE
SPECTROSCOPY
Synchrotron-based X-ray absorption fine structure (XAFS) spectroscopy has become
one of the most widely used methods to probe the local structure in gas, liquid and
solid phases without long-range order, being able to provide neighbouring atom
identity, bond-lengths, and coordination numbers.41 Compared to other methods for
the structural characterisation of non-crystalline samples (scanning probe and
electron microscopies, nuclear magnetic resonance), XAFS measurements have a
number of key advantages:
(i) high versatility through compatibility with dynamic, time-resolved in situ and in
situ measurements in environmental cells and reactors under practical process
conditions.42
(ii) facile and proven combination with simultaneous measurements using, e.g.
vibrational
spectroscopies,43
X-ray diffraction,44
electrochemical
methods,45
differential scanning calorimetry46 or rate measurements in reactors using optical
absorption,47 mass spectrometry (MS)48 or HPLC.49
(iii) proven and well-documented applicability to a very broad range of systems,
including, e.g. functional and structural materials, homogeneous50 and heterogeneous
catalysts51, biological systems (especially metalloproteins),52 thin films53 and
adsorbed
layers,54
nano-structured
materials,55
nucleation/crystallisation
phenomena,56 soft and/or amorphous matter.57
23
CHAPTER 1
INTRODUCTION
(iv) simultaneous delivery of extensive chemical information (oxidation states,
concentrations, local coordination geometries, nature of ligands) through the X-ray
absorption near-edge structure (XANES).58,59
The high intensity and small beam size, especially in 3rd generation synchrotron
sources, allow very rapid XAFS data acquisition and sample/reactor miniaturisation,
which are well matched to micro-scale high throughput screening. With suitable
ancillary techniques, both the structure and the function of a wide range of systems
can thus be dynamically monitored in situ.
The main drawback is that the technique requires synchrotron light since high photon
fluxes (>1012 ph×cm-2×s-1) are needed to obtain reasonable signal to noise ratios
(S/N). The methodology and theoretical basis of X-ray absorption spectroscopy
(XAS) which is a superset of the abovementioned techniques of XANES, NEXAFS,
XAFS, will be discussed in more detail in the techniques and methods section
(Chapter 3).
1.6. HIGH THROUGHPUT IN SITU XAS
1.6.1. REACTOR DESIGN AND REACTOR INFRASTRUCTURE
High throughput catalyst screening necessitates the development of suitable multiple
cell reactors so that a number of catalysts can be exposed in parallel to controllable
conditions. There are numerous examples of experimental apparatii60,61 that have
been used in catalysts screening. These usually employed a multitude of
characterisation techniques as mentioned briefly in Chapter 1.4. In order to create a
combined experiment with XAS characterisation and MS gas analysis of a multiple
cell reactor, several parameters have to be planned thoroughly. The development of a
parallel reactor has to be in line with the constraints that are imposed by the
particular characterisation techniques. For example, the mechanical design of the
micro-reactor, physical position of reactor with respect to X-rays, efficient control of
gas flows, high spatial resolution for addressing all the catalysts with the X-ray
24
CHAPTER 1
INTRODUCTION
beam, output gas system for concurrent MS testing, temperature control,
synchronisation with beamline computers and future compatibility with other
characterisation techniques. Finally, it is imperative to have provisions for automated
and intelligent archiving to allow future accessibility and analysis of the timeresolved data.
Current developments in the high throughput catalysis field as well as work
completed as part of this project will be presented in Chapter 2.
1.6.2. DATA MINING - INFORMATION EXTRACTION
A typical HT experiment usually produces several hundred performance and
structure related data. The creation of an appropriate hardware and analytical
infrastructure and its complexity elicits one of the bottlenecks of extracting useful
knowledge on a particular reaction.29 In particular, the analysis of XAFS and MS
data requires a considerable human effort and due to the large data body the
logistical cost is immense. Reasonable time-scales of data interpretation can only be
achieved if data acquisition and analysis becomes, at least partially, automated.
Reports on the importance of automated acquisition and analysis of catalytic
performance data using MS, for the identification of promising catalysts, will be
discussed further in Chapter 2 (HT MS analysis results in Chapter 6, 7).
Synchronisation of the MS data acquisition with an in situ structural screening
technique such as XAS will allow obtaining not only performance-related data while
a reaction proceeds but also a structural snapshot of the local environment of a
catalyst. An implementation of an automated XAS data analysis software capable of
data reduction (background subtraction, extraction of χ(k) data,
Fourier
transformation and basic first-shell fitting) will be presented in the Chapter 5.
The reaction parameters can then be optimised by varying catalyst compositions, gas
flow, temperature, performance and structural data. Other optimisation tools such as
genetic algorithms,62 and Monte Carlo simulations63 have been successfully used by
25
CHAPTER 1
INTRODUCTION
many scientists and will be discussed briefly in the library design section of Chapter
2 and in more detail in Appendix 1. Their use is recommended in possible future
extension (Chapter 9).
An important issue with the creation of a multitude of reaction data lies with the
archiving and tagging of the data. It is often the case in science that the tagging or
description of experimental data is accomplished by means of a hand written
logbook. Recorded data are then recalled and analysed for a small length of time
using the logbook for guidance. Following the initial research period recorded data
are not utilised for months or years. When the need arises to compare or
subsequently analyse archived experimental data it can be found that handwritten
logbooks can be difficult to reproduce. In addition, sharing the data across different
scientific communities becomes very difficult.
The Extensible Markup Language (XML) facilitates the storage and sharing of
scientific data across different platforms (Windows, Linux, MAC).64 An
implementation of an automated meta–data tagging system that tries to address some
of the above drawbacks when large data bodies are involved will be shown in
Chapter 7. Individual materials or catalysts are “tagged” with additional data such as
catalyst description, synthetic pathway, reaction temperature, XA spectra, XRD
patterns, MS data and others. These are subsequently saved intelligently according to
an XML schema (description of the data structure). The data are then uploaded to a
remote server which was developed by our collaborators in Southampton University,
and are then safely archived and backed up for future preservation and use.
Combining the above features into a HT framework will not only yield optimised
catalysts and facilitate the faster discovery of the mechanistic origins of catalytic
behaviour (Chapters 5, 6, 7) but it will also help future generations of scientists to
compare and analyse otherwise inaccessible data. This project has attempted to
concentrate scientific efforts to overall coordination, inspection and analysis of the
statistically important changes.
26
CHAPTER 1
INTRODUCTION
1.7. SUMMARY
Chapter 2 discusses the general advantages and applications of HT technologies and
techniques and their application in catalysis science.
Chapter 3 contains information about the theory and the analysis methodology of
X-ray absorption spectroscopy.
Chapter 4 introduces the reader to Au based catalytic systems before presenting
Chapter 5 which contains the first investigations on these systems using ex situ high
throughput XAS.
Chapter 5. As a proof of principle, model tri-metallic catalyst precursors were
synthesised and subsequently screened and analysed using in situ X-ray absorption
spectroscopy. The same positioning system was used in the case studies presented in
Chapter 5 and 6 and will be discussed in Chapter 6.
Chapter 6 contains results from a medium throughput in situ XAS screening system.
The knowledge gain from the ex situ high throughput XAS investigation (Chapter 5)
helped develop a prototype 8 well reactor and associated infrastructure which
allowed in situ screening of Au/TiO2 and Au/Al2O3 catalysts.
Chapter 7 contains the natural progression and scaling of the infrastructure to a full
high throughput XAS screening system capable of automatically collecting
simultaneous catalytic and structural data from 96 catalysts. This chapter is focused
on HT XAS acquisition and the software infrastructure: (i) the positioning software
system, (ii) software for automated analysis of the gas products, gas control,
temperature control and mass spectrometry for multiple reactors.
Chapter 8 describes software that allows the production of 2D correlation spectra.
Chapter 9 consists of an overview of the use of HT XAS and offers some possible
future applications and extensions of the developed system. Please note that each
chapter contains its own specific discussion.
27
CHAPTER 1
INTRODUCTION
Chapter 10 contains the concluding remarks.
Appendix 1 discusses the principles of optimisation algorithms as a starting point for
future work.
Appendix 2 contains the theoretical basis of the generalised 2D correlation analysis
which was used to develop the 2D correlation analysis program (Chapter 8).
1.8. COLLABORATORS
Collaboration with the Chemistry Department, University of Southampton
The developed control and acquisition infrastructure is compatible with both the high
throughput XAS and MS system created in house at The University of Manchester as
well as the Ultra High Vacuum, UHV, chamber apparatus for combined XRD, XAS
and Raman experiments, built by our collaborators at Southampton University.
Collaboration with Daresbury Synchrotron Radiation Source (2nd generation)
and Diamond Light Source (3rd generation).
The partnership with corresponding station scientists and coordinators will facilitate
the incorporation of the combined HT and MS system in the current instrumentation
of the newly built materials characterisation station.
1.9. ADDITIONAL INFORMATION
The developed software infrastructure is accessible at www.nikolaostsapatsaris.info
We thank the Engineering and Physical Sciences Research Council (EPSRC) for
financial support under grant numbers GR/S85801/01 and GR/S85818/01.
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2. HIGH THROUGHPUT TECHNOLOGIES
HTT is the collective term used for all the experimental techniques, methodologies,
infrastructure and computational basis that facilitate and increase the pace of research
and development by several orders of magnitude. It is accomplished by overall
parallelisation of state of the art synthetic methods, screening techniques and
advanced computational tools. HTTs have been increasingly used in numerous
applications in combinatorial material science, catalysis science and pharmaceutical
industry for the discovery and optimisation of thousands of substances.65 The
reduction of scale also means that smaller quantities are now needed for the synthesis
and characterisation of various systems. Not only the possibilities for new science
have expanded by covering more experimental parameters but also a higher cost
effective realisation of such ventures has been achieved.
This increase in productivity and research efficiency can be attributed to the highly
parallelised and iterative methodological approach HTTs utilise (Figure 2.1).
Design
Formulation
Characterisation
Evaluation
Data Mining, Analysis &
Optimisation
No
Satisfactory
Yes
Novel
Optimised
Product
Knowledge
Extraction
Figure 2.1. The HTT framework. The basis of novel product research and
development in science and industry.65
In industrial research and development it is common to adopt the entire HT
framework. In academia, however, it is usually possible to investigate in detail
individual segments of the cycle. The HT methodology consists of an iterative
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operation were the product is continuously improved until no further experimentation
can be justified. The parameters affecting the optimisation limit are performance,
financial expenditure and time constraints. Careful design of all the methodology
phases is essential since bottlenecks in individual steps of the cycle can lower the
throughput of the entire optimisation procedure.66-68 The cumulative advantages of
HTT have been appreciated by many industrial sectors as it made processes reliable,
efficient and accelerated the pace of research and development. The utilisation of
HTT in various industrial and scientific applications will be summarised below.
Particular emphasis will be given to the application of HTT in catalysis and the
various implementations will be discussed thereafter. The individual steps of the high
throughput framework will also be analysed in-depth with respect to catalysis
applications.
2.1. PHARMACEUTICAL APPLICATIONS
The main objective in HTT is the reduction of research and development costs and
the reduction of “lab to store” time, for which optimisation of all the intermediated
steps is essential. The pharmaceutical industry has been the first to adopt and exploit
all the features of HTT. For example, fast formulation and initial toxicological
testing of thousands of possible drugs intended for a particular disease allowed to
quickly exclude dangerous drug candidates. It was also the first sector to experience
the use of robotics for the full automatic coordination of organic reactions.69 In such
cases the whole synthetic procedure was optimised according to system throughput,
yield and purity of the products. Figure 2.2 shows an optimisation robot used in1984
for protein synthesis.
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Figure 2.2. Left: Early implementation of a fully automated protein synthesis
and optimisation robot.69 Right: High throughput metalo-protein expression
from the mycobacterium genome.70
The use of bioassays that contain thousands of different formulations and employing
HTT in the human genome project gave hope to the creation of individualised drugs
according to the genetic information of the subject.70,71 Other applications like the
formulation of combinatorial peptide libraries72,73 enabled research on improved
molecular descriptors on polypeptide constituents. In fact, the drug discovery sector
has been pioneer in creating models for the behaviour of drugs before formulation.
This is an extremely useful tool since pre-screening and size reduction of the initial
drug library can reduce total experimental effort. Modelling is based on the “similar
property principle”74-76 which states that molecules that have similar structure have
similar physicochemical properties. This assumption is largely true since identical
functions lead to similar binding properties. At first various attributes on the
constituents of a molecule are collected and their responses to a particular reaction
are analysed using HT essays. Large libraries have been built in that way, containing
information on the molecules, such as the inter-bonding angle and length, the
existence of a donor group, etc. Computer algorithms then screen the in silico
synthesised candidate molecules and extract the most promising subset. Large
savings have been accomplished in drug discovery and similar computational
methodologies have been adapted for use in other sciences. It shall be noted that the
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fast developments in pharmaceutical research result in quickly outdated software.
The pharmaceutical sector might be the biggest industrial HTT user but the pace of
computational innovation is one of the costly drawbacks of HTT.
2.2. MATERIALS SCIENCE
Competition has put modern industrial research and development teams under
immense pressure for higher success rates and reduced “R&D to shelf” time.
Nevertheless, intense competition and the economies of scale77-79 factor have proved
to be, to pharmaceutical industries, the driving force of innovation. Materials science
has recognised quite early the advantages that HT combinatorial methods have to
offer.80-82 HT research strategies accelerated the “discovery process” especially when
the materials properties were controlled by a large parameter space. Major advances
took place in the material science sector especially in the formulation and screening
phases (Figure 2.1) of the HT cycle. Strategies for the fast formulation of materials
such as “split-pool” and “parallel” synthesis will be explored further in the catalysis
section.
2.2.1. SUPERCONDUCTING MATERIALS
The first high throughput attempt of synthesising new high Tc superconducting
materials was made in 1970 by Hanak.83,84 A continuous phase-spread approach was
used to optimise the composition of alloy superconductors. The synthesis technique
used was a refinement of Kennedy’s multiple source co-sputtering for creating mixed
intermettalic species.85 Their synthetic approach, however, did not gain popularity
because of its appearance before the widespread use of advanced data analysis and
representation. In later years, a new synthetic methodology was developed by Xiang
and co-workers61 using spatially addressable arrays of samples. The technique
involved creating an array of superconducting oxide thin films by radio-frequency
(RF) sputtering. The thin films were deposited on a crystal support of either MgO or
LaAlO3. Different compositions and deposition series containing BaCO3, BiO3, CaO,
CuO, PbO, SrCO3 and Y2O3 were created61 as shown in Figure 2.3.
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Figure 2.3. Left: 128 combinations of possible superconducting materials before
sintering (adapted from Hanak.83). Right: Schematic representation of material
gradient deposition with sputtering using two materials (A, B).61
The first library contained 16 samples with an area of 4 mm2 each and a second
library of 128 samples of 2 mm2 each. They were both deposited on a single chip
with a surface area of 6.45 cm2. The chip library was then annealed at 840 oC to
allow the formation of alloys in an oxidising atmosphere. The resulting materials
were consequently screened for superconductivity by measuring the resistance as a
function of temperature. A prototype four point probe array86 was used to analyse 64
samples at a time and enabled the discovery of superconducting films of
BiSrCaCuOx, BiPbCaSrCuOx and YBa2Cu3Ox with a critical temperature of
approximately 80 K-90 K. A more in depth analysis of subsequent libraries also
revealed that superconductivity is affected not only by the stoichiometry of the metal
compositions but also by the order of deposition and the annealing temperatures.
Densities of more than 10000 compositions per cm2 were accomplished with RF
sputtering and proved the applicability of the technique in the development of novel
superconducting materials.
2.2.2. MAGNETORESISTANT MATERIALS
The above combinatorial synthesis techniques were also used in the discovery of a
new class of cobalt oxide magnetoresistive materials.87 The variation of a material’s
electrical resistance with the applied magnetic field is termed magnetoresistance, and
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is extremely useful in high density storage technology such as: reading heads in
computer hard drives and magnetic potentiometers. Colossal magnetoresistance
(CMR, ΔR=aΔΒ2) was initially discovered in magnesium based perovskite oxides
(ABO3) containing other dopants Ca, Sr, Ba and rare earth elements La, Y, Nd.87
Two identical libraries of 128 spatially addressable thin film deposited members
were synthesised on single crystals of LaAlO3. The two material libraries were
annealed and sintered under different conditions. One of the libraries showed a
decrease on the performance (change of resistance with magnetic field) again
emphasizing on the effect of synthetic variables. A four point array was used in the
screening process.86 Mixtures of cobalt with high percentage of Ca, Sr or Ba
exhibited an increase in the magnetoresistance. An interesting result was also the fact
that the synthesis of the magnetoresistive material with the best performance in bulk
showed a much higher activity compared to the deposited thin-film. False negatives,
that is when the thin-film deposited structure has a negative performance and the
bulk structures perform positively, are of major concern. Excellent performing
materials in bulk could escape discovery since the combinatorial approach wouldn’t
allow the thorough investigation of thousand of samples both in bulk and in thin film.
2.2.3. DIELECTRIC AND FERROELECTRIC MATERIALS
The major application of dielectric materials lies in the field of information
technologies. Improved thin-film insulators are essential in electronic devices and
miniaturisation is the current trend. New developments have been reported in
dynamic access memory chips (DRAM) about materials that possess much higher
dielectric coefficient than the commonly used amorphous silicon dioxide.88
RF-sputtering allowed the synthesis of a continuous gradient of ternary compositions
of Zr, Ti, Sn on a TiN coated silicon wafer. The actual compositions on particular
locations on the continuous gradient were identified by using Rutherford
backscattering spectroscopy (RBS).89 The search for the optimum thin film transistor
candidate required the screening of 30 libraries of an area 36 cm2 each completed
after one month of testing. In each library 4000 points were scanned with a Hg probe
in order to measure capacitance per unit area (F/m2) and breakdown voltage Vbr.
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Evaluation of the results revealed a strong dependency on the deposition conditions
during synthesis of Zr0.15Sn0.3Ti0.55O2-x thin films. The materials and synthetic
conditions were selected so that they are fully compatible with Si IC (integrated
circuit) fabrication technology. The development of improved materials in
semiconductor industry is somewhat less problematic given that thin-film
technologies are predominately use in the manufacturing process.
The HT approach has also been explored in ferroelectric materials science.90
Ferroelectrics are a range of materials used in frequency filters, phase shifters,
resonators, Hi-Q inductors and new microwave devices. Ba-Sr compounds have been
studied in such applications because they possess low high frequency losses and high
dielectric coefficients. Four libraries were created by doping 256 different
concentrations of Ba, Sr and TiO3 on a LaAlO3 support and using RF sputtering to
deposit the thin films. The four libraries were deposited as four quadrants on a 2.54
mm2 LaAlO3 support. The carefully controlled and consistent synthetic conditions
produced results that were a considerable improvement to conventional methods.
Non-destructive scanning-tip microwave near field microscopy was employed in the
characterisation of those materials.91 Figure 2.4 reveals that materials doped with La
and Ce have the highest dielectric coefficient and that materials doped with W
possess a lower loss tangent underpinning W-doped BawSrzTiyOx (BST) as a
promising tuneable dielectric material.
Figure 2.4. Left: Dielectric constant of various concentrations of BST with
dopants. La and Ce exhibit the highest dielectric coefficient, Right: Loss
Tangent measurements, lower values observed in W-doped candidates.90
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2.2.4. LUMINESCENT MATERIALS
Currently, despite extensive research, there are less than 100 commercial phosphors
available. In addition, there is no quantitative theory that describes the relation
between luminescence and material consistency and structure. Phosphors with high
quantum efficiency find numerous applications in cathode ray tube screens and in
improved plasma screens, field emission and electroluminescent flat-panel
displays.92 High quantum efficiency is developed when a fluorescing atom returns
from the excited to the ground state primarily by emitting light.93 Phosphors are
usually inorganic materials consisting of a polycrystalline support enriched with ions
of rare earth or transition metals.93 The rare earth metals then usually act as the
luminescence centre that emits visible light when it is excited by ultraviolet radiation
or as a sensitizer that absorbs and transfers energy to the luminescent centre. A major
discovery of a superior red phosphor was accomplished by Symyx Technologies by
applying high throughput combinatorial methodologies in phosphor materials based
on metal oxides.94 Phosphor libraries were deposited in thin films using electron
beam evaporation (EBE, a beam of high energy electrons evaporates layers of high
purity material) on silicon wafers. The libraries were doped with oxide combinations
of Al, La, Mg, Y, Sn, V, Eu, Tb, Tm, Ce and SrCO3. A total of 25000 or on average
600 chemical compositions per cm2 were laid on the wafer of Figure 2.5.
Figure 2.5. Left: compositional variation on the silicon wafer deposited thin film
luminescence candidates, Right: CCD visible colour image during ultraviolet
irradiation of the library at 254nm. Y2O3: Eu exhibits the highest efficiency.94
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The library was subsequently heated in an oxidising atmosphere to attain inter-planar
mixing of the phases. Due to the high diversity of the elements different synthetic
methods were used so that the performance of the resultant materials can be
evaluated against the conditions of synthesis. High throughput screening of
luminescent activity was achieved by using a CCD (charge coupled device) camera
while synchronously exciting the library with ultraviolet radiation at 254 nm (Figure
2.5, right). The best compounds were selected by means of highest intensity for a
particular chromaticity (e.g. red, green, blue). The best performing materials were
further doped with La, in line with combinatorial exploration. After varying the
elemental composition by several iterations, an improved phosphor compared to that
of the commercial Y1.95O3Eu0.05, was discovered with a composition of
Y0.845Al0.07La0.06Eu0.025VO4 (R1) and thus containing only 50% of the expensive rare
earth Yttrium and retaining improved chromaticity and comparable intensity. The
above samples were also structurally characterised using XRD and exhibited a single
phase material isostructural with YVO4 and with lattice parameters changes
consistent with the substitution of Y with La and Al. As in other applications of
combinatorial methodologies the comparison of activity between thin film and bulk
samples is required since there are different uses for resulting materials. For this
reason bulk quantities of the above (R1) optimised composition were synthesized and
the result showed agreement between bulk and thin film performance.
Although performance screening of thousand of phosphors is possible and is
routinely applied in research, the fundamental theoretical understanding for guiding
the initial selection of elements is lacking.92
2.2.5. ORGANIC REACTIONS
Although there are several examples in literature for a multitude homogeneous
catalytic reactions, the sole example of amplifying asymmetric autocatalysis which
explains homochirality was developed by Soai and co-workers.95 The Soai reaction is
an autocatalytic reaction which involves the alkylation of a pyrimidyl aldehyde in the
presence of a pyrimidyl alcohol which is both the catalyst and product. The reaction
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is believed to proceed through dimers (trimers, etc) that act as catalysts in the
transition state, but experimental evidence for their structure is currently missing.
The first reported structural characterisation of both solvents was achieved by in situ
extended X-ray absorption fine structure (EXAFS) technique using a high throughput
continuous flow reactor plate.96 The system was developed in-house at the University
of Manchester. The Zinc environment of diisopropylzinc was modified with a
solvent change from toluene to tetrahydrofuran. The Zn K-edge XANES spectra of iPr2Zn and Et2Zn were found to be similar compared to Me2Zn in toluene. In toluene
i-Pr2Zn is linearly bonded with two carbon atoms and a Zn-C bond distance of
1.89 Å. The same bond distance was found to increase to 2.03 Å with the
introduction of the Lewis base solvent (donation of electrons to form a covalent
bond). This coordinating feature justified the inability of tetrahydrofuran (THF) to
perform in asymmetric autocatalysis. Usually the preparation of the above reactions
is a tedious and time consuming operation and the HT methodology offered an
efficient platform for the future characterisation of this highly debated and unique
reaction.
2.2.6. POLYMERS AND PIGMENTS
A typical polymer is a multicomponent formulation containing various additives that
are intended for the optimisation of the various physical parameters. They provide
stability against the ageing or weathering process and enhance the quality and overall
performance of the product.97 Fast synthesis and screening is important and HT
combinatorial methods can be employed for accelerated a) synthesis of different
polymer compounds and b) screening performance data during the ageing process.
For example, polymerisation reactions for creating biodegradable materials have
been examined using 112 distinct copolymers.98 The library components were
structurally similar but with small variations that altered the polymer free volume,
bulkiness, flexibility and hydrophobicity. Many other recent examples exist with
respect to polymer sensors99 or molecular weight control100 demonstrating some uses
of HT polymer synthesis and screening. Potyrailo followed a new approach of
polymer synthesis and combined weathering screening.101,102
Three types of
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aromatic polymers (polycarbonate PC, poly-butylene terephthalate PBT, PC/PBT
45/55 wt%) were pigmented with two types of pigments, TiO2 and carbon black.
Samples had undergone eleven weathering conditions by using different UV
exposure times and a total of 66 pigments were created. Weathering kinetics were
analysed by observing in parallel the changes in fluorescence intensity among the
samples. Fluorescence imaging allowed the detection of initial changes to be
detected after only a few minutes or hours depending on the additives and the
polymers. It was shown that determination of colour change and thus photo
degradation screening of a polymer was accelerated by 150 times.
2.3. HIGH THROUGHPUT IN CATALYSIS SCIENCE
Chapters 2.1 and 2.2 explored some diverse applications where HTT played an
important role in increasing the pace for material discovery, synthesis, performance
analysis and quantification. The HTT framework can be adapted to suit the needs of
the catalysis science (Figure 2.6) and in the following pages catalyst discovery,
optimisation and the structure/function elucidation will be elaborated further.
Library Design
Library Synthesis
Library Testing
Evaluation
Data Mining, Analysis &
Optimisation
No
Satisfactory
Yes
Superior
Catalyst
Knowledge
Extraction
Figure 2.6. The high throughput technology methodology when applied to HT
catalysis.65
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The above framework consists of iterative optimisation cycles which are
continuously evaluated according to various criteria such as catalyst performance in
terms of selectivity and yield, or of knowledge gain on the catalytic reaction. The
ultimate goal is the exploration and intelligent recording of the parameter space in
order to obtain a level of information that would enable us to model a catalytic
reaction.
The size of the parameter space is finite and can be calculated. If we assume that
there are 50 stable elements for use in heterogeneous catalysis this equals 1225
binary, 19600 ternary, ~1010 decanary combinations according to the standard
statistical combinatorial formula (Equ 2.1).
c
C  k
 cn

ck !
 
 cn !ck  cn !
Equ 2.1
The size of the parameter space can be significantly increased by adding
compositional, structural and synthetic information and reaches the millions for
multielement mixtures of small concentration grades. In view of the size of the
parameter space even with the development of HT instrumentation only a small
fraction can be searched by parallel screening. As a consequence one of the most
important issues in combinatorial catalysis is the intelligent design of representative
libraries that will cover a high number of responses and unveil more information on a
reaction.82,103 The exploitation of accumulated knowledge from traditional catalysis
research also aids in increasing the probability of discovering novel or optimising
existing catalysts. It is essential to acknowledge that there are no accepted theories
that explain robustly how structural variations affect the activity of a catalyst.
However, there are various promising computational tools developed for the
extraction of knowledge from catalyst libraries focused at a particular reaction and
will be discussed further below.
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2.3.1. LIBRARY DESIGN
A catalyst library is a collection of catalysts which were prepared using different
concentrations of elements and different or identical synthetic pathways.29
The creation of a library can have different purposes such as a discovery or an
optimisation program.
If a discovery program is intended, the library is created ab initio, that is when little
or no information is available or when local activity maxima of previous research
must be avoided. A library with initially thousands of components is created, and
progressively becomes smaller according to the response of the catalysts. Discovery
programs are very expensive operations since they require the tedious synthesis,
testing and subsequent analysis of catalyst candidates.
An optimisation program uses a priori information such as chemical or “know-how”
deduced from the literature. Usually the starting catalyst combinations possess some
activity for the target reaction. This type of program has been primarily targeted by
many scientists using the HT catalytic methodology. However, the knowledge of
experience scientists must be mobilised when selecting of the appropriate elements
for the creation of a particular catalyst. The common concern, in both optimisation
and discovery programs, is that the libraries have to be designed in a well defined
manner if any useful additional information is to be obtained.
2.3.2. HEURISTIC RULES
Heuristic rules are all the algorithmic steps that a scientist follows to convey useful
information from the observed phenomena. These rules are generic in many
disciplines and are based upon the following principles:
Trial and Error, Make a Table, Look for a Pattern, Draw a Diagram, Restate the
Problem, Compare and Contrast Data, Account for All possibilities, Simplify the
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Problem, Break Set, Write a Mathematical Sentence, Make a Graph or Table, Make a
Model, Work Backward, Work Forward.
In catalysis science, these are used predominately in early determination of suitable
catalysts and subsequent analysis and comparison of promising candidates. For
example, concentration gradients will be chosen so that they cover the most
promising areas of the parameter space. Trial and error procedures enable the
scientist to discover a pattern and also allow the initial training of computational
algorithms. Similarly, other information such as calcination or reduction
temperatures will be taken into account so that the effects on the catalytic reaction
can be correlated and compared with the data.
2.3.3. EXPERT SYSTEMS
They are a branch of computational modelling that use heuristic information obtained
from user experience and experimental data.104 The end system contains a network of
rules that could potentially predict the performance of a catalyst according to the
input parameters. These systems received little attention due to the fact that large
datasets are needed for the rule creation and dedicated experts are required for
training the system. The increased costs and reduced reliability that accompanies the
development of such systems hindered their further progress.
2.3.4. DESIGN OF EXPERIMENTS
Other more quantitative computational tools have been used for the design of optimal
libraries. Statistical tools like “Design of Experiments” toolbox provide a way of
covering a large portion of the parameter space which results in reducing the number
of experiments.105 However, predicting where interesting leads are, using purely
statistical regression models is limiting since a catalytic reaction possesses a highly
non linear response.
Stochastic library design methods include Monte Carlo simulations, Neural
Networks and Genetic Algorithms.
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2.3.5. MONTE CARLO SIMULATIONS
Monte Carlo Simulation (MCS)106,107 is a modelling approach commonly used in
cases where a catalytic system can be simulated by rules and probabilities that model
the various reaction steps like adsorption, dissociation, surface diffusion, reactions or
desorption of reaction products. It results in a minimisation of dynamical rules that
can incorporate knowledge of the individual reaction steps and substrate properties
obtained from different studies. More information about the technique accompanied
with selected examples can be found in Appendix 1.
2.3.6. ARTIFICIAL NEURAL NETWORKS
A neural network (NN or ANN) consists of a number of similar processing units
named neurons in close analogy to the function of the biological neurons. This
network of neurons can be interconnected in different ways and the type of
architecture is called topology. Recently, ANN have been used successfully in
catalyst modelling.108-110 Kito applied a neural network for estimating catalyst
deactivation. His team developed dealuminated mordents (a type of zeolite, ABO3)
containing different Si/Al ratios and used these for methanol conversion into
hydrocarbons. The study showed agreement of the predicted first order deactivation
rate constant (k) with the experimental data.
2.3.7. GENETIC ALGORITHMS
A stochastic method that is widely used in catalyst discovery and optimisation and
has been inspired by nature is the genetic algorithm. Initially named evolutionary
algorithm (EA) by Holland,111 the Darwinian rules of natural selection and survival
of the fittest were employed as the theoretical basis of this method. In living species,
the use of genetic operators (GO’s) such as, mating, crossover and mutation, are
responsible for the selection of the individuals with the most promising performance.
Despite the valid examples in literature of discovering new improved catalysts by the
use of GA’s for the oxidative dehydrogenation (ODH) of ethane and propane
the scientific community is still apprehensive in the use of
112,113
“black box” type
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techniques (these do not provide a transparent mathematical model). This response is
due to the inability of a GA to provide quantitative information on the decision
making process or in other words extracting rules that describe the selection process
of an improved catalyst. More information about Genetic algorithms along with
selected applications can be found in Appendix 1.
2.4. LIBRARY SYNTHESIS IN CATALYSIS
Materials science has made extensive use of sputtering and deposition techniques for
the fast synthesis of luminescent, magnetoresistive and other materials, as seen in
Chapter 2.2. Currently, in catalysis science, the most widely used HT synthesis
techniques can be divided into thin-film deposition and solution based methods.
Solution based methods include impregnation, sol-gel and co-precipitation.
2.4.1. IMPREGNATION AND SOL GEL
This synthesis technique is based on impregnating a precursor solution containing the
catalytic materials on a porous powder. The amount of solution the powder can
adsorb depends on the surface area and can be calculated. The resulting wet powder
is then dried and activated usually by reduction in an appropriate atmosphere114.
Impregnation has two main advantages: (i) the surface area and mechanical
properties of the catalyst are defined by the support, which potentially leads to highly
repeatable synthesis and (ii) synthesis can be highly parallelised and automated
(Figure 2.7). Other solution methods include sol-gel synthesis which is based on the
use of a gelating agent for establishing a 3D silicon matrix. Solvent evaporation by
heating causes the matrix to shrink and activates the catalyst. The resulting catalyst
can have controlled porosity and the method has been extensively used in developing
catalysts for the hydrogenation of 1-hexene, oxidation of propylene and ODHE.115-118
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Figure 2.7. Micro-jet liquid dispensing unit for preparing catalysts. Alumina
beads are about to be impregnated with different concentrations of Pd-Pt-In.103
2.4.2. DEPOSITION TECHNIQUES
Deposition techniques are based on the creation of different materials by producing
layers. For example thin film deposition has been employed intensively in material
science using RF sputtering. Cong et al
116
has created a triangular library of 120
member family containing different amounts of Rh, Pt, Pd and Cu. Layers of these
elements were sputtered through masks onto a quartz wafer. Each of the final
deposited catalysts was of 1.5 mm diameter and 100 nm thickness and the entire
library was then annealed at 773 K and under a stream of 5% Hydrogen and inert
gas.
X-ray diffraction ensured that the deposited metal layers were properly diffused to
create metal alloys. The entire procedure required one hour and produced catalysts
with similar characteristics to ones synthesised by sol-gel methods. The advantage of
sputtering and other deposition methods such as thermal deposition, plasma
deposition, chemical vapour deposition and epitaxy is the ability to prepare a number
of sites, simultaneously and with high speed.119 In addition the libraries can be then
further processed, as seen previously, for the creation of catalytic materials.
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2.4.3. HT PULSED ELECTRODEPOSITION
High throughput pulsed electrodeposition has recently been applied successfully for
the creation of supported catalysts.120-122 Baecks created an automated synthesis
platform for the controlled deposition of Au nanoparticles on TiO2 (Figure 2.8). The
spatially addressable library of catalysts had undergone electrodeposition in a serial
fashion and subsequently was screened for photo-electrochemical activity.
Figure 2.8. Variable nanoparticle nucleation as a result of varying the pulsed
electrodeposition period. A deposition time of 0.5 s produces 10 nm particles.121
The technique has the advantage that it creates a higher density of nanoparticles than
traditional electrodeposition and results to improved mechanical and physical
properties. In addition, the lower cost makes it more attractive than chemical vapour
deposition CVD or physical vapour deposition PVD techniques.
Final Note
When catalysts are deposited, using the abovementioned methods, the screening and
testing will yield results sometimes representative of the activity of thin layered
materials, which cannot be scaled up directly to suit the needs of a manufacturing
process or of a real system. Thus, there should be always caution for false positives
when interpreting catalytic results from materials deposited from thin layers since
many catalysts depict improved performance only in the nanometre scale. These
methods are nevertheless a promising solution especially for the creation of very
large “discovery-type” libraries consisting of tenths of thousands of components.
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2.4.4. SPLIT POOL SYNTHESIS
Current methodologies in HT synthesis are based on the creation of libraries where
each of the catalysts can be addressed by its position in space. By keeping a matrix of
the synthesis steps the constituents of each of the catalysts can be known with
accuracy. In the case of the abovementioned deposition techniques, up to 40000
different compounds can be synthesised on a single 1 cm2 support. However, some of
the disadvantages are that very accurate masks have to be created to achieve high
spatial resolution and only small amounts of catalysts can be deposited per site.
A novel concept was introduced by Furka and co-workers named as “Mix & Split “or
S&P synthesis.123 Split-Pool synthesis is an intriguing technique that attempts to
overcome the above problems since it requires minimal experimental effort and
allows the facile production of millions of materials combinations. The technique
works as follows:
An amount of resin beads that includes thousands of porous spherical beads which
are homogenous in size and loading capacity, is split into a number of equal groups.
Each of the groups is then bound to a different compound (A, B, C etc) and
subsequently the groups are pooled together and homogenously mixed.
The resulting group has an amount of beads equal to the beginning but now the beads
contain aliquots A, B, C etc. This group is similarly split into a number of equal
populations and each population is mixed with compounds D, E, F etc. The groups
are then pooled and thoroughly mixed for a second time. The resulting bead mixture
will contain nine equal groups each of which will be comprise of either combinations
of AD, AE, AF, BD, BE, BF, CD, CE, CF (Figure 2.9).124
The total number combinations that can be produced after S number of split steps
and M elements is MS. In this way each bead acts as a micro-reactor enabling the
synthesis of a highly diverse library depending on the number of different
components and the number of split-pool steps. As an example Klein initially started
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with 3000 γ-Al2O3 beads which were impregnated with liquid precursors of Mo, Bi,
Co, Fe, Ni, at four different concentrations.124 In this case the total possible
combinations is 4 elemental concentrations with 5 split pool steps were each of the
precursors is being added in total: 45=1024 possibilities.
Figure 2.9. Schematic representation of the “S&P”synthesis method. Adapted
from Klein et al.124
Split pool synthesis has the advantage of easier and faster practical implementation
and the ability to create combinatorial libraries that linearly sample the parameter
space. Yet, for the fabrication of catalysts many synthetic steps are interlaced such as
drying, calcination, activation thus reducing the efficiency of the method. After each
split step, in Klein’s publication,124 there is a 16 hour delay before the next pool and
split can take place, to allow for drying of the precursor before the next impregnation
step. Clearly, there are limitations with this technique, yet it remains an interesting
synthetic methodology, especially in cases where the intermediate split and pool
steps are not time consuming.
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2.4.5. TOWARDS RATIONAL SYNTHESIS
There has been much research in the field of catalyst synthesis. Different methods
have been engaged in the battle for more efficient, high speed and repeatable
production of catalyst formulations. However, the complex behaviour of catalysts
makes them prone to variations in activity by slight changes of the synthetic
parameters. Predicting the catalytic performance before physical synthesis is of
outmost importance for the catalysis community and in the past few years research in
de novo predictions has started to gain momentum. Successful attempts in predicting
and controlling the shape and micropore size of zeolites have already been
reported.125 A Monte Carlo approach has been applied by Draznieks
126
for the de
novo prediction of inorganic structures by using secondary building units (SBU). The
use of this technique immensely reduced the number of possible structure types that
could possible exist in nature.
More fundamental views of synthesis have been expressed by Jansen127 in response
to the canonical rules that hinder synthetic chemistry. Any material that is capable of
existence is already occupying a position in the free energy surface. The free
enthalpy of formation and thus the thermodynamic stability are not critical factors as
far as the feasibility of synthesising that compound. The only precondition to make a
compound capable of existence is a local minimum in its configuration space which
is defined by a value corresponding to its free energy. This simple notion cannot be
realised with the existent computational tools since the calculation of realistic-size
systems requires calculations of the quantum-mechanical energy and the derivation
of their macroscopic state function based on these results.
The computational effort is dependent on the component number of the system and
the step size applied for the variation of the thermodynamic variables. For an
individual energy calculation the maximum number of atoms allowed and the
number of atoms and the charge of the nuclei core will define the number of quantum
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mechanical calculations. It is thus clear for ternary and quaternary systems our
computational power becomes inefficient.128,129
However, exploration of possible systems cannot be successful without a suitable
mathematical/informatics basis. The de novo prediction of possible systems, although
in its infancy, is a promising robust path in the prediction of new composite lattices
for use in a plethora of catalytic systems.
2.5. TECHNIQUES FOR CATALYST SCREENING
Advances in design methodology and synthesis techniques have enabled the efficient
production of a large number of catalyst formulations which is important to
characterise in a HT fashion. The first step in a HT scheme is the fast identification
or initial screening of potentially promising materials. Secondary screening is
focused on an in-depth analysis of candidates revealing interesting properties.
Numerous methods have been used for the detection of reaction related parameters
i.e. activity/selectivity measurements that permit the determination of the system’s
kinetics and allow create a picture of the macroscopic behaviour of a system. Many
characterisation techniques make use of the interaction of molecules with photons to
allow an in-depth structural analysis. The combination of various time-resolved in
situ probes is currently being assessed by many researchers, as seen in the Chapter 1.
There are numerous practical considerations in the design of HT characterisation
experiments especially when attempting to establish multiple simultaneous catalytic
reactions in a parallel system at varying temperatures, multiple flows, different gases
and spatial resolutions and in situ structural probing. The following paragraphs are an
overview of the heuristics involved in the overall design.
2.5.1. LIBRARY DESIGN
The high throughput catalysis community has adopted various designs in the
development of parallel reactors. Some of the most recent examples follow the
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industrial standard of the Society of Biomolecular Sciences (SBS) (Figure 2.10). This
has been used mainly in biomedical and pharmaceutical applications.130 The SBS
standard includes array sizes of 8, 48, 96, 192, 384, 1536. The primary reason for
choosing a SBS standard plate is the widespread compatibility with synthetic robots
which provide a proof-tested and accepted improvement in overall system
productivity.
Figure 2.10. Left: The 96 well version of the SBS standard, reproduced from
ANSI/SBS 1- 2004, specifications. Right: Implementation of a 96 well reactor
from Watanabe.130
However, imposing physical limitations on the design may offset the advantages of
using standard technology. One has to consider that, for example, the 96 SBS
standard contains 96 wells in an area of approximately 100 cm2; a typical parallel
reactor layout would require a minimum of 192 gas connections if all the gas
products of the individual wells had to be measured (Figure 2.10, right).
Different physical implementations have also been reported: a 49-fold circular
metallic reactor for the high pressure synthesis of Cu/ZnO,131 a 64-fold circular
ceramic rector for the ODP114 and a 207 fold hexagonal metallic reactor for mixed
oxide screening.31 Each researcher has adapted their design according to the
structural characterisation needs of their research. Nevertheless, some techniques are
inherently incompatible with each other and make infrastructure and reactor design a
daunting procedure.
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2.5.2. CONTROL OF GAS DISTRIBUTION AND COMPOSITION
The educts of heterogeneous catalysis reactions are usually gases or vapours.
Currently, the most established method for control of gas composition is by means of
mass flow controllers (MFC).132 It is a bulky and expensive method for single or
multiple gas control because it requires strenuous calibration according to the gas
specific properties and custom made design. Nevertheless, it remains the most
accurate method of control (better than 1% over the dynamic range) of gaseous or
liquid components. Recent advances in MFC modelling will make incorporation in
industrial control more efficient.133 However, there are serious cost and size-related
restrictions when using MFCs with large numbers of independent gas or liquid
reactants.
Nevertheless, to date, there have been no examples of an efficient miniature device
that has been integrated to a high throughput catalyst screening system for the control
of gas flows at the individual reactor level.
However, there are several examples in literature for micro-electromechanical
(MEMS) devices utilised in gas sensors and fuel cells.134-136 It is well known that
increasing the temperature of bimorphs comprised of Si-Ni causes a mechanical
distortion due to the different thermal expansion rates.137 Tomonari and co-workers
exploited this feature and produced a fully controlled micro-valve with a flow range
of 0-500 cm3 / min.138 The micro-valve sizes 1 cm2 could be potentially useful in the
individual control of gas flow of large size combinatorial reactors. New MEMS hold
promise in gaining more control and continuing the miniaturisation of HT catalysis.
2.5.3. TEMPERATURE CONTROL
The most readily available method of heating and controlling the temperature of
reactors is by means of resistive elements which are coupled to a simple proportional
integration differentiation (PID) controller. However, controlling the temperature of
closely spaced reactors becomes difficult, due to heat conductivity.
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Cong et al addressed the problem using an alternative heating method.116 A quartz
wafer was developed, which contained deposited layers of 136 quaternary catalyst
compositions. The temperatures of individual catalysts were sequentially controlled
using a CO2 laser which produced a localised heating effect and allowed the
screening of the initial activity and selectivity of the catalysts during the oxidation
reaction of CO. However, the accuracy of the temperature measurements and the
exact method of control have not been reported.
Cooling control has also been realised by using solvent cooled infrastructure and/or
Peltier elements (thermoelectric heat pumps). However careful design of these
apparatii is required due to the reduced dynamic range of some materials since high
temperature differentials in localised regions could create defects leading to a
possible fracture of the entire reactor.
2.5.4. ANCILLARY CONTROL
The use of positioning control is paramount to many characterisation techniques.
Some analytical techniques such as XAS and XRD (see below) are inherently
sequential and positional control permits the construction of 2D or 3D structural
maps using parameters deduced from those techniques. Ancillary control is usually
achieved by means of mechanical stages that allow many degrees of freedom (e.g. 6
DOF: linear x, y, z, rotation Rx, Ry, Rz, Figure 2.11). Mechanical stages can be
driven by stepper or magnetic motors.
Power
Supply
Computer
Control
Motor
Drives
Position Feedback
PWM control
Linear or
Circular
Stage
Figure 2.11. Basic ancillary electrical control scheme. Arrows denote
communication pathway between components.
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The latter have the advantage that they produce minimal noise, have clean operation
and very high speeds but they lack stability when vertical weights are controlled. In
those systems a power failure would result in loss of weight support. Stepper and
magnetic motors are driven by power electronic circuits that are responsible for the
efficient, smooth and error free operation of positioning control. There is a
tremendous variety of positioning systems depending on the requirements for
operational length, depth and height of movement and need for rotation control in
any of the primary axis. Mechanical accuracy, stepper motor resolution as well as
power control can be detrimental to the overall repeatability, speed and correctness
of the analytical techniques that are part of a HT screening system. The above
considerations are extremely important when screening libraries synthesised by
deposition routes. Libraries that contain in excess of 40000 components per cm2
require high encoder resolutions and a highly accurate mechanical construction.
2.5.5. PERFORMANCE MEASUREMENTS AND STRUCTURAL
CHARACTERISATION
The screening of a catalytic reaction necessitates the analysis of the gas products and
other reaction parameters, and the structural probing of the catalyst properties.
The following paragraphs will elaborate on a variety of techniques for HT catalyst
screening. Some of the techniques are used mainly for detecting temperature or
colour changes, which can be quantitatively attributed to a change in the molecular
concentration.
Other techniques such as GC and MS, are used to collect chemical concentrations of
the reactants and products. Optical screening methods like FT-IR spectroscopy hold
promise since they provide more information on the molecular structure of all
constituents. However, they do not provide information on molecules that don’t have
dipolar or quadrupolar moments. In addition, complex molecules can possess
overlapping bands that produce ambiguous results.
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Structural characterisation techniques use a wavelength of radiation that is
appropriate for exciting the corresponding atoms or molecules. Variable energy
X-ray radiation is used in various detection techniques and processes that are
governed by the interaction of photons with matter (Chapter 3).
2.5.6. MEASUREMENTS OF ACTIVITY USING TEMPERATURE
A catalytic reaction can cause a measureable temperature differential on the surface
of the catalyst. Sensitive tools can detect an exothermic or endothermic reaction by
an increase or decrease in temperature. In some cases it is sufficient to use miniature
thermocouples for measuring temperature differences in single quartz reactors of
small mass and volume. However, their use becomes problematic in HT catalysis
because hundreds or thousands of components need an equal number of
thermocouple connections and the need of proximity to each catalyst bed. For this
reason, emission or absorption of heat in the infrared spectrum is often used to
measure temperature. Infrared thermography is a non-contact technique which
utilises a camera equipped with a special detector (e.g. InSb) to collect thermal
images. Subsequently, images are calibrated and provide with great accuracy a twodimensional representation of the temperature of any object.139 One of the first
applications of this technique was documented by Moates140 who developed a 16
fold reactor to test the catalytic activity in the oxidation of H2. Temperatures for all
16 catalyst beds were screened in parallel and Ir, Pd, Pt, Rh showed to have the
highest activities. Other researchers117,141 have also extended the use of this
technique using emissivity correction to observe temperature differentials of 0.1oC in
less exothermic reactions. Provided images are appropriately calibrated, infrared
thermography is potentially a very fast parallel and powerful tool in catalyst
discovery.
2.5.7. MEASUREMENTS OF ACTIVITY USING OPTICAL METHODS
Various optical methods have been used by researchers to allow the quantification of
changes in the light absorption of a catalyst. Omata142 has used the absorption in the
visible and ultraviolet spectrum to quantify the activity of a Cu-Zn catalyst in
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methanol synthesis. An absorption increase around 600 nm showed an increase in
catalyst activity. In a different application simple AlGaAs photo diodes (Light
Emitting Diode, LED) were used to record in a parallel manner the performance of a
library of chemo-optical H2 sensors.120 Circular dichroism has also been used for
measuring the enantiomeric excess in a reaction product without having to separate
the chiral products.143 Optical methods have the inherent advantage of speed and
simplicity and are highly suitable for pre screening and fast optimisation of catalytic
reactions.
2.5.8. RESONANT ENHANCED MULTIPHOTON IONISATION (REMPI)
Resonant Enhanced Multiphoton Ionisation (REMPI), was first discovered by
Senkan82 and was initially used for the characterisation of the conversion of
cyclohexane to benzene. This approach was based on photo-ionising the gas products
by using a tuneable UV laser. If the laser wavelength is in resonance with the
resulting products then the produced photo-ions are detected by micro-electrodes
which are located in the vicinity of the laser beam. The disadvantages of the
technique are the need of a tuneable UV-laser, the lack of other spectral fingerprints
except benzene, and to date there hasn’t been more research reported on the
technique.
2.5.9. GAS CHROMATOGRAPHY
Gas chromatography allows selection of all catalytic products and has been used
more in the past than nowadays since the time for separation of all compounds
ranges from 5-15 min, which is prohibitive for many HT experiments. However,
combined use has been proposed31 and applied by Hahndorf.114 GC was used to
separate the compounds and MS to deconvolute the separations in the oxidative
dehydrogenation of propane (ODP) using 64 catalysts with various components on
-Al2O3 support.
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2.5.10. MASS SPECTROMETRY
Mass Spectrometry (MS) is one of the most popular methods for measuring the
complete range of gas products in a reaction.144 The theoretical basis of MS is the
ionisation of gas products under vacuum and the separation of ions according to their
mass by using a quadrupole mass filter (Figure 2.12). MS enables simple scanning,
high sensitivity (ppb), high measuring and repetition rate and large dynamic range of
up to 10 orders of magnitude of the concentration of gas components.
Figure 2.12. Left: Principle of MS detection, Right: Cyclohexane MS
fragments.144
Many HT systems have utilised MS-based identification of reaction product
detection because it is accompanied by high speed and accuracy. However, only fast
sequential scanning can be implemented since parallel screening would require the
use of one MS for each reactor which is practically impossible at the time of writing.
However, a MS system can be fitted with a multivalve apparatus that in principle can
increase the measuring capacity of such system to tenths of catalyst formulations.145
Urschey,31 Orschel,115 Cong116 and Senkan146 have produced transient measurements
on the catalytic activity of spatially addressable libraries using a single MS nozzle
The advantage of this approach is the less complex gas distribution setup and the
high resolution data of the transient response. However, the fact that MS data can be
collected one catalyst at a time results in a less thorough investigation of the
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catalyst’s performance. Weiss147 has also incorporated statistical analysis of the MS
data of the reaction, facilitating the identification of promising catalyst candidates.
MS measurements are suitable for measuring the activity and selectivity of many
reaction effluents. However, information can be lost when products with similar
mass/charge ratios overlap. Nevertheless, it remains the instrument of reference
especially in combination with other techniques.
2.5.11. INFRARED SPECTROSCOPIES
Infrared, Fourier Transform Infrared (FTIR) and Raman are optical techniques
focusing on the vibrational states of molecules and can be used to gain information
on types of bonds. Raman relies on the inelastic scattering of a monochromatic
exciter and experiments require minimal sample preparation (gas, liquid, solid) and
can be used for a multitude of molecules. The drawback is sample fluorescence
(reemission of light on a lower wavelength) that could conceal the infrared signal and
create ambiguous results. FTIR is finding more applications in catalysis
22,148
and
electro-catalysts149 and a more advanced form of FTIR using imaging of the sample
catalyst, has recently been used.Specific electronic information can be extracted, for
example in the change of structure of a metallic catalyst which results from adsorbed
CO (Figure 2.13).22
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Figure 2.13. Series of infrared spectra of CO adsorbed on a Cu-ZSM5 catalyst
pellet at three temperatures during heating. As temperature increases water
desorbs.22
2.5.12. X-RAY DIFFRACTION
X-ray Diffraction (XRD) is a non-contact structure probing technique that is based
on the elastic scattering of X-ray radiation on a crystalline material. Provided the
material is reasonably ordered, constructive or destructive interference between the
incoming wave and the structure at different angles will produce a characteristic
X-ray diffraction pattern of the analysed molecule. In the interest of catalytic
reactions XRD will provide information on crystallisation changes of the catalyst
and/or catalyst support. Along these lines Cong150 recently utilised the screening
capabilities of XRD to analyse the changes in structure that are related to catalyst
function. A range of mesoporous silicates (MCM) were analysed and their XRD
patterns were clustered according to their degree of long order crystallinity (Figure
2.14). Cluster 1 is more disordered and can be attributed to low order MCM. Clusters
2,3,4,5 correspond to progressively increasing long order crystallinity. Cluster 5
corresponds to high order MCM-48.
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Figure 2.14. Clustered XRD patterns deduced from the individual catalysts.
Cluster 1-4 denotes progressively more ordered structures (Adapted from150).
It was found that XRD is a useful tool in the development of a model for predicting
changes in the structure MCM and their relation to catalyst function. Note that, the
probing ability of XRD is limited from medium to highly ordered catalysts or to
samples that contain some level of long order structure like molecular assemblies.
2.5.13. X-RAY ABSORPTION SPECTROSCOPY
While XRD deals with the diffractive properties of the interaction between photons
and matter, XAS utilises the characteristic excitation of an inner core electron of a
molecule to higher orbital and the constructive and destructive interference of the
emitted photoelectron when scattered by other neighbouring atoms.
Over the last two decades the catalysis science has taken full advantage of the
molecular probing capabilities of XAS to gain information on the electronic state,
local geometric structure of diverse molecules. The theory behind it will be explored
further in Chapter 3 which deals with the characterisation techniques. Here an
overview will be given on recent attempts for the HT implementation of this
technique.
Grunwaldt and co-workers employed HT experimentation to synthesize, under high
pressure, 49 CuO/ZnO compounds.131 Five of the most promising compounds were
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then further reduced and the fractions of Cu0, Cu+1, Cu+2 were extracted using linear
combination analysis (LCA). However in the experiment, only a small number of
catalysts were examined by using X-ray absorption near edge structure (XANES).
The term combinatorial near edge X-ray absorption fine structure (NEXAFS) was
defined by Genzer and Fischer151,152 on chemically heterogeneous substances.
XANES and NEXAFS are abbreviations of the same technique that is used to collect
chemical information of a sample structure from the near-edge of its absorption
spectrum (XANES is used more by solid state scientists whereas surface scientists
prefer the term NEXAFS). In the abovementioned examples the molecular
orientation was probed on a double gradient surface. The surface was positioned
around the beam at different angles and at each interval a spectrum was obtained.
Initial experiments focused on the molecular orientation of C-F bonds of semifluorinated t-F8H2 molecules. Depending on the angle and position different features
emerged in the spectra.
Research made by Bhat et al utilised Combinatorial Partial Electron Yield (PEY)
NEXAFS to characterise the distribution and size of Au nanoparticles attached on a
molecular gradient of NH3. Perfect agreement was established between Atomic Force
Microscopy (AFM) particle size measurements and PEY NEXAFS data.153
Combinatorial NEXAFS has also been used152 to determine the activity of ZSM-5
zeolites, with varying silica to aluminium ratio, in the rehybridisation of propylene. It
was suggested that combinatorial NEXAFS accelerated the catalyst discovery and
allowed the selection of the most reactive zeolites.
The combination of XRD, X-ray fluorescence XRF and NEXAFS was first
attempted by Bell Laboratories, NEC and Symyx technologies in 1998 with some
success in the HT characterisation of combinatorial libraries.154 A library of 128 thin
film phosphors on a Si substrate was screened to gain information on
crystallographic, elemental and chemical state of the components of each thin
deposited film. It was shown for three of the thin films that Eu2+ in a cubic perovskite
(CP) matrix might be responsible for blue fluorescence. In agreement with the above
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it was shown that Eu3+ in the same CP host is responsible for the red phosphor
(Figure 2.17).
Figure 2.17. XRD and NEXAFS detailed spectra of 3 phosphor candidates.154
Although, only three samples were tested it was demonstrated that it is feasible to
construct an analytical probe based on XAS that provides detailed structural and
chemical information for the individual elements of a fabricated library.
2.5.14. X-RAY FLUORESCENCE
XRF is a technique that directly measures the emission spectrum of an element
through the fluorescent photons emitted at one energy point. XRF provides
information on the elemental composition and chemical states of the components of a
sample.
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An implementation of HT XRF characterisation of catalysts was demonstrated by
Eba and Sakurai.155 Nine catalysts containing MnCo2O4 at three different
temperatures and three different HNO3 were impregnated on an Al2O3 substrate. The
purpose of the experiment was to obtain information about the optimal synthetic
parameters which support the creation of ultrafine particles. The small size (8 mm2)
of the combinatorial library allowed it to be illuminated completely from the high
brilliance X-ray beam. The library was set into an angle of 2 degrees with respect to
the beam and a 1000 × 1000 pixel X-ray CCD camera, made of silicon, was located
exactly above the library elements. In this way a true parallel recording of all the
spectra concurrently for each of the energy steps could be achieved. Recorded
images at each of the energy steps provide information about the elemental
composition for each of the pixels in that picture. Consecutive energy scans below
and above the absorption edge, provide full XANES spectra and additional
information on chemical states of each pixel (Figure 2.18).
Figure 2.18. Left: 2D XRF image at an incident energy of 6570 eV just above the
Mn K edge with an exposure time of 1 s. Right: MnO K edge (6545 eV) shifts
due to changes in oxidation state.155
Although, the authors attempted to obtain full EXAFS spectra, that was not
accomplished due to the very low signal to noise ratio that the CCD produced.
However, it was established that either higher concentration HNO3 or higher
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temperature produced the highest shifts in oxidation number of Mn and the most
accurate synthesis of MnCo2O4.
It shall be notes that this technique requires approximately 100 sec to record a full
2D XANES from 9 samples. The technique, however, is limited to the size of the
monochromatic beam which has to cover all the samples simultaneously so that an
image can be produced. In most practical applications the beam size is restricted to
10 mm2 which minimises the total number of samples that can be tested at any one
time. The ability to produce elemental 2D images and 2D energy shifts using
XANES provide a promising avenue for accelerated parallel in situ screening of the
electronic state of catalysts.
Final Note
This chapter dealt with recent advances in screening techniques used in high
throughput discovery and optimisation of catalysts. Indirect techniques allow the
extraction of information on a catalytic reaction using physical characteristics such as
temperature which are assumed to correlate to the performance of a reaction.
Other techniques such as mass spectrometry, allow measuring the ratios of reactants
to establish more quantitatively the yield and selectivity of a catalyst. Last but not
least, direct techniques probe the molecular structure and provide information on
how it affects the macroscopic function or performance of a catalyst.
It can be concluded that only combined use of several of the above techniques, while
the catalytic reaction takes place (in situ), will facilitate the identification and
elucidation of structural changes.
It can be inferred that the size of the acquired data body necessitates the use of an
efficient computational environment that will enable the deconstruction of the
catalytic mechanism. Recent developments in the field of knowledge extraction
relevant to catalysis and materials science will be discussed further below.
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2.6. LIBRARY OPTIMISATION: DATA MINING AND
KNOWLEDGE EXTRACTION
The combined use of numerous screening and characterisation techniques enables the
production of large quantities of data. Consequently the development and
implementation of informatics management tools is crucial for the successful future
of combinatorial catalysis. Optimisation methodologies that enable the discovery and
improvement of catalysts are essential for industrial applications. However,
answering short term questions is not sufficient for science. Extracting knowledge
from the data or “Data mining” is an integrated part of many applications and is
intended in establishing patterns amongst the data,103 for example, relating catalyst
formulation to catalyst function. When the theoretical analytical basis does not exist
or when it is scarce, it would be useful if the data are recorded, annotated and
suitably archived for later analysis.
2.6.1. KNOWLEDGE EXTRACTION
A catalytic reaction produces a number of responses that correspond to values of its
parameter space. Hence, utilisation of catalytic data, past and present, enable the
modelling of a catalytic reaction. Discovering, however, a new catalyst and defining
why it possesses a particular response requires the careful design of descriptors that
aimed at, as the word implies, describing parameters such as its physicochemical
characteristics that are related to its performance. Creating a diverse library, that is, a
library that gives a maximum of different responses in the given target application, is
also crucial for covering more possibilities.29 These rules increase the chances of
discovering regions in the parameter fabric that would justify further exploration.
More focused libraries are then constructed around promising formulations and
further optimisation can be accomplished.
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2.6.2. CATALYST DESCRIPTORS
Descriptor parameters in the context of catalysts are attributes such as synthetic
method, constituents and chemical information; atomic radius, oxidation state,
enthalpy of formation of oxide and others (Figure 2.20). The methodology of
diversity and descriptors was applied by Klanner in the synthesis and optimisation of
a library containing 467 catalysts.13 The library was highly diverse with respect to
the synthesised materials: binary oxides, multinary oxides and different catalyst
supports were used for targeting propene oxidation. The catalysts were tested in a 16fold reactor and their performance was recorded at five temperature points using GC.
After screening each of the catalysts a multitude of attributes were assigned, to name
some, yield, selectivity to 21 products, temporal behaviour and carbon mass balance.
Figure 2.20. A possible descriptor vector containing all the attributes that might
be related to the performance of a catalytic formulation.13
In addition, other attributes were assigned according to the physical properties of the
elemental constituents of the catalysts. It was found that the dimensionality of these
data is too large to interpret and thus heuristic knowledge and principal component
analysis PCA was incorporated followed by Euclidian distance clustering. Euclidian
distance is a simple means of establishing the “closeness” or possible relation of data
according to their position on the 2D principal component matrix. The set of data
describing the catalysts was reduced to 75 attributes. Subsequently neural networks,
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classification trees and a statistical benchmark were used for predicting which of the
attributes have a considerable influence in the catalytic performance. It was found
that the maximum difference in the atomic radius of all elements, the mean electron
affinity, the Pauling electronegativity and other tabulated data can have a tremendous
predicting power in choosing the most appropriate catalysts for particular classes of
reactions. The methodology holds promise in the in silico pre-screening of catalyst
formulations and efficient discovery of novel catalysts.
2.6.3. KNOWLEDGE EXTRACTION USING XRD
XRD yields useful information on crystalline structures and phase changes. As such,
Quantitative Structure Property Relationship (QSPR) modelling research has
employed XRD as a tool in the quantification of catalytic activity in relation to the
catalyst structure. The QSPR main directive is the incorporation of various
descriptors, for instance synthetic descriptors and descriptors that directly infer to the
structure of the catalyst when measured by XRD.
This technique could be considered as an evolution to Klanner et al’s proposal for
the use of tabulated chemical and/or performance data as descriptors for the fast prescreening of candidate catalysts. Corma150 demonstrated that XRD patterns can be
incorporated in QSPR analysis in the process of characterising different mesoporous
silicates. The collection of XRD data and their use as descriptors improved the
prediction performance for an optimum catalyst, since the final solid material
performance is depended on specific material properties (coordination number,
phase) indescribable by tabulated data alone.
2.6.4. INFORMATION HARVESTING USING XAS AND 2D CORRELATION
In complement to XRD, X-ray absorption spectroscopy (XAS) can yield structural
and electronic information of phases without long range order. Analysis of the near
edge part of the absorption spectrum (XANES) can yield information on oxidation
state, molecular symmetry and general structure.
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Analysis involves identifying features on the X-ray absorption spectrum of the
particular material and each is related to the molecular structure. Shifts on the main
excitation energy and amplitude changes on the near edge resonance (white line) of
the spectrum, denotes possible valence changes. For example, an increase of the near
edge resonance intensity is related to higher density of empty states near the Fermi
energy.58,59
A small absorption increase before the main energy excitation (pre-edge) is usually
associated with alterations in the local symmetry by electron excitations to dipole
forbidden orbitals (e.g. a s-d transition). A more detailed description of X-ray
absorption will be given in the techniques section (Chapter 3). It is highly desirable
to be capable of identifying spectral features because they correspond to important
information related to the electronic structure of a catalyst.58,59
2D correlation analysis is a signal processing technique that operates by using a
spectrum to filter itself (in the spatial domain) with a spectrum that overtook a
change. In this way all the features of the spectrum that are identical are filtered and
only differences are amplified and easily identifiable (Appendix 2). The technique
has been developed by Noda,156 who applied it initially to infrared spectroscopy.
Haider157 utilised 2D correlation analysis for the identification of trends in XANES
spectra of a cobalt catalyst that had undergone changes during the temperature
programmed reduction (TPR) in a hydrogen atmosphere. Time resolved XANES
spectra of the reduction of process and their corresponding 2D correlation spectrum
can be seen in Figure 2.21.
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Figure 2.21. Left: 2D Synchronous correlation spectra from the first and last
XANES spectra from the TPR of Co-MCM41 catalyst in H2 (Right). The
positive maxima (red hue), correlate to the existence of two features (namely a
pre-edge 7714 eV and white line 7724 eV). Adapted from 157.
Applying 2D correlation analysis on the first and last spectrum of the TPR XANES
series (Figure 2.21 right) produces the 2D spectrum on the left. The spectrum
supplies important information such as the location of the pre-edge and white-line
which are the positive maxima in that graph. Furthermore, the order of the changes
can be deduced from the location and sign of the other maxima in the 2D spectrum.
Here it can be seen that the onset of the pre-edge feature precedes the change in
amplitude of the white line peak which may be interpreted as the cobalt orbital
hybridisation with the MCM-41 zeolite support (lattice change) and subsequent or
concurrent valence change in the Co chemistry.
The generalised 2D correlation analysis has proved useful for the observation of
main energy shifts and the extraction of a multitude of information that otherwise
would be difficult to observe. However this technique relies on visual interpretation
which limits its use for HT analysis of XANES spectra. A potential improvement
would be the automation of the technique by identifying maxima and minima on the
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2D correlation spectra using image analysis techniques. More information on the
theoretical basis of 2D correlation analysis can be found in Appendix 2.
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3. STRUCTURAL PROBING IN CATALYSIS USING
X-RAYS
A catalytic process is a chemical reaction that occurs on the surface of a material and
the catalytic properties of a material are determined by the elemental composition,
structure and configuration of the surface at the atomic scale.
The elucidation of a given structure/activity correlation from numerous catalyst
candidates requires detailed screening of their electronic and local geometric
structure associated to metal/support interactions and of in situ modifications of their
chemical environment during catalytic reactions. The nature of catalytic precursors,
and especially the interaction of metal particles with the support material affects the
catalyst stability, activity and selectivity.1-4 For example, the distribution of crystal
plane orientations in metal particles are influenced by the chemistry involved in the
preparation of the precursor and by the interaction with support material surfaces.2
More highly dispersed metal particles often tend to yield more active sites available
for catalytic reactions. Special attention to these interactions is required particularly
when undesirable processes occur, e.g., when support dissolution takes place during
preparation or re-hydration and thermal degradation occur during catalyst use.1
In addition, the study of catalyst precursors is also of vital importance particularly in
catalytic systems where the co-impregnation of multiple metallic salts can result
either in intermetallic phases or in phase-separation.5-12 These influence synergistic
catalytic effects in these systems and can have a dramatic effect on selectivity and
activity.7-12 To determine systematic correlations between preparation conditions,
molecular structure and catalytic activity detailed information is required on (i) the
nature of the catalyst precursor, (ii) phase transformations during preparation and
use, and (iii) structural transformations under in situ catalytic reaction conditions.
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Therefore, it is essential to collect information on the chemical state, defects, steps,
and corner and boundary effects of the structure of a catalyst. There are numerous
techniques that exploit interactions of energy with matter at different wavelengths.
Infrared and Raman158 spectroscopic techniques range in energy from 0.1-2 eV (farinfrared to visible spectrum) and exploit the resonant vibrational features that many
molecules and adsorbents possess. Ultraviolet spectroscopic (UPS) techniques159 in
the range of 2 eV to 100 eV, have been used for identifying the band structure of
adsorbents on catalyst single crystals. X-ray photoelectron spectroscopy (XPS)160
and X-ray absorption spectroscopy (XAS)131 can similarly be applied using higher
energies for extracting structural and electronic information on a catalyst. X-ray
diffraction (XRD) uses a monochromatic x-ray and is utilised in gaining specific
information on the long and short range order of crystalline structures.161 It should be
noted that in general higher energy (>100 eV) X-rays are used as a characterisation
tool, and they account for 80% of all the published catalysis research nowadays.162
The usefulness of X-ray techniques (like all spectroscopic techniques) lies in the
ability of X-rays to penetrate and interact with matter in predictable ways. By
exploiting different mechanisms of absorption, diffraction and emission a multitude
of information can be collected and will be described in the following paragraphs.
As highlighted in the introduction there are numerous advantages using X-ray
absorption spectroscopy. However, XAS experiments require a well defined (small
size) and high brilliance (high density of photons/cm2) beam which in most cases can
only be supplied by synchrotrons.
Synchrotrons are particle accelerators that produce photons of various wavelengths
ranging from infrared to high energy X-rays (Figure 3.1). They operate by
accelerating electrons (or other charged particles) to near the speed of light and
maintaining them on a circular path.
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Figure 3.1. Beamline layout of the newly built Diamond Light Source (Didcot,
UK). It is a 3rd generation synchrotron that can deliver micrometre-sized beams
of synchrotron radiation from far-infrared to X-ray photons.
The ring consists of a number of straight paths and bends, where each bend consists
of an electromagnet that forces the deflection of high speed electrons under the effect
of the magnetic field. The electrons change their velocity because they change
direction and therefore they undergo a short period of acceleration which creates
“white light” or “bremsstrahlung” radiation. A detailed description of 3rd generation
synchrotrons can be found in Bilderback.163 The ability of a synchrotron to produce
highly controlled photons is what attracts scientists since it allows them to probe the
physics and chemistry of materials.
An overview of the atomic interaction with photons will be discussed in the
following pages. For the purpose of this thesis (i) diffraction and (ii) the
photoelectric effect will be elaborated further.
3.1. X-RAY DIFFRACTION
Diffraction is defined by the elastic scattering of monochromatic photons produced
by atoms on an ordered lattice. When the scattered rays are in phase they produce
constructive interference according to Bragg’s law (Equ 3.1).
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n  2dsin  ; n  1,2,...
Equ 3.1
 is the wavelength, d the distance between two lattice planes,  the angle of
reflection, and n the order of reflection.
The spacing of atoms in a plane can be calculated by identifying the angles that
produce maximum intensity of the outgoing monochromatic photons. The Bragg
angles correspond to the positions in which the photon wave vector coincides with
the reciprocal lattice vector (which describes the molecular lattice, Figure 3.2).
X-ray Photons
θ
d
Figure 3.2. X-ray photons scattered by atoms in an ordered lattice of spacing d
interfere constructively and destructively as a function of incidence angle, as
given by Bragg’s law.164
Diffractograms are patterns of the intensity of the diffracted beam as a function of
incident angle. They enable measurements of the lattice spacing of single crystals
and polycrystalline powders. The theory and practical application of X-ray
diffraction is given in standard text books.165
X-ray diffraction is used in catalysis science to allow the identification of the phases
that are present in a sample. The acquisition of the diffraction pattern can be
accomplished rapidly provided a synchrotron is used as X-ray source. Consequently,
transient phases and structural changes can also be identified. However, it is
restricted to the characterisation of materials that contain crystalline phases as for
example polycrystalline powders. Nevertheless, it is a very useful technique for
characterising the bulk structure of crystalline materials. For example the formation
of crystalline phases can be readily recognised when increasing the temperature of
amorphous cobalt (Figure 3.3).
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Figure 3.3. X-ray diffraction patterns of cobalt in a microporous catalyst
framework. The patterns track changes in the formation of the crystalline
phases from the amorphous gel, while temperature increases.166
3.2. THE PHOTOELECTRIC EFFECT
Absorption of photon energy and subsequent relaxation of the atom by emitting
photons or electrons can be exemplified by the photoelectric effect. In this process an
atom absorbs a photon and subsequently a core electron or a valence electron is
ejected with binding energy characteristic of the energy level of the electron (Equ
3.2).
Ek  hv  Eb  
Equ 3.2
Where: Ek kinetic energy of ejected photoelectron, hv photon energy, Eb binding
energy of photoelectron, φ work function of spectrometer (energy loss).
A X-ray photon of characteristic energy will force the atom to an excited state.
Subsequently, the atom can return to the ground state by one of the following ways:
(i) the ejection of a photoelectron with characteristic kinetic energy, (ii) the ejection
of an Auger electron from a higher orbital or (iii) through the emission of a
fluorescent photon. Usually, atoms return to the positive photo-ionised state through
Auger emission or photoelectron emission (99% of the time) and through
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fluorescence photon emission (1% of the time). Figure 3.4 shows a schematic
representation of the three ways an atom responds to an X-ray photon.
Photoelectron
Auger Electron
Continuum
φ
EF
Fluorescent
Photons
Photons
Ka , Kβ
L23
L1
L or M
Electron Hole
Electron
K
Figure 3.4. Summary of the possible final states of an atom after being
irradiated with high energy photons. Left: Photoelectron emission, Auger decay,
fluorescent decay.
One of the most often used characterisation technique exploiting the photoelectric
effect is X-ray photoelectron spectroscopy (XPS). An X-ray source of known energy
is used to irradiate the sample. Due to the photoelectric effect electrons with
particular binding energies are excited. Measuring the kinetic energy distribution of
the ejected photoelectrons enables the identification of the element(s) being probed
(Figure 3.5). The method yields information on the elemental composition, oxidation
state and in some cases dispersion of one phase over another. XPS is a highly surface
sensitive technique since the energy of the ejected photoelectrons is usually less then
1.5 keV giving them an escape depth of a few Ångstrøms. However, due to the low
energy of the ejected electrons the system has to be operated under ultra high
vacuum (10-9 mbar) that incapacitates its use for in situ catalysis experiments.
Nevertheless XPS is amongst the most frequently used characterisation techniques in
catalysis along with XRD and infrared spectroscopies.89
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Figure 3.5. Left: XPS spectrum, of Au-Co in iron oxide catalysts, over 1000eV,
Right: Higher resolution spectrum for identification of the exact binding
energies corresponding to different oxidation states of cobalt a) CoO, b)Co 3O4,
Au/Co3O4.167
3.3. X-RAY ABSORPTION SPECTROSCOPY
X-ray absorption spectroscopy (XAS) was first examined in detail by Kronig
168-170
in the 1930’s. It is based on photon absorption. The physical description of
absorption of X-rays from a sample is related with the photon energy–dependent
absorption coefficient μ(E), as introduced through the Lambert-Beer attenuation law
(Equ 3.3).
I ( E )  I o ( E )e   ( E ) d
Equ 3.3
The intensity of the photons transmitted through a sample depends on the intensity of
I(E) of the incoming photons and the absorption depth d. Variations of the X-ray
absorption spectrum of materials can be explained by combining the Lambert-Beer
law with the (i) photoelectric phenomenon (previously discussed) and (ii) with
interference effects between the photoelectron wave function.
When an incident photon has a kinetic energy that does not correspond to any
available electronic transition it will not be absorbed by the atom (Figure 3.6 left).
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When the photon approaches the binding energy of a core electron, transitions to
unoccupied states become possible. Initially, in the near energetic vicinity of the Xray absorption edge, intra-atomic transitions to bound states below the ionisation
threshold occur (Figure 3.6 middle). When the photon energy is increased further
above the photo-ionisation threshold the emission of a photoelectron will occur
(Figure 3.6, middle). In other words, if there is an available unoccupied state in the
valence band of the atom then that will be filled with the ejected photoelectron and a
higher absorption coefficient will be observed. This allows a higher number of
photons to be absorbed when their energy is just high enough to excite the core
electron to an available electronic state in the valence band. If the energy of the
incident photons is higher than the ionisation threshold, then the resulting
photoelectrons will be excited to the continuum (Figure 3.6, right).
μ
μ
E
E
E(eV)
b
hv
μ
E(eV)
b
hv
E E(eV)
b
hv
Figure 3.6. Left: No absorption (μ) from the atom, Middle: Onset of edge step,
showing maximum photon absorption, Right: Absorption steadily reducing
since the atom becomes more transparent at higher energies.171
If the abovementioned one atom model is extended to a molecular lattice or a crystal
lattice, one has to consider that the ejected photoelectrons, since they have both wave
and particle nature, will be backscattered from neighbouring atoms.171 The
backscattered photoelectron will interfere with it self and produce constructive or
destructive effects (Figure 3.7).
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Figure 3.7. Variation in the X-ray absorption spectrum caused by interference
of the photoelectron wave vector caused by a nearby scatterer.172
Since the photoelectron interferes with itself, this results in variations in the existence
of available states which affects the absorption of photons. The initial state wave
function of the photoelectron will have a superimposed fraction of the final state
wavefunction scattered back from the neighbouring atoms.164 Consequently, if a
cluster of atoms is subjected in a linear increase of incident photon energy, the
absorption of photons will exhibit a variation, which is termed X-ray absorption fine
structure (XAFS or extended XAFS) (Figure 3.7). A multitude of local structural
data (coordination number, distances of neighbours etc) can be collected if the fine
structure is extracted and analysed since it will contain the history of the path the
photoelectron followed at different energies. If only the energies around the binding
energy of the core electron are considered, then the intense scattering effects will
yield bulk structural information on the atom cluster. The EXAFS signal is
superimposed on the absorption spectrum of single atoms of the same element.
Specific structural knowledge can gained on an absorbing atom and its neighbouring
atoms, if the fine structure is extracted from the total X-ray absorption. The smooth
atomic background does not contain any XAFS oscillations and is  (k). The total
absorption is μ(k). The XAFS function is then defined as in Equ 3.4.
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   
CHARACTERISATION USING X-RAYS
      
  
Equ 3.4
Where: wavenumber k=2π/λ and wavelength λ.
The energy of the free photoelectron can be readily converted to the wavenumber by
calculating the momentum according to Equ 3.5 through 3.7.
M particle  2me Ek
Equ 3.5
and
M wave 
k
2
h
hk
2
Equ 3.6
Equ 3.7
2me Ek , where : Ek  hv  Eb
The EXAFS oscillations (    ) can be extracted from the bulk absorption
spectrum    , by modelling the background absorption and subtracting from the
total X-ray absorption.
Theoretically,    can be estimated as the sum over all possible photoelectron
scattering paths j. The following equation (Equation 3.8) forms the basis of all
EXAFS data analysis packages and is known as the general EXAFS formula.171
2R j

2 2
So2
 (k)  2σ j k
 (k )   N j
f
(
k
)
e
e
sin 2kRj   j (k )  2 c (k )
j
kRj 2
j

Equ 3.8
where:
j is the scattering path
k is the wavenumber (related to energy)
N Coordination shell (group of atoms at similar distance)
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So k-independent amplitude factor
λk mean free path function (essentially the life time of the excited state)
Rj distance to scatterer
σ2 Debye-Waller factor (accounting for thermal vibrations of the atom)
fj(k) the back scattering amplitude of each path
φj(k) the effective phase shift of the scattering path
δc(k) the final state phase shift at the central atom
The muffin-tin approximation is used in Equation 3.8 to define a spherical scattering
potential centred on each atom with a constant value in the interstitial regions. The
plane wave approximation has also been used to determine Equation 3.8. This
effectively means that all atoms scatter photons in the same way. This becomes
problematic at lower photon energies because low Z atoms scatter more efficient
than their heavier counterparts.173 This produces different phase shifts and result in
calculations of erroneous inter-atomic distances. Curved wave theory provides a
better approximation at lower energies.173
This expression (Equation 3.8) takes into consideration the “many-body” interactions
that the photoelectron wave experiences as it collides with other electrons in the
same atom or in its path to the scatterer and back. These are inelastic (loss of energy)
interactions which are accounted for in the terms So , λk. The many body effects were
first discovered by Rehr174 in 1978 and helped to improve the XAFS approximation.
Other solid state effects such as polarisation dependence of the incoming wave
vector with the orientation of the scattering surface atoms have also been discussed
in depth.175
3.3.1. ACQUISITION OF XAS IN TRANSMISSION MODE
The acquisition of XAS in transmission mode requires preparation of a thin sample
which interrupts the X-ray beam. The intensity of the beam (photons/cm2/sec) is
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measured before and after the sample (Figure 3.8). In transmission measurements,
samples have to be prepared in homogeneous thin foils or films of a few micrometers
to allow for the portion of the non-absorbed photons to exit the sample. In this way a
low noise to signal ratio can be sustained while the fine structure of the XAFS
spectrum can still be resolved. Although, transmission measurements are the simplest
and most reliable method of obtaining the X-ray absorption spectra they provide only
bulk information on a sample.
3.3.2. ACQUISITION OF XAS IN FLUORESCENCE MODE
A diluted sample is positioned at an angle against the beam and an energy selective
detector is used to measure the fluorescent photons (Figure 3.4, photoelectric effect).
An example of the setup can be seen below (Figure 3.8). It is assumed that the
number of absorbed primary photons (which have as a result an excited
photoelectron) is proportional to the emission of fluorescent photons. In other words
the ratio of the fluorescent to incident beam intensity is proportional to the absorption
coefficient.
   
If
Io
Equ 3.9
X
Figure 3.8. Experimental setup for collection of absorption spectra using
transmission or fluorescent measurements. Adapted from 172.
Fluorescence measurements achieve a better signal to noise ratio (S/N) when used
for diluted samples and thin films. In concentrated samples, however, there might not
be a linear analogy to photon excited core electron and the creation of fluorescent
photons. This effect has been discussed extensively176,177 and is due to the fact that
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most of the ejected photoelectrons will be re-absorbed due to the large number of
identical neighbouring atoms in a thick sample.
3.3.3. ACQUISITION OF XAS IN TOTAL REFLECTION MODE
Fluorescence XAS can also be used in surface sensitive mode. Thin film sensitivity
can be accomplished if the sample is positioned below the critical angle for X-ray
reflection. In such case, most of the incident beam will be reflected away from the
sample. However, a small portion of the incident photons will be absorbed, and
fluorescent photons will be produced from first few layers of the sample surface.
This technique requires long acquisition times because achieving high S/N ratio
requires the averaging of a number of spectra.
3.3.4. ACQUISITION OF XAS USING TOTAL ELECTRON YIELD
Total electron yield (TEY) XAS is a technique that measures the electrical current
produced by all electrons (mostly Auger electrons) ejected from the atom during
irradiation (Figure 3.4). The technique is highly surface sensitive because the small
kinetic energies lead to a small mean path of the ejected electrons. TEY provides
similar to fluorescence spectra but in addition it can be used for depth profiling of
thin layers. The resolution is estimated to a few hundred angstroms, comparable to
that of some electron microscopies (Scanning Electron Microscopy, SEM). The in
situ application of the technique has been extensively described by Schroeder.164
Final Note
The above modes of XAS acquisition are essentially different ways of exploiting the
information content of the photoelectrically ionised atom. The end product of all is in
principle the equivalent absorption spectrum over a range of incident photon
energies. An example of a raw absorption spectrum of catalyst containing Au in
transmission mode can be seen below (Figure 3.9).
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-1
-1.2
-1.4
AU
-1.6
-1.8
-2
-2.2
E(eV
)
-2.4
12
12
11
11
11
11
11
11
7
07
7
03
7
99
3
96
8
94
3
93
8
91
3
90
Figure 3.9. Transmission absorption spectrum of a pure Au catalyst (not
normalised).
The edge step which is associated with the core electron excitation and transfer of the
photon wave momentum to the electron can be identified by the large increase of
photon absorption at 11918 eV. However, the spectrum is not normalised, the slow
varying background is not removed and the edge position is not aligned properly
with the LIII 11919 eV Au excitation.
3.3.5. DATA ANALYSIS - NORMALISATION AND BACKGROUND
SUBTRACTION OF XAS
Equation 3.8 forms the basis of modelling X-ray absorption behaviour of most
elements and surroundings. A priori knowledge on the structure of the sample under
investigation is used along with the XAFS equation to enable the analysis of the
X-ray absorption spectra.
XAS analysis, requires first the modelling of the slow varying atomic background
(  (k)) and its removal to allow the calculation of the chi (χ(k)), from the absorption
spectra. Fourier analysis of chi determines the frequency components of a signal. In
the case of absorption spectra, Fourier analysis determines the approximate location
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of the scatterers or the “radial distribution”. In other words modelling involves
finding the locations of phase changing scatterers that reproduce the chi spectrum.
Information obtained experimentally is modelled with equation 3.6 in an iterative
refinement cycle. In this, several parameters such as coordination number, distance
to nearest neighbour, type of atom and others, are altered until the experimentally
observed spectrum (and/or Fourier transform) matches the modelled one within a
statistically accepted error. Analysis of such spectra is meticulous and time
consuming and requires in-depth knowledge of the system under investigation.
However, when properly performed, the refined parameters yield indispensable
knowledge of the local structure of most materials in situ, which renders it
particularly useful in catalysis experiments. The fundamental principles and XAFS
data analysis steps have been summarised by Koningsberger171 and other
textbooks.178,179 Analysis of XAFS spectra can be accomplished by using well
established software packages such as ATHENA and ARTEMIS developed by Matt
Newville and Bruce Ravel180 , EXCURV developed by Binsted et al181 and XDAP
by Vaarkamp.182 In the following the general procedure of XAS analysis experiment
will be demonstrated.
The first step in XAS data analysis is normalisation. It facilitates the comparison of
multiple spectra with different amplitudes and beam fluxes. Furthermore, removing
the atomic background enables the extraction of the XAFS oscillations. Background
removal can be accomplished by fitting a spline (polynomial function) of varying
degrees to the absorption spectrum. In some cases a linear function is fitted on the
pre-edge part of the spectrum and a cubic polynomial is fitted after the edge step.
Other programs, however, use more sophisticated fitting procedures. Modelling the
background with higher polynomials has the danger of filtering out some of the
XAFS signal since the spline will have enough freedom to follow the oscillatory part
of the spectrum. The XAFS information (χ(Ε)) can then be extracted by subtracting
the absorption spectrum form the background spline. The result can be seen in
Figure 3.10.
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1.2
1
0.8
AU
0.6
0.4
0.2
0
-0.2
-0.4
E(eV)
7
07
12
7
03
12
7
99
11
3
96
11
8
94
11
3
93
11
8
91
11
3
90
11
8
77
11
Absorption μ(Ε)
Xafs oscillations χ(Ε)
Background Spline
Figure 3.10. Spline fitted on the raw absorption spectrum of a pure Au foil and
the extraction XAFS oscillations in energy.
The energy scale is then converted to wavenumber k according to equation 3.5,
(Figure 3.11). In this figure only a small part of the actual total available spectrum is
presented. A higher wavenumber corresponds to a potentially higher content of
information.
0.1
0.05
χ(k)
0
-0.05
-0.1
-0.15
-0.2
k(1/A)
3.85
4.35
4.85
5.35
5.85
6.35
6.85
7.35
Figure 3.11. Fine structure χ(k) spectrum converted from χ(Ε) in Figure 3.10. A
Fourier transform window is chosen (e.g. 4.35 – 7.35) to exclude noise.
However, the signal to noise ratio deteriorates fast with increasing wavenumber (k)
since the absorption signal decreases. Yet, the ejected photoelectrons have higher
kinetic energies and can be scattered from heavier metals. Hence, by adjusting a low
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k weight on χ(k) for example (k × χ(k) or k2 × χ(k) etc) it is possible to selectively
enhance the information content for light elements such as O and C Similarly, if a
high weight (k3 × χ(k)) is applied, information on higher Z metals can be extracted
easier.
3.3.6. DATA ANALYSIS – FOURIER TRANSFORMATION
Sayers
183
has shown that applying the Fourier transform on the k weighted signal
results in approximate (within 0.2-0.5 Å) atomic distances of scattering atoms from
the absorber. The distances can be calculated more accurately if a phase correction is
applied, the reason being that the phase factors φj(k) in the XAFS equation are also
energy dependent.171 The Fourier transform of χ(k) (Figure 3.11) can be seen in the
graph of Figure 3.12. The larger peak located at approximately 2 Å is a group of
atoms that exist on the corresponding radius (commonly called coordination shell)
around the absorbing atom and produce the collective constructive interference. The
scattering atoms in the probed sample are Au (pure Au sample).
7.E-02
6.E-02
FT k*χ(k)
5.E-02
4.E-02
3.E-02
2.E-02
1.E-02
R (Å)
0.E+00
9
9.
4
9.
9
8.
3
8.
8
7.
3
7.
8
6.
3
6.
7
5.
2
5.
7
4.
2
4.
7
3.
1
3.
6
2.
1
2.
6
1.
0
1.
5
0.
0
0.
Figure 3.12. The Fourier Transform of k*χ(k) calculated from Figure 3.11. The
higher amplitude denotes a first shell of scattering atoms at an approximate
distance of 2 Å from the absorber.
3.3.7. DATA ANALYSIS – MODELLING
Modelling of the XAFS data enables the calculation of atomic distance of shells,
number and type of atoms in each shell (N). This is accomplished by starting with a
87
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CHARACTERISATION USING X-RAYS
reference compound which contains the atoms of the sample under investigation and
whom the atomic distances (R), phase shifts and thermal disorder (σ2) is known. The
theoretical XAFS oscillations, which are due to the backscattering contributions of
all the atoms, are calculated in k-space or R-space. The variables of the XAFS
equation are then altered until the statistical goodness of fit of the theoretically
calculated oscillations with the corresponding experimentally obtained oscillations
from the sample is acceptable.
1.80E+00
1.60E+00
1.40E+00
FT k2*χ(k)
1.20E+00
1.00E+00
8.00E-01
6.00E-01
4.00E-01
2.00E-01
0.00E+00
R (Å )
3
4.
7
3.
1
3.
5
2.
8
1.
2
1.
6
0.
0
0.
Experimental
Modelled
Figure 3.13. Top Left: Normalised XAFS spectrum of an alumina impregnated
AuCl4 precursor catalyst, Top Right: Conversion into k space and Bottom:
single scattering, first shell model fitted on experimental data (N = 3.92,
σ2=0.0015, R= 2.3 ű0.23).
If the scenario is successful, that is when the fitness of modelled and experimental
XAFS is deemed good (within a statistical error), then the values of Ν, R, σ2 etc that
were used initially in the XAFS equation for the creation of the model data
correspond to the actual experimental values. A summary of XAFS analysis can be
seen in Figure 3.13.
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The modelling of EXAFS data can yield information on various parameters like
number of coordinated atoms (N) in each shell, atomic radii, and thermal disorder.
This can be accomplished in most cases by using prior information on the
approximate molecular structure extracted from other techniques such as XRD.
XRD can supply the approximate atomic distances of bulk Au atoms and XPS could
be used to identify the existence of other atoms on the surface (or oxidation state).
This a priori knowledge is of essence for the correct initialisation and interpretation
of any XAFS data. A posteriori screening of samples using other surface techniques
is also essential. For example in an in situ catalysis experiment, the probable change
in structure and/or oxidation state should ideally be validated by at least one more
screening technique.
3.3.8. DATA ANALYSIS –X-RAY NEAR EDGE STRUCTURE
The structure of the photoelectron induced oscillation, extends several hundred eV
beyond the excitation energy of the particular orbital, and is therefore termed
extended XAFS or EXAFS (Figure 3.14). However, there is a structurally rich region
before and after the absorption edge which provides structural and electronic
information. This region is termed X-ray absorption near edge or XANES.
Nevertheless, depending on the data analysis approach of the near edge region, it can
also be found with alternate terminology (NEXAFS, Near edge XAFS).
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Figure 3.14. Mo K-edge X-ray absorption spectrum extending 1500 eV after the
absorption edge. Near edge region (XANES) and extended fine structure
(EXAFS).184
The XANES region shows a dependence on oxidation state and coordination
chemistry. For ions with partially filled d shells, the p-d orbital hybridisation alters as
a result to change in the symmetry of the element lattice (e.g. octahedral to
tetrahedral).185 This gives a dramatic pre-edge peak which is essentially absorption of
photons to a localised electronic state, less than the characteristic excitation energy
used for probing the ion. Changes in the oxidation states of probed samples can be
identified by examining particular features on the XANES structure. A high
absorption peak following the main absorption event is termed whiteline and its
height can be quantified to determine density of empty sates in the valence band. 58,59
This event is caused when the excited photoelectron has just enough kinetic energy
to fill one of the valence electron holes. Higher number of electron holes will lead to
the atom absorbing more of its own photo electrons bringing it to equilibrium and as
a result higher measured absorption. Another feature in XANES such as the location
of the absorption peak can also be used for quantising the oxidation state of oxides
(Figure 3.15 left).
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CHARACTERISATION USING X-RAYS
Figure 3.15. XANES spectra corresponding to different oxidation states of Fe
(Left) and H2 adsorption coverage (Right).184
Measuring the area under a specific excitation energy range enables one to identify
small changes which are related with slight changes in the electron density of the
valence orbitals of the main absorber (Figure 3.15, right).
Given that only a small energy region of a 100 or 200eV is used, acquisition of
XANES can be accomplished faster and with higher S/N ratio than an extended fine
structure absorption spectrum. It is indispensable in catalysis experiments in which
the analysis of the reaction chemistry and change in structure has to be obtained in
situ and in reasonable time frames. Other applications of XANES include the
determination of the molecular orientation of mono-layers of light atoms on
coatings.152
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IMPORTANCE OF GOLD IN CATALYSIS
4. IMPORTANCE OF GOLD IN CATALYSIS
The following passage constitutes a short overview of recent developments in Au
catalysed CO oxidation research in relation to results presented in this work.
4.1. INTRODUCTION
In recent years, considerable research effort has been dedicated to the elimination of
major pollutants such as nitrogen oxides and carbon monoxide produced from
common chemical reactions such as the incomplete combustion of hydrocarbons in
automobiles.
Catalysts based on Pt, Pd and Rh are very popular in what has been termed “green
chemistry” because they are actively involved in mediating many gas phase reactions
and are capable of transforming dangerous gas products into more benign
forms.186,187
However, the need for large quantities has increased dramatically the cost of those
metals (especially Pt) and has led to the search for more affordable metal catalysts
with comparable activities.
Due to its noble character, Au has been regarded as inactive in catalysis. However,
this view changed after the discovery of low-temperature CO oxidation catalysts by
Haruta.188 It was also independently found that Au particles are active in a variety of
reactions such as:
(i) SELOX of CO in H2 stream (SeLective Oxidation)189
(ii) Catalytic combustion oxidation of hydrocarbons190,191
(iii) Hydrochlorination of ethyne192
(iv) H2 + O2 reaction forming H2O2193
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4.2. CARBON MONOXIDE OXIDATION WITH GOLD
In its bulk form Au is extremely stable and reacts only if in contact with extremely
acidic environments such as “aqua regia” (mixture of HCl & HNO3 1:3 ratio).
However, it becomes catalytically active for a range of reactions if synthesised in the
form of supported nanometre-scale particles (Figure 4.1). Unusually high activity in
the CO oxidation reaction at temperatures as low as -60oC has been reported.131,194,195
Figure 4.1. TEM micrograph of a very active Au on TiO2 supported catalyst.
Note that the average particle size is below 2 nm.196
A reaction mechanism that has been proposed by Haruta in the case of atomic Au, is
summarised by the following chemical formula.197
O
C
Au+[CO +O2]m
O C-Au
O
CO2 + O C-Au O
Au + CO2
O
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It was suggested that the reaction mechanism consists of reversible adsorption of CO
on the surface and perimeter interface of the Au atoms:
(i) Adsorption of CO:
Au + CO
Au-C O
(ii) Adsorption of O2 at the perimeter interface:
Au-Al2O3 + O2 + e-
Au-O2- - Al2O3
(iii) Surface reaction on Au:
Au-C O + Au-O2- -Al2O3
Au-O- + CO2 + Au-Al2O3
(iv) Desorption of CO2 :
Au-C O + Au-O-
Au-O--Au-C O
2Au + CO2 + e-
Despite considerable research effort since the initial Haruta report, the question of
why supported Au catalysts are active for low-temperature oxidation reactions
remains unanswered. In addition, the support and preparation procedure strongly
influences the activity of supported Au catalysts.
Catalyst supports based on active (e.g. TiO2) and inactive oxides (e.g. Al2O3) can
produce large activity variations. For example, activity variations for Au/Al2O3
catalysts range from inactivity, to comparable performance with highly active
Au/TiO2 catalysts.198,199
Reaction models in the peer review literature, assume different roles for the active
phase of Au. Anionic, cationic, neutral Au species and combinations are employed to
explain the unusual reactivity of supported Au clusters.
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IMPORTANCE OF GOLD IN CATALYSIS
4.3. IN SITU SPECTROSCOPIES ON GOLD
Research in the Au-catalysed CO oxidation has been focused on the study of atomic
structural changes under in situ catalytic conditions aimed at the identification of the
active sites. Studies on how a Au catalyst surface can control the kinetics of the
reaction,200,201 particle size and particle shape effects on the catalytic activity,201-204
Au - support interaction,23,131,201 and the electronic state of the active Au species
involved in the reaction,205-209 have been debatable topics of research for the last
three decades (Figure 4.2).
In recent reviews some authors have proposed that the high CO oxidation activity at
low temperature is associated with metallic Au, while others found the presence of
cationic or anionic Au species, or a combination of metallic and ionic
species.201,207,210-216
Figure 4.2. Possible reaction mechanism for Au on TiO2 catalysts. It is suggested
that CO and O2 activation takes place at the perimeter and surface of the Au
cluster.217
A large number of studies have suggested that metallic Au was important
131,218-222
and more specifically, that the presence of small metal nanoparticles was
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IMPORTANCE OF GOLD IN CATALYSIS
necessary.209,210 ,220,223 ,224-226,227 ,228 Other authors claimed that the activity increases
with the amount of cationic Au species, Au3+ or Au1+, present in the material221,229-232
or that cationic Au species are present at the interface between the metal Au particle
and the support.207,233
The nature of the support appears to be another key factor for achieving high activity,
as reducible supports, such as TiO2 and Fe2O3, usually exhibit appreciably better
performance than non-reducible ones, e.g., MgO, Al2O3 or SiO2.23,131,157,196,198,234-237
Reducible supports would facilitate the activation of O2,23,131,207,214,238,239 but some
studies, do not find the same catalyst classification,240,241 and propose that oxygen
can be activated on cationic Au species at the metal-support interface.242,243
Various studies have attempted to identify and characterize the active sites in situ
under CO oxidation reaction conditions. Many studies employed X-ray absorption
fine structure (XAFS) spectroscopies, i.e. studies of the electronic properties of Au
particles by X-ray absorption near-edge structure (XANES) and of their
crystallographic
structure
by
extended
XAFS
(EXAFS)
measurements
(Figure 4.3).221,228-230,244-247
Figure 4.3. XANES derived data which indicate the activation of O2 molecules
by the Au cluster of a TiO2 supported catalyst. It is proposed that the active
phase of Au is primarily zero-valent.248
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IMPORTANCE OF GOLD IN CATALYSIS
Other researchers, used infra-red spectroscopies, including Fourier transform infrared
(FTIR)213 and diffuse reflectance infrared Fourier transform spectroscopy
(DRIFTS).23,131,249,250 In many cases, interpretations that were derived from these
studies, have been contradictory even when performed on the same Au/support
catalyst systems.
One of the major aspects that need to be taken into account when performing the CO
oxidation
reaction
is
that
the
catalyst
can
evolve
under
reaction
conditions.221,228,245,251 It has been claimed that the conditioning of as-prepared
catalysts in the reaction gas mixture, without any additional activation treatment
(calcination or reduction), leads to higher catalytic activity.251-253
Some authors linked this high activity to the presence of cationic Au species, for
instance Au1+
232
and Au3+.232 However, CO can reduce the cationic Au species
present in as-prepared samples at room temperature, at least partially.221,254-256 The
working catalytic system could then be described as a complex mixture of unreduced
species, such as Au3+ and/or Au1+ and/or even Au1-, and metallic Au0. Consequently
the complex mixtures of Au species do not allow facile discrimination between the
contributions of each Au species to the CO oxidation activity.
It shall be noted that the stability of as-prepared catalysts is often poor due to
uncontrolled reduction of the supported Au precursors. For example, Au3+ species
can occur if catalysts are not dried and subsequently stored under suitable conditions.
Dispersion and particle size are also important factors that substantially affect the
catalytic performance. For example a critical and optimal Au particle size for CO
oxidation lies within the range of 3-7 nm.201,202,204,257-259 Therefore most of the in situ
spectral characterisation of CO oxidation catalytic systems, namely Infrared, Raman,
X-ray absorption and X-ray diffraction, has been carried out within this particle size
range. However, it is still of fundamental interest to investigate how low catalytic
activity of large particles affects the spectral features within reaction conditions and
whether or not it is possible to overcome this particle size limit.
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4.4. AU ADDITIVES AND ENHANCEMENT OF CO
OXIDATION ACTIVITY
Platinum is one of the most used noble metals used in NOx and CO detoxification of
gases260 and in dehydrogenation reactions.261 As discussed above Au can be a
promising alternative to other more expensive noble metals. However, the high cost
of noble metals has led to the search for less precious metal catalysts and multimetallic combinations that have comparable activities with their pure counterparts.
Copper oxides (Cu2O, CuO)245,262 and supported copper oxides263,264 are known to be
highly active for CO oxidation at temperatures above 200oC. Unsupported CuO,
however, shows catalytic activity at room temperature.145,265 CuO-based catalysts
have also been used in a variety of CO conversion reactions including methanol
synthesis,266,267 low temperature methanol synthesis,268 water-gas shift reactions269
and in the oxidation of nitrous oxides.263 In the Cu catalysed oxidation of CO it has
been proposed that CuO undergoes initial or partial reduction and that meta-stable
copper oxide species play a key role.245,265
Hutchings and co-workers270 reported a remarkable increase in CO oxidation
activities for Au-CuO catalyst at room temperature. Improved selectivity in the
catalytic oxidation of NH3 over Au-Cu/Al2O3 has also been reported.271
Nieuwenhuys and co-workers have measured the total oxidation of propene and
propane over Au-CuO/Al2O3 catalysts.272
Other researchers reported that although Au-Ag alloy nanoparticles on mesoporous
Si/Al had larger particle size than monometallic Au particles, they exhibited
exceptionally high activity in the low temperature CO oxidation. It was proposed that
Ag plays a key role in the activation of oxygen in that reaction.273
Multi-metallic catalysts based on Au, Pt and Cu show promise in the oxidation of CO
and provide an ideal chemical system for XAS characterisation using HT
technologies (Chapters 5, 6, 7).
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IMPORTANCE OF GOLD IN CATALYSIS
4.5. NON-LINEAR PHENOMENA IN CO OXIDATION
Oscillations during the oxidation of CO over noble metal catalysts were discovered
almost 40 years ago.274 Heterogeneous Pt and Pd catalysts are now considered classic
systems that exhibit oscillatory reaction kinetics during CO oxidation (Figure 4.4).275
The elucidation of the mechanistic origins of these oscillatory reactions has been the
object of considerable research effort in surface science275-277 and applied
catalysis.278-282 Other metals, for which rate oscillations have been observed during
the oxidation of CO include Cu,283,284 Ir285 and Rh286.
In contrast, there have been no reports of sustainable rate oscillations over Au
catalysts, despite the fact that the low-temperature oxidation of CO by Au has
attracted much attention over the last two decades.48,188,196,197,232,245,287-289 The only
possible exception is evidence for rather slow and irregular rate variations, which
was reported for a Au/Co3O4 catalyst operating at low temperatures.290
Figure 4.4. Quasi periodic oscillations during the CO oxidation over a Pt (110)
surface. The partial pressure of CO decreases from a to c.277
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IMPORTANCE OF GOLD IN CATALYSIS
4.5.1. BI-STABILITY AND OSCILLATIONS IN IDEAL SURFACES
In the presence of CO and oxygen, a Pt (111) surface, in certain parameter regions,
can be in one of two states with either predominant CO or O coverage.277 Upon
variation of a parameter hysteresis occurs, resulting in a motion consisting of abrupt
transitions between the two states. This behaviour is readily explained by a
Langmuir-Hinshelwood mechanism (Appendix 1).
One can conclude, without calculation, that the network is unstable in an unspecified
parameter region and will give rise to oscillations. The oscillating system is limited
by the rate of adsorption and dissociation of CO and O that restrict the system to bistability. An oscillating model based on Langmuir-Hinshelwood kinetics has been
proposed elsewhere using ordinary differential equations.291
4.5.2. ISOTHERMAL AND NON-ISOTHERMAL MODES
Generally, partial pressures below 1 mbar give rise to quasi-isothermal operation and
above 1 mbar to non-isothermal behaviour.292
In isothermal systems such as well-defined metal crystal surfaces in vacuum, the
mechanistic origin of chemical oscillations in CO oxidation over Pt and Pd has been
attributed to surface reconstruction and/or subsurface oxygen diffusion in
real291,293,294 and modelled systems.295,296
In non-isothermal systems transport limitations can arise because of the interplay of
adsorption, desorption and diffusion events at the level of the catalyst pellet, the
catalyst bed or the entire reactor (Figure 4.5).297
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CHAPTER 4
IMPORTANCE OF GOLD IN CATALYSIS
Figure 4.5. Oscillations in the CO oxidation response of a Pt/Al2O3 catalyst. The
catalyst was in pellet form; an example of a non-isothermal system.298
This may result in thermo-chemical oscillations, in which the reaction rate is
determined by heat transport limitations, i.e. local heat production can yield high
local temperatures that can deactivate the catalyst. The associated decrease of the
reaction rate results in lower heat production, and allows the catalyst to recover
activity, which then re-initiates the catalytic cycle.
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5. HIGH THROUGHPUT EX SITU XAS
This chapter contains the first investigations in ex situ high throughput XAS. As a
proof of principle, model tri-metallic catalyst precursors based on Au were
synthesised (Chapter 4) and subsequently screened and analysed using X-ray
absorption spectroscopy (techniques section, Chapter 3). Experiments were
organised according to a high throughput workflow: (i) Array Design, (ii) Synthesis,
(iii) Ex situ Catalyst Screening, (iv) Performance Evaluation and (v) Characterisation
using X-ray absorption spectroscopy.
5.1. ARRAY PLANNING AND DESIGN
Catalyst compositions based on Au, Pt and Cu are very popular in catalytic science
because they are actively involved in mediating many gas phase reactions (Chapter
4).186,187 The exact mechanism of Au activity is not completely understood and such
system is an ideal model catalyst for scientific investigation.
An array of 96 multi-metallic combination containing different concentrations of Au,
Pt and Cu was synthesised. A 96 element array (SBS standard, Society of
Biomolecular Sciences) was used for the synthesis and screening procedure.
5.2. CATALYST SYNTHESIS
Wet impregnation based on surface adsorption interactions, has been extensively
used in the literature for the formation of multiple metal combinations (described in
Chapter 2). It involves first the mixing of metal precursors, that are compatible (do
not form solid precipitates) at the appropriate concentrations. In the following figure
an example of the creation of the Au catalyst is shown. A precursor solution of
HAuCl4 with adjusted pH (using NaOH) was impregnated in porous -Alumina
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HIGH THROUGHPUT EX SITU XAS
powder. The metal particles adsorb on the inner surface pores and form Au hydroxychlorides (Figure 5.1). Subsequent drying at low temperature (<80 oC) suffices for
the adhesion of the Au particles in the alumina pores. Washing with water of
adjusted pH removes the chloride ions (Cl-), which act as catalyst poison by binding
strongly to Au adsorption sites and promote aggregation of metal particles. Heating
of the catalyst under high temperature and hydrogen atmosphere reduces the Au
particles to Au0 which some research groups299 describe as the active phase in Au
catalysis. In some cases, after reduction in hydrogen and depending on the
temperature, there is alloy formation (Figure 5.1).
pH  9
 Al2O3
HAuCl 4 

 Au(OH ) x Cl y /   Al 2 O3
H 2O
Au(OH ) 3 /   Al 2 O3
Au O /   Al 2 O3
nano Au /   Al 2 O3   2 3
Au /   Al 2 O3
H 2 ;  ( heat )
O2 ;  ( calc)

similarly : nano Cu /   Al 2 O3 

 Alloy formation : nano Cu 3 Au /   Al 2 O3 ?
nano Pt /   Al 2 O3 

Figure 5.1. Synthetic pathway of wet impregnation for creating catalysts
containing different concentration of metals. Impregnation in alumina followed
by drying, washing, oxidation (if required) and reduction in H2.221
A library of catalyst precursors was prepared according to the procedure of Figure
5.1 by using CuCl2 (Aldrich, 99.995%), PtCl2 (Aldrich, 98%) and HAuCl4 (Riedel-de
Haën, 51% Au) on Al2O3 (D1011, BASF, mesh size 200-450 m) support. A
standard 96 well plate (Corning) was used to prepare and store the samples.
Solutions with 4 different concentrations corresponding to 0.1, 1, 5 and 10 wt% of
metal loading were prepared using concentrated HCl as solvent.
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CHAPTER 5
HIGH THROUGHPUT EX SITU XAS
The conceptual flow and concentration matrix can be seen in Figure 5.2 and 5.3
respectively. Metal concentrations in each solution were chosen so that 200 L
contained the amount of metal required to achieve the desired loading. Each well
containing 200 L of the corresponding metal solution was then filled with 200 mg
of -Al2O3 using a powder pipette (Zinsser Analytic).
HAuCl4
in dilute
HCl
CuCl2
in dilute
HCl
PtCl2
in dilute
HCl
50μl
From
Each
Solution
+
550mg
γ-Al2O3
Wt%
Wt%
Wt%
A0
A0
B 0.1
C 1 B 0.1A 0
D 5 C 1 B 0.1
E 10D 5 C 1
E 10 D 5
E 10
Figure 5.2. Different concentrations of equal volumes of each metal chloride are
mixed and added to 200 mg powdered alumina (D1011, BASF, mesh size 200450 m), according to the concentration matrix below.
104
CHAPTER 5
HIGH THROUGHPUT EX SITU XAS
1
2
3
4
5
6
7
8
9
10
11
12
A
aaa
aca
baa
bca
caa
cca
daa
dca
bbb
bee
cec
ceb
B
aab
acb
bab
bcb
cab
ccb
dab
dcb
bbc
cbb
cee
eec
C
aac
acc
bac
bcc
cac
ccc
dac
dcc
bbe
cbc
ebb
eee
D
aad
acd
bad
bcd
cad
ccd
dad
dcd
bcb
cbe
ebc
Al2O3
E
aba
ada
bba
bda
cba
cda
dba
dda
bcc
ccb
ebe
Al2O3
F
abb
adb
bbb
bdb
cbb
cdb
dbb
ddb
bce
ccc
ecb
Al2O3
G
abc
adc
bbc
bdc
cbc
cdc
dbc
ddc
beb
cce
ecc
Al2O3
H
abd
add
bbd
bdd
cbd
cdd
dbd
ddd
bec
ceb
ece
Al2O3
Figure 5.3. Schematic representation of each well and its correspondent
composition of Cu, Pt and Au. The metal concentrations are: a = 0 wt%, b = 0.1
wt%, c = 1 wt%, d = 5 wt% and e = 10 wt%.
The library comprises all possible combinations of Cu, Pt and Au in the
concentrations 0, 0.1, 1 and 5 wt% leading to a total of 64 elements; 27 additional
elements were added as permutations of Cu, Pt and Au in concentrations of 0.1, 1
and 10 wt%, resulting in a library of 91 elements. The remaining 5 wells were filled
with pure Al2O3 for calibration purposes. The library was left to dry for 48 hours at
room temperature (Figure 5.4 left-middle-right).
Figure 5.4. Schematic representation of the impregnation steps. Left: Solutions
were created according to concentration matrix. Red, green, yellow hue
indicates higher Pt, Cu or Au concentration respectively. Centre: depositing
200 mg of alumina in the liquid phase, Right: Final catalyst precursor array.
105
CHAPTER 5
HIGH THROUGHPUT EX SITU XAS
The abovementioned array contained catalyst precursors which were not washed or
reduced (as per Figure 5.1) and thus contained mixed species. The wells where
screened ex situ using XAFS at station 9.3, SRS, Daresbury.
5.3. LIBRARY SCREENING
The library of 96 catalyst precursors, shown in Figure 5.4, was characterised using
high throughput XAS. Experiments took place in the materials processing station
(9.3) of the STFC. The station provides a fast double crystal monochromator which
produces an X-ray beam of tunable energy.149 XANES and EXAFS spectra of a
concentrated sample with reasonable signal to noise ratio (S/N ratio) can be
accomplished in 15 mins. An in-house built positioning control system was
incorporated in the infrastructure of the station to allow synchronisation with XAS
data acquisition (Figure 5.5).
Figure 5.5. The developed system allowed the automatic acquisition of EXAFS
spectra from the 96 member array. Right: The robotic system was integrated in
the infrastructure of station 9.3, Daresbury STFC. Note the 96 cell array is
tilted 45o to allow for fluorescent photon detection.
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CHAPTER 5
HIGH THROUGHPUT EX SITU XAS
The X-ray beam was reduced in size by means of a mechanical slit to meet the
dimensions of each well. The intensity of the incoming X-rays collected from the
ionisation chamber was recorded by the main control computer. A control computer
hosted the program that was responsible for automatically positioning the 96 well
array in the X-ray beam on each individual precursor and the synchronisation with
the station control computer. The hardware and software positioning infrastructure
was used for ex situ (this chapter) and in the in situ characterisation of the catalysts
(Chapter 6) and will be described in more detail in Chapter 6.
5.4. X-RAY STATUS MEASUREMENTS WITH SILICON
PHOTODIODES
Direct observation of the beam status was performed by analogue to digital
conversion (A/D) of the electrical currents of the first and last ionisation chambers (Io
in Figure 5.5). A Labview-based program provided X-ray beam status information
and was also used to calculate the X-ray absorption in transmission mode
(Chapter 3). The interface of the program was also used for the evaluation of visible
light photodiodes used as X-ray sensors.
Valuable time is usually lost when aligning an X-ray beam with a sample.
Calibration of the beam in relation to a sample is usually accomplished by using
photographic paper which is exposed to the beam to establish the point of contact.
An alternative solution would be the use of commercial low cost photodiodes to
provide information on the location of the X-ray beam. An automatic X-ray aligning
system that allows accurate positioning of an array to a predefined known point.
without the need for a costly X-ray camera would be very beneficial.
The photodiodes used, were encapsulated in a 16 linear element package and in a 4
quadrant package. Upon exposure to visible light they produced a small electrical
potential. A thin aluminium foil (~100 μm) covered the photodiode window and
eliminated all visible light. The 100 μm thick aluminium foil is transparent to 65% of
107
CHAPTER 5
HIGH THROUGHPUT EX SITU XAS
incident photons with energies bigger than 12000 eV. More than 99% of photons
with energy less than 5200 eV are absorbed from the aluminium (calculated using
HEPHAESTUS, part of the Ifeffit software package300).
A standard Hamamatsu Silicon photodiode was used as a reference. The aluminium
covered silicon diodes were illuminates by a monochromatic X-ray beam at 14 keV
and rectangular size of 1 mm2. The voltage measurements of each of the diodes can
be seen in the following table (Table 5.1). The testing was carried out by switching
the beam off-on-off so that dark current (no photons) and light current can be
compared.
Hamamatsu Si
1 cm²
16x linear array
4x quadrant
Ionisation
Chamber I
Photodiode
voltage
Dark
voltage
S/N
1.19
1.57
1.61
0.184
0.108
0.067
0.009
0.006
0.005
20.44
18
13.4
Table 5.1. Measurements on commercial low cost photodiode arrays.
As expected, the Hamamatsu photodiode exhibits the highest S/N ratio since it has
the largest area and is specifically designed for high linearity and efficiency when
illuminated by X-rays. The 16x linear array shows a higher S/N ratio over the 4x
quadrant (area 1.5 mm2) especially if one considers that the photodiode elements
occupy only 85 μm2.
These experiments showed it is possible to measure the approximate intensity of the
X-ray beam with very low cost (£5) silicon photodiodes. In later experiments a
modified 2D photodiode array will be utilised for determining the exact location of
the X-ray spot over a larger area.
5.5. X-RAY ABSORPTION DATA ANALYSIS
X-ray absorption spectra were collected from the 96 precursor catalysts which were
contained in the SBS well plate. The X-ray absorption data were obtained at station
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9.3 of the SRS, Daresbury. The SRS storage ring was operated at 2.0 GeV with a
beam current of 150-220 mA. The station uses a Si (220) double crystal
monochromator, which was detuned to 50% intensity in order to minimise the
presence of higher harmonics. A horizontal plane Pd-coated mirror was used to
provide harmonic rejection as well as vertical collimation and focusing. The beam
size was 1.0 mm x 1.0 mm. An ion chamber filled with Argon was used to measure
the intensity of the incident beam (I0). XAS spectra were acquired in fluorescence
mode using a 13 element Ge solid state detector. The EXAFS region was scanned in
the k-scan mode from 2 to 15 Å-1 using k-steps of 0.04 Å-1. Data were recorded on an
energy grid of 22 eV/step in the pre-edge region and 0.8 eV/step in the remaining
XANES region, resulting in an acquisition time of 10 min per spectrum. An energy
calibration of the beam was performed by collecting the transmission spectra of Au
and Pt reference foils at the Au LIII and Pt LIII edge.
The library well plate was aligned at 45º towards the beam in order to maximise the
solid angle of the detector, which was positioned approximately 5 cm above the well
plate. Prior to XAS measurements the positioning system was calibrated using a
635 nm focusing diode laser. The library plate was subsequently placed on the
positioning system. Positioning errors were measured to be less than 1 mm over the
whole length of the plate.
The raw XAS data were converted into standard 2-column format using
SEXCALIB.301 An in-house automated data reduction software based on the
IFEFFIT library300 was used to perform all necessary tasks of conventional XAS
analysis. First, four individual spectra from Au and Pt were analysed and the suitable
parameters for background subtraction, normalisation, k-weighting, and Fourier
transformation were used as a starting point using ATHENA and ARTEMIS. The
automated IFEFFIT script was then used for normalising and background subtraction
of all of the 96 spectra. Furthermore, the data χ(k) were fitted also with the
automated script using as a reference the crystalline structures of AuCl 3164 and
PtCl4.302
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5.5.1. MANUAL FITTING OF EXAFS DATA
Samples containing the highest concentrations of PtCl4 and AuCl3 were manually
analysed in order to assess the overall noise level of the XAS data sets and determine
the windowing parameters for k and r space. The analysis of EXAFS data, as
described in chapter 3, involves normalisation, background subtraction, conversion
to χ(k) and Fourier transformation and finally fitting the experimental absorption
spectra with a model using theoretical standards.
Figure 5.6 shows spectra taken at the Au and Pt LIII edges which exhibit a white line
feature near the edge that indicates the presence of empty d-states, as expected for
oxidised metallic species of these elements. A comparison with reference spectra
reveals that Pt and Au are partially reduced after impregnation.
3.5
3.0
H8, 5 wt% Pt
AU
2.5
2.0
E2, 5 wt% Pt
1.5
D1, 5 wt% Au
1.0
H8, 5 wt% Au
0.5
0.0
Eph-Eo (eV)
-20
0
20
40
60
80
100
120
140
Figure 5.6. LIII X-ray absorption near edge spectra of selected catalysts. The
spectra were aligned to their corresponding absorption edges to allow
comparison. (Pt LIII 11564 eV and Au LIII 11919 eV). Both metals are oxidised
(edge positions shifts of: +3 eV for Pt and + 2 eV for Au).
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D1, 5 wt% Au data
0.15
D1, 5 wt% Au fit
H8, 5 wt% Au data
H8, 5 wt% Au fit
k²χ(k) (AU)
0.05
H8, 5 wt% Pt data
H8, 5 wt% Pt fit
-0.05
E2, 5 wt% Pt data
E2, 5 wt% Pt fit
-0.15
4
5
6
7
8
k(1/A)
9
Figure 5.7, Corresponding spectra in κ space with their fitted model
counterparts. EXAFS region of samples D1 (aad), H8 (ddd) and E2 (ada). The
plots show the k2χ(k) function (lines) and the correspondent fit (dark shades).
5
D1, 5% Au
Data
D1, 5% Au Fit
FT k²χ(k) (AU)
4
H8, 5% Au
Data
H8, 5% Au Fit
3
H8, 5% Pt Data
H8, 5% Pt Fit
2
E2, 5% Pt Data
E2, 5% Pt Fit
1
0
1
2
3
4
5
6
R(Å)
Figure 5.8. The radial distribution model (shaded lines) and experiment (lines).
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Table 5.2 summarises the results obtained for each of the samples after first shell
analysis for Au and two shell analysis for Pt precursors. Fitting values are acceptable
when Debye-Waller factor () is positive and the atomic potential (E0) has absolute
value less than 12 eV.196
Only first shell analysis was performed on Au containing samples since the signal to
noise ratio was unacceptable for high k values. Analysis showed that for Au the first
coordination shell of chlorine atoms is located at approximately 2.3 Å from the
central absorber and contains on average 3 atoms of chlorine. The Debye-Waller 
and atom potentials E0 have acceptable values. The statistical indicator (R-factor)
shows that there is reasonable agreement between theoretical model and data. There
is a higher degree of certainty in the model accuracy of Au spectra since only the
first shell is modelled (smaller fitting range). Precursors containing Pt (e.g. E2 and
H8) had two coordination shells: at 2.3 and 4.4 Å respectively and which contained
approximately 4 and 9 atoms for E2 and 5.6 and 15.1 for H8. Similarly the DebyeWaller  and atomic potential E0 have acceptable values for each of the
coordination shells.
Sample
N
Edge
R (Å)
Å2
E0 (eV)
E2 (ada)
3.67
2.29
0.003
10.4
Pt LIII
9.1
4.42
0.009
-0.85
H8 (ddd)
5.63
2.3
0.003
7.28
Pt LIII
15.1
4.37
0.009
-7.46
3.12
2.27
0.002
9.95
D1 (aad)
Au LIII
H8 (ddd)
Au LIII
3.1
2.3
0.002
dk
k–range
(Å-1)
R factor
FT-range
(Å)
3.75 – 7.7
<0.04
1.0 – 5.0
3 - 10
<0.007
1.2 – 2.4
0.4
9.9
Table 5.2. Fitted values for coordination number N, nearest neighbour distance
R and Debye-Waller factor 2 obtained from the analysis of four spectra from
selected samples: E2 (ada), H8 (ddd), D1 (aad), taken at the Pt LIII and Au LIII
edges. A Hanning window determined the k-range and the boundaries of the k2 weighted Fourier transform (FT). Analysis of the spectra taken at the Au LIII
and Pt LIII used a k2 weighting in the data and fit plots.
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Fits for the Au LIII -edge are in line with the 4-fold coordination of Au found in
AuCl3. The slightly lower coordination number e.g. N = 3.1 is associated to structural
disorder in the material, as one would expect from an impregnation process at room
temperature.
The Fourier-transformed Pt LIII spectra show two well-defined shells that were fitted
using two Pt-Cl backscattering paths centred at different distances from the Pt atom.
The fitting results are in line with the crystal structure of PtCl4 although the limited
k-range available leads to larger error bars in the fit, particularly for sample H8. In a
similar way, the spectrum taken at the Au LIII-edge of sample H8 appears to be
influenced by the EXAFS oscillations from the Pt LIII absorption edge due to their
proximity in energy range (E0,
Au LIII
= 11919 eV, E0, Pt LIII = 11564 eV). One must
also take into account that the fluorescence-yield spectra at the higher metal
concentrations suffer from self-absorption effects (i.e., non-linear distortions on the
near-edge region and reductions of the XAFS amplitudes when elemental
concentrations are too high). This is, for example, evident in the case of sample H8
(ddd). Nevertheless, all the fitting results are still well in line with the crystal
structure of AuCl3.
All results show that Pt and Au are mainly present as their most stable chlorides
(PtCl4 and AuCl3) which are consistent with the synthesis procedure applied. A slight
reduction in the white line intensity is observed at the Pt and Au LIII edges, pointing
at a partial reduction of these salts, which has been observed in previous studies of
similar systems.303
5.5.2. AUTOMATIC BATCH FITTING OF EXAFS DATA
The advanced scripting facilities of the Iffefit library and Linux operating system can
help to automate part of the fitting operations that are usually performed by the
scientist. The main script is responsible for calling all the appropriate subscripts that
enable first the automated normalisation and background subtraction of the
experimental data. The iffefit script produces the k space data that are then used by
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another script for the fitting procedure. Other scripts are responsible for plotting the
results in 3D (gnuplot) so that direct visualisation is possible.
The HT EXAFS analysis was performed using Linux shell scripts that, sequentially,
(i) read all the spectra produced, (ii) applied the IFEFFIT background subtraction
routine and (iii) performed a single-shell fitting analysis assuming the presence of
metal chlorides. All the fits were performed using multiple k-weightings of 1, 2 and
3. The fitting algorithm required the input of initial guess values for backscattering
species, edge-position, coordination number (N), nearest neighbor distance (R) and
Debye-Waller factor (σ2). The script then reports back with the shift on the edge
position, the edge-step (ES) values, the normalized data, the (k) data, the Fourier
transformed data, the fitted EXAFS curves and the resulting fitted values for N, R
and σ2. This software performed single-shell EXAFS analysis on 96 samples at two
different edges (i.e., a total of 192 spectra) in approximately 15 minutes.
The main Linux shell script (courtesy of Dr Angela Beesley).304
#!/bin/bash
for i in `ls Au*.dat`; (#Do the following for all the files in the directory starting
with Au and having extension .dat)
do cp $i current.dat; (#copy the file to current.dat)
ifeffit doit2.iff; (#run the ifeffit script for background correction, get the
normalised data for that file, the chi vs k and the FT vs r)
cp current1.xmu $i.norm; (#copy the normalised data to a file with the same
name as the initial one but extension .norm)
cp current2.xmu $i.chi; (#copy the normalised data to a file with the same name
as the initial one but extension .chi)
cp current3.xmu $i.chir_mag; (#copy the normalised data to a file with the same
name as the initial one but extension .chir_mag)
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cp $i.chi data_chi.dat; (#copy $i.chi to data_chi.dat)
ifeffit newAu_fit.iff; (#run the ifeffit script for EXAFS fitting on data_chi.dat
using the ifeffit script Au_fit.iff)
cp fit_chir_mag.dat $i.fit_chir_mag; (#copy the fitting results (fit FT vs r) to the
corresponding file name under analysis)
cp fit_chi.dat $i.fit_chi; (#copy the fitting results (fit chi vs k) to the
corresponding file name under analysis)
. lines (#this is another script for extracting the parameter results and plotting
them)
Values of the extracted edge step for each of the spectra along with their
coordination numbers, Debye-Waller factors and atomic distances of the first
coordination shell is shown in the following figures. All plots were produced
automatically using the automated script in conjunction with a visualisation module
based on gnuplot command line graph creator. Below are the concentrations of Au
and Pt (Figure 5.9, a and b).
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Figure 5.9. Colour coded real concentration matrices of Au (a) and Pt (b)
corresponding to each of the 96 well precursors. Yellow: 10%, orange 5%, red
1%, blue 0.1%. Precursor E1 contains 0% AuCl3, and 0.1% PtCl4.
The automated script permitted the visualisation of the amplitudes of the absorption
edge steps of Au and Pt. They are presented as hues of different colours ranging from
black (no amplitude) to yellow (maximum amplitude over tested samples).
Correlation with the real concentration matrices can be easily seen for higher
concentrations of Pt and Au. For example the nominal amount of Au precursor in
cells C9 D10 E11 is 10 wt% (Figure 5.9, a). In can also be seen in Figure 5.10, (a)
that the amplitudes of the absorption edge steps of those concentrations correspond
to the highest values (orange-yellow hue, 0.2).
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a.
b.
Figure 5.10. Colour coded edge step amplitude matrices of Au (a) and Pt (b)
corresponding to each of the 96 well precursors. The correlation between edge
steps and concentrations can clearly be seen for the samples containing more
Au.
Black hues in Figure 5.10 denote precursors which produced unreasonable or no
coordination values in the automatic fitting. As expected, that was the case, in low
concentrations of Au and Pt which yielded a lower fluorescence signal and low data
quality. Cells F4-F8 contain low concentrations of Au (Figure 5.10, a). Cells C10 G10 contain low concentrations of Pt (Figure 5.10, b).
First shell XAFS analysis of each of the precursors helped calculate the coordination
number of (Figure 5.11, a and b). The average coordination numbers of AuCl3, and
PtCl4 are in good agreement with the theoretical value of 4. As the Au and Pt LIIIedges are in close proximity the fitting results at the Au LIII-edge are somehow
influenced by the concentration of Pt and vice versa. This trend is more pronounced
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for the coordination numbers (Figure 4.19) than for the nearest-neighbour distances
(Figure 4.20).
Figure 5.11. Colour coded first shell coordination matrices of Au (a) and Pt (b)
corresponding to each of the 96 well precursors. The average coordination
numbers of AuCl3, and PtCl4 are in good agreement with the crystallographic
value of 4 (crystal structures: AuCl3164 and PtCl4302).
The atomic distances between the main absorber and the first coordination shell can
be seen below. The values obtained for most of the cells, where data could be fitted
and when there was Au, are close to the theoretical distance of 2.3 Å. As with the
coordination number cells that contain low or no concentration of the respective
metals don’t allow correct determination of the fitting parameters. For example cell
A12 in Figure 5.12 (a) shows an atomic distance of Au-Cl approximately1 Å which
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is unreasonable for the resolution that can be achieved for Au – scatterer atomic
distances (minimum sensical distance is 1.5 Å for a Au-H shell).
a.
b.
Figure 5.12. Colour coded first shell absorber (Au or Pt) – scatterer (Cl) atomic
distance matrices of Au (a) and Pt (b) corresponding to each of the 96 well
precursors. The average atomic distances of AuCl3, PtCl4 over the entire array
is 2.3 Å which are in good accordance with the crystallographic value of 2.21 Å.
(crystal structures: AuCl3164 and PtCl4302)
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5.6. ADVANTAGES OF 2D XA SPECTRA
REPRESENTATION
Some of the results from the ex situ HT XAS analysis of spectra taken at the Cu Kedge are shown in Figure 5.13. The Cu concentration of the catalysts can be seen in
Figure 5.13, a) and the determined number N of coordinating Cl atoms (Figure 5.13,
b). Black colour in the figures indicates either the absence of the metal under study
or spectra not analysed because of an extremely low S/N ratio.
The usefulness of these 2D visual maps shall be illustrated using an example. The N
map (Figure 5.13, b) has an ‘island’ of samples (E6, F6 and G6) with very low Clcoordination numbers, which are clearly non-sensical. Consequently, the EXAFS
data from these samples were examined in detail.
a.
c.
b.
Figure 5.13. Colour map representations of Cu concentrations (a) and Cl
coordination numbers (b) for each catalyst precursor, calculated using the
script based HT analysis. (c) presents the Cu K-edge XANES spectra of
precursors A7, E8, E6, F6 and G6, and of a Cu metal foil.305
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The analysis, somewhat unexpectedly, revealed the presence of Cu0 metal in these
samples. The plausibility of this result is underlined by the data presented in Figure
5.13, (c) which shows the full XANES spectra of samples A7 and E8 (corresponding
essentially to pure CuCl2), of samples E6, F6, G6 (containing Cu0 metal), and of Cu
foil.
X-ray induced reduction of Cu2+ was excluded as it was not observed in any of the
other samples. It was noticed, however, that the reduction to Cu0 was limited to
samples in which PtCl2 was present. The formation of Cu0 thus appeared to be
related to the redox couple:
[PtCl6]2- + 2e-  [PtCl4]2- + 2 Cl- [E0 = +0.68 V]
Cu2+ + 2 e-  Cu0 [E0 = +0.34 V]
A more quantitative evaluation using the Nernst equation reveals that the reaction
[PtCl4]2- + 2 Cl- + Cu2+  Cu0 + [PtCl6]2becomes feasible at room temperature in the presence of excess Cl- (note that we
used solutions in concentrated HCl). The drying process raises the concentrations of
all reactants and thus shifts the equilibrium towards the products: Cu metal and
[PtCl6]2-. The expected oxidation of Pt2+ to Pt4+ was clearly evident also through an
increase of the near edge resonance (white line) in the Pt LIII-edge XANES from
these samples (an example is shown in Figure 5.14).
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(b)
Pt4+
Pt2+
Pt0
Figure 5.14. Pt LIII-edge XANES spectra of samples E2 (just impregnated with
PtCl2) and E6, which contains Cu metal. Note the strong white line in this
sample, indicating the presence of Pt4+ generated by the reduction of Cu2+.
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6. MEDIUM THROUGHPUT IN SITU XAS
The experience gained from the ex situ high throughput XAS investigation
(Chapter 5) made feasible the development and evaluation of a medium throughput
XAS screening system capable of following in situ reactions.
The system was evaluated with XANES studies of the structural evolution of Au
catalysts supported on Al2O3 and TiO2 during CO oxidation. The system was
incorporated in station 9.3 at the STFC Synchrotron Radiation Source, SRS in
Daresbury Laboratory (2nd generation synchrotron light source).
6.1. SYSTEM COMPONENTS
The developed system consists of five subsections (Figure 6.1):
Fluorescence Detector
Monochromator
2D slit
Beamline
Computer
SRS
Database
Io
Multi-Mass
Spectrometry
X-Rays
8 Reactor Array
z
y
Main Control
System
Positioning
Control
Gas Flow
Control System
xx
XYZ, Theta Positioning
Figure 6.1. System overview and integration in the existing infrastructure of
station 9.3, Daresbury STFC.
(i) The gas flow reactor array, currently including 8 independent micro-reactors.
(ii) The gas supply control unit.
(iii) Multiplexed mass spectrometric gas analysis system that accommodates the
effluents from each reactor.
(iv) A x, y, z,  automated positioning system.
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(v) A supervisory software control system responsible for controlling all sections and
communicating with the X-ray station computer and synchrotron main servers.
An overview of the components required for the implementation of the HT XAS
experiments including the most significant segments of station 9.3, at STFC, is
illustrated in Figure 6.1.
6.2. REACTOR ARRAY
The 8-reactor array was developed in-house and allowed parallel studies of
heterogeneous catalysts with different reactant concentrations, flow rates and
temperatures (Figure 6.2 and 6.3).304 The reaction vessel was made of anodised solid
aluminium alloy providing good corrosion resistance for temperatures up to 600 K.
The reactor block had the dimensions 127 mm × 85 mm × 17 mm (L × W × H) and 8
catalytic reactors were machined on a 4 × 2 matrix as shown in Figure 6.2.
Gas
Inlet
Heating
Element
Gas
Products
X-rays
Fluorescent
Photons
Figure 6.2. Mechanical drawing of the 8 fold well plate reactor used in the in
situ experiments.
The centre-centre reactor spacing was 54 mm and 28 mm across the X and Y axis
respectively and the reactor diameter was approximately 6.5 mm. These reactor
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dimensions are similar to those used for 96 micro-well plates (Society for
Biomolecular Sciences, SBS, standard) and thus facilitate compatibility with
standard
programmable
synthesis
robots
and
other
compatible
analytical
instrumentation. Each reactor had its own inlet and outlet so that the reactor effluents
could be sampled serially through an automatically controlled multivalve assembly
(Pfeiffer®GSS300) Inlet and outlet orifices were machine threaded to accommodate
small footprint aluminium Lee® connectors. Standard 1/16” PTFE tubing was used in
the gas connections of the reactor. Quartz wool plugs were used to prevent
inlet/outlet orifice blockages with powder materials. A custom made, aluminium
heating plate was coated with PTFE and Nichrome® wire (1000 mm length, 0.6 mm
diameter) was wound around the plate. The side of the reactor was covered with
single sided adhesive Kapton® film to permit acquisition of XAS spectra under
various reaction conditions by fluorescence yield detection.306 The 200 m thick
Kapton® film is virtually 100% transparent to hard X-rays with photon energies
above 5 keV. The temperature of the reactor apparatus could be controlled up to
420 K. The operating ceiling was created by the adhesive component of the Kapton®
film. Alternative sealing methods can extent the operating temperature to over 570 K
(Figure 6.3).
Figure 6.3. Left: 8 – fold, sealed (Tested at 3 bar) steel reactor for in situ
combined XAFS/MS experiments. Right: Each of the eight compartments
consists of one input and one output and enables completely independent
reactions to take place.
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6.3. GAS SUPPLY SYSTEM
The detailed scheme of the gas apparatus is shown in Figure 6.4. The system was inhouse built in a portable IP22 container with die cast skeleton and comprised eight
Bronkhorst® digital mass flow controllers with Profibus® protocol (SMS-FC-01PGB-V), shutoff valves and associated electronic control circuits. In initial
experiments the Mass Flow Controllers (MFCs) were controlled in the standard
analogue mode. All gas flow set-points and gas flow read out lines of the MFCs were
fed to an analogue to digital (ADC) and digital to analogue (DAC) multi-channel
converter card (ADC, DAC) supplied by National Instruments® (NI6733). The
acquisition channels were sampled at 10 ms intervals and digital information was
then routed to the Virtual Instrument Software Architecture (VISA®) drivers and
subsequently to the Labview® programming environment. The MFCs contained
shutoff and additional bypass valves which were responsible for controlling the
direction of gas flow. An electronic 24 channel current amplifier was built to enable
control of higher current loads similar to solenoid shutoff/bypass valves and/or lasers
for beam calibration purposes using a common 32 channel DAC card (NI6259).
Figure 6.4. Detailed scheme of the high throughput infrastructure with
instrumentation used, communication protocols and multiple gas stream
connections.
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The in-house built control box can supply a total of 240 W at 24 V which allowed the
automated control of up to 24 solenoid valves and 16 gas combinations. The internal
construction of the MFC system is modular and allows facile mass flow controller
exchanges within minutes. All other gas line connections remain unchanged after
assembly, greatly reducing experimental downtime, when different MFCs are
required or when an unforeseen instrument malfunction is discovered. The MFCs
carry individual shutoff valves which isolate the gas output circuit when an
instrument exchange is required and stop any residual MFC micro-flows
(~0.01 ml/min) propagating in the combined gas output. All internal gas connections
were created using Swagelok® components. Several Swagelok® 1/4" connectors and
three way adaptors were the building blocks of the combined output gas circuit and
1/4" panel connectors were used for transferring the four gases from the cylinders to
the gas supply system encapsulation and also to the gas input of the 8 micro-reactor
plate. For the demonstration experiments reported here, four high pressure gas
cylinders fitted with pressure reducers, supplied helium (Grade 5.0), CO (Grade 4.7),
Oxygen (Grade 5.0) and 5% H2 in helium (Grade 5.0 overall) at 1.5 bar to the MFCs
gas supply system, through 1/4” PTFE tubing with Swagelok ® connectors on both
ends. The MFC’s were used as calibrated by the manufacturer and exhibited an
accuracy of better than 0.5% over the dynamic range of 20-100 ml/min.
The MFC output circuit carried the gas mixture through a single 1/4" PTFE tube to
an 8 way 1/16” PTFE splitter (Omnifit®) which in turn supplied individual reactors
with 1/8 of the total input stream. The gas supply system was automatically
controlled through software routines developed in Labview®. The gas supply control
program (GSCP) enabled in an intuitive manner, similar to that found in many
industrial process automation, concurrent views of gas flow and valve information on
a gas schematic improving legibility for non-experts (Figure 6.5). The software and
hardware architecture was designed with adaptability as the main feature:
(i) Different MFCs can be exchanged in the system and additional or different gas
combinations can be created within seconds.
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(ii) Chemical reaction protocols such as oxidation or reduction can be automated by
the use of a software reaction script.
(iii) The hardware wiring is redundant; if part of the gas flow system malfunctions it
can be readily interchanged with another spare module with a minimum of wiring
and gas connection changes.
Figure 6.5. User interface, developed in Labview®, for gas control of multiple
reactions.
6.4. MULTI – INLET QUADRUPOLE MASS
SPECTROMETRY
Gas product analysis was performed using quadrupole mass spectrometry (MS) with
a Pfeiffer Omnistar® corrosive resistant model. Under normal operation the MS
withdraws a gas flow of 0.9 ml/min through a 1 m stainless steel capillary that is
heated to 390 K to prevent adsorption and blockage by condensates. It also provides
the necessary pressure reduction from atmospheric pressure to the internal base
pressure in the MS analysis chamber of 5×10-9 mbar. The chamber can be heated to
temperatures of 470 K before operation so that gas memory effects (from
adsorbates), on the internal stainless steel of vacuum chamber, can be minimised.
The inlet response time of the MS analysis system was below 1 s. For gas phase
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concentrations higher than 1% wt/wt approximately 100 molecular fragment masses
could be determined within approximately 1 s. A 12-fold distribution system was
coupled to the MS capillary input to allow sequential examination of multiple
catalyst effluents. The multivalve system (Pfeiffer® GSS300) contains a separate
diaphragm pump and allows switching between gas inlets within 0.5 s. The overall
system response time was approximately 1.5 s at volumetric flow rates of 15 ml×s-1.
Eight of the twelve 1/8” gas inlets of the distribution system were reduced to 1/16”
and subsequently connected through 1/16” PTFE tubing to each of the eight reactor
outlets of the micro-reactor plate using Lee connectors. The distribution system inlets
and therefore each of the catalyst bed products can be selectively directed to the MS
for analysis (Figure 6.4). The software user interface included a script module that
allowed recording of sequences of MS traces of individual reactors. MS-testing of
the gas supply system with a flow of 10 ml × min-1 He indicated that it was leak tight
on the order of 1 ppm. For the in situ experiments reported here each mass was
sampled for approximately 0.1 s which resulted in satisfactory S/N ratio of the ion
currents.
6.5. POSITIONING CONTROL
6.5.1. HARDWARE INFRASTRUCTURE
The HT XAS control system comprises a high precision (x, y, z, θ) manipulation
stage that allows positioning of standard (SBS) or smaller and highly compact arrays
prepared for example by physical vapour deposition (PVD), chemical vapour
deposition (CVD) or lithography (more than 104 discrete materials/cm2). Positioning
control was accomplished using three linear stages (Parker® 404LXR) and a 1 circle
segment (Huber® 5203.1). The stages are precision-engineered from extruded
aluminium alloy with high linearity (±10 μm / 400 mm) and low angular tilt
(±100 μrad) over the travel distance. They were stacked in three vertical planes
providing range of movement of 400 × 200 × 300 mm (x, y, z) and ±15˚ rotation
around the y axis (θ). The linear stages were driven by high torque DC stepper
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motors. The system could sustain continuous vertical loads of up to 250 N. The
stepper motor resolution was 1.25 μm per step, and the optical position feedback
provided an accuracy of 100 nm in all axes. The stepper motors of the θ circular
segment allowed angular displacements of 10 mdeg over the ±15˚ range. The
velocities and acceleration variables for each stepper motor were individually set to
reflect the minimum attainable positioning delay with no consequent sample spillage
or physical rearrangement in the case of powder samples. The mechanical setup was
mounted on the existing optical table of the SRS station and required virtually no
setup changes. To facilitate fast and efficient sample exchange the translation system
permitted removal of the in situ reactor array from the X-ray beam axis and
positioning close to the edge of the optical table (Figure 6.6, left).
An additional aluminum platform was attached on the vertical stage to allow for
mounting of the various components such as array, circular segments etc. Additional
adjustments were made to the design to allow for space restrictions in the beamline,
for example a small distance of the X-ray hutch wall to the X-ray beam (125 mm) at
Station 9.3 of Daresbury Laboratory.
The linear stages and circular segments were controlled by five VIX500IM servo
motor drives and three XL-PSU power units all of which supplied by Parker. All
cabling and components were encapsulated in a custom built in standard 19” box
rack. All safety precaution were taken according to CLASS I electrical safety
specification (all cables isolated to 2140 V, grounded box and fused to 5 A). The
servo drives communicate with each other in chain mode using CAN bus. The first
drive in the chain is connected through an RS232 port at 38.4 kbit to a 4 way
National Instruments RS232 hub. The main control system consists of a portable
personal computer (PC) with P4 3.6 GHz processor running Windows XP operating
system. Digital stepper driver units supplied by Parker® facilitated remote control of
the motors (Figure 6.6, right). Combining the RS232 and CAN protocols and inhouse developed drivers allowed communication with the drives and hence control of
the positioning stages. A caption of the servo drives, servo drive box and the control
computer can be seen below.
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Figure 6.6. Left: The PC control the drives (located underneath) and allows
remote positional control over 3 axis (XYZ stage in the background), Right:
Parker servo motor drive and wired power supply.
6.5.2. POSITIONING PROTOCOL OF PARKER DRIVES
The drives communicate to the computer via a set of 50 clear text commands (and 50
property variables), that are transmitted though the physical layer (the RS232 port).
The commands are sent as variable length byte code with a carrier return
hexadecimal byte that signifies the end of command. The commands are constructed
from a number that signifies the address of the drive (where to send the forthcoming
command), the instruction, and properties of the instruction.
An example of commands sent during the initialisation of a new motor would be:
1MOTOR
(Type,
Current,
Resolution,
Max_vel,
%thirdharmonic,
R,
inductance)
Saving and resetting
1SV, 1Z
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Setting velocity, acceleration, deceleration, distance
1V10 (in rps), 1AA10, 1AD10 (10rps ,-10rps), 1D1000 (encoder steps)
Activating the drive, move, change direction, move, go to home position and
turn off
1ON, 1G, 1H, 1GH, 1OFF
The instruction set is capable of movement profiling (triangular, trapezoidal etc),
stall control, analogue input channels and program writing for stand alone robotic
automation in industrial applications. The high acceleration of the serve motors is
accompanied by high velocities and forces exceeding 500 N which can be reached
within a few hundred msec. The operation requires the use of safety non-contact
limits and appropriate mounting. Nevertheless, the instruction set provides
commands that can instantly halt the motor (kill or stop command) and set positive
and negative limit settings. The motor drives were initially tested using the command
line Easi-V software (Parker®).
6.5.3. THE LABVIEW PROGRAMMING ENVIRONMENT
Labview is a programming language with a C core. In contrast to procedural or
object oriented languages like (Java, Visual C++) Labview utilises essentially
information flow diagrams. Labview is nowadays the standard in instrumentation
control in academia and is becoming very popular in industry.
There are two types of program views (similar to Visual Basic) one shows the actual
objects and information flow of the program function (block diagram). The other
shows the visual representation of the program (user interface UI, Front panel). The
block diagram is made of the objects serving the particular program function and
their information flow (input, output).
An example of Labview program code can be seen in Figure 6.7. For comparison,
the “for loop” structure (repetitive cycle) has been constructed in 4 different
languages: Perl, Visual Basic, C and Labview.
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Dim I as Int
For i=1 to Max
print “Number ”, I
MEDIUM THROUGHPUT IN SITU XAS
int I, Max
for (i=1; i=Max; i++)
printf(“Number %d \n”, i);
For i=1 to Max
for(my $i = 1; $max; $i++){
print ”Number $max\n”;}
Next i
Visual Basic
Borland C
Perl
Labview
Figure 6.7. Implementation of an identical function in four programming
languages.
6.5.4. POSITIONING CONTROL PROGRAM
A stand alone positioning control program307 developed in Labview® gave the user,
through the Graphical User Interface (GUI), the choice between manual and
completely automated positioning control (Figure 6.9). Parameters that can be set
through the GUI are the type of array (physical size, elements), movement profiles
and hardware/software communication protocol parameters. The most significant
code modules deal (i) with the translation of actual positions to the RS232 control of
the motor drives, (ii) the virtual plane on which the sample array was moving (with
respect to detector orientation) and (iii) with the beamline communication protocol
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that enables synchronisation of positioning with the acquisition of XAS spectra. The
whole positioning system is easily integrated into other beamlines, because provision
has been made for software-selection of communication protocol details by the user.
This seamless integration of positioning facilities with spectrum acquisition has been
successfully tested during experiments at beamline 12BM of the Advanced Photon
Source (APS, Argonne, USA) and at beamline I18 at DIAMOND Light Source
(UK).
Synchronisation of the positioning software with the beamline station control system
allowed the automated acquisition of XA spectra from an array of catalysts or
materials with no operator intervention. The software module, that was responsible
for the automated positioning, contained control routines for synchronisation with the
solid state detector (SSD) acquisition computer (SSDAC) at Daresbury Laboratories.
A custom communication protocol was used to exchange information between the
positioning program and the SSDAC through a TCP/IP network connection. The
communication protocol contains control and handshake commands which allow
operational safety even when the network connection was unstable or lost. The basic
protocol included command for the start, stop and verification of the EXAFS
scanning. The assignment of parameters and simplified program flow can be seen in
the following caption. Parameters describe the sample array being tested
(dimensions, well number etc) and are inserted in the positioning software
(Figure 6.8).
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Parameter
Setup
(Array Size,
XYZStep,
Timing,
Communication
s Z Angle etc)
Manual
Control
Automatic
Control
Finished
Array?
XYZ Theta
movement
Beamline
Computer
Synchronisation
Step
movement to
next sample
well
End of
Experiment
Figure 6.8. The program flow of the positioning control system. The system can
be set in either fully automatic or manual operation.
At the start the program initialises parameters concerning well number and centre to
centre distance, fluorescence angle, communication details etc. If manual control or
timer control is chosen then the system will iterate between the wells of the 96
precursors and one has to manually instruct the fluorescence detector to collect an
EXAFS scan in each well. In automatic control the system does not require any
intervention. Visual observation of the array and the positioning was accomplished
by using a creative webcam in direct viewing angle. The user interface reflects all the
above features and can be seen in Figure 6.9.
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Figure 6.9. User interface, developed in Labview®, for automated positioning
control over x, y, z, and theta angle. Movements are performed in
synchronisation with the acquisition of XANES spectra.
6.6. SAMPLE PREPARATION AND REACTION
PROTOCOL
The catalysts investigated in the demonstration study presented here, contained
different concentrations of Au supported on γ-Al2O3 (AluC Degussa, 110 m2/g) and
on TiO2 (P25, Degussa, 50 m2/g, 70% Anatase, 30% Rutile) (Table 6.1). Incipient
wetness method (impregnation) was used for the preparation of the Au/Al2O3
catalysts.196 Au/TiO2 catalysts were prepared by deposition-precipitation as described
elsewhere.196 Both synthetic methods were based on HAuCl4 solutions as the metal
source.
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Sample
1
2
3
Au wt%
1
0.5
0.25
Support
TiO2
TiO2
Al2O3
Synthesis Method
Deposition/Precipitation
>>
Impregnation
4
6
Al2O3
>>
5
4
Al2O3
>>
6
7
0.5
1
Al2O3
Al2O3
>>
>>
8
2
TiO2
Deposition/Precipitation
Table 6.1. The catalyst compositions of the 8 reactor array. The catalysts were
supported on TiO2 and Al2O3.
The resulting powder materials were dried in vacuum at room temperature for 48
hours. It shall be noted that active Au catalysts require a low residual halide content
(especially chloride Cl-, sulphide S-) since these can poison the catalyst by adsorption
at active sites.217 Solutions of HAuCl4 (overall purity 99.9%) were prepared in
deionised water and contained the desired amount of Au for each catalyst (in wt%).
The catalyst support material was then added and the suspension was stirred and
heated under exclusion of light to 353 K for 16 h. The obtained solid was separated
by filtering, washed with hot deionised water and with 1M NaOH until pH 8 was
reached.
20 nm
Figure 6.10. TEM micrograph of a 4 wt% Au/Al2O3 and particle size
distribution (inlay).
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Elemental analysis (energy dispersive X-ray analysis, EDX) indicated a residual
chlorine content below the detection limit (<0.2 wt%). Average particle size was 1-2
nm for 4 wt% Au/Al2O3 catalysts as determined by TEM (Figure 6.10).
The sequence of process conditions used for testing the catalysts in our automated
system is shown in Table 6.2. The acquisition of the X-ray absorption data
corresponding to the individual process steps took approximately 28 h at station 9.3
of the SRS at Daresbury Laboratory. Note that the SRS is a 2nd generation
synchrotron source; the integration of the system in a 3rd generation synchrotron
system will reduce the data acquisition times by at least an order of magnitude.
Reaction
He Conditioning
CO Adsorption
1st Reduction cycle
He Conditioning
CO Oxidation
>>
>>
O Adsorption
CO Adsorption
He Conditioning
2nd Reduction cycle
CO Oxidation
>>
>>
CO oxidation
Gas Composition
He
10%CO/He
10% CO/He
He
2:1 CO,O2 /He
1:1 CO, O2 /He
1:2 CO, O2 /He
10%O2/He
10%CO/He
He
10% CO/He
2:1 CO,O2 /He
1:1 CO, O2 /He
1:2 CO, O2 /He
Various ratios CO/O2
T oC
RT
RT
RT-110 oC
RT
RT
RT
RT
RT
RT
RT
RT-110 oC
RT
RT
RT
RT
Techniques
XANES/MS
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
Total Time
Time
20mins x 8
20mins x 8
20mins x 8
20mins x 8
20mins x 8
20mins x 8
20mins x 8
20mins x 8
20mins x 8
20mins x 2
20mins x 2
20mins x 2
20mins x 2
20mins x 2
20mins x 2
28 Hours
Table 6.2. Experimental Protocol (He = 100 ml/min, CO + O2 = 120 ml/min,
catalyst weight = 200 mg).
6.7. STATION SETUP
XAS measurements at the Au LIII absorption edge (11919 eV) were carried out at
station 9.3 of the STFC Synchrotron Radiation Source (SRS) at Daresbury
Laboratories (UK).308,309 During the experiments the SRS storage ring operated at 2.0
GeV with a beam current of 150-220 mA, with a daily beam refill mode. The station
employed a fast Si (220) double crystal monochromator, which was detuned to 50%
intensity in order to minimise the presence of higher harmonics. A horizontal plane
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Pd-coated mirror was used to provide harmonic rejection as well as vertical
collimation and focusing. The beam size was manually adjusted to 3 mm × 1 mm
using a XY slit (Huber®). A 635 nm focusing silicon diode laser was aligned with the
X-ray beam to assist calibration while testing the system. XAS spectra were acquired
in fluorescence mode using a recently commissioned 11 element Ge SSD. XANES
spectra were recorded on an energy grid of 22 eV/step in the pre-edge region and 0.8
eV/step in the remaining XANES region, resulting in an acquisition time of 8 min
per spectrum.
6.8. MEDIUM THROUGHPUT MASS SPECTROMETRY
6.8.1. CATALYST PERFORMANCE
To demonstrate the functionality of the multiplexed MS activity measurements we
present data acquired during the investigation of a set of Au/Al2O3 and Au/TiO2
catalysts. As summarised in Table 6.2 XAS data were recorded while the catalysts
were exposed to streams with various CO:O2 concentration ratios at room
temperature. The activation of these catalysts was simultaneously followed by XAS
and these results will be presented in section B below. The concentrations of CO/N2
(m/Z = 28 amu/e0), O2 (m/Z = 32 amu/e0), CO2 (m/Z = 44 amu/e0), He (m/Z = 4
amu/e0), H2O (m/Z = 18 amu/e0), using total flows of 27.5 ml/min per reactor
approximately. The space velocity of each reactor in the system was kept at ca.
5600 h-1 (corresponding to 8500 ml×h-1 per mg catalyst) under all CO:O2 ratios
(Table 2). All catalysts except 0.25 wt% Au/Al2O3 and 0.5 wt% Au/TiO2 exhibited
significant CO conversion rates, which are summarised in Figure 6.11.
It is well known that the conversion efficiency of Au-based catalysts is highly
sensitive to metal particle size and metal dispersion.221 Catalysts containing 6 wt%
and 0.5 wt% supported on Al2O3 yielded very similar CO conversion results. High
metal concentrations in the precursor solution can lead to catalysts that contain large
inactive metal agglomerates that may explain the comparable activities of the two
samples.188 In line with this we observed particle sizes of 5-10 nm in TEM images of
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these catalysts.310 Under the specific synthetic and reaction conditions employed on
our test the best performing catalysts in terms of total CO conversion were the 4 wt%
Au/Al2O3 (rank 1) and 2 wt% Au/TiO2 (rank 2).
CO Conversion
40%
C O :O 2
2 :1
C O :O 2
1:1
C O :O 2
1:2
4wt% Au/A
½wt% Au/A
6wt% Au/A
2wt% Au/T
1wt% Au/T
20%
0%
0.0
2.0
4.0
6.0
8.0
Time (h)
Figure 6.11. Carbon monoxide conversions over an 8 hour period for the five
most active catalysts. The space velocity for each reactor was 6600 h -1 or
8250 ml g-1 h-1. Note that Au/A and Au/T correspond to Au on Al2O3 and
Au/TiO2 respectively.
All catalysts showed slow deactivation within the time frame (ca. 8 h) of the
experiment. The main reasons for Au deactivation over time are particle sintering
and, especially at room temperatures and lean oxygen conditions, formation of stable
adsorbate e.g. carbonates, carbonyls and subsequent carbon nucleation and
growth.217,254 The two best-performing catalysts namely the 4 wt% Au/Al2O3 catalyst
and the 2 wt% Au/TiO2 remained active after reaction periods of 6-8 h. However, the
rate of deactivation is slower on the TiO2-supported catalyst. The turnover
frequencies (TOF, here obtained by dividing the number of converted CO molecules
per second by the total number of Au atoms in the catalyst) associated with the
catalysts are given in Figure 6.12.
It is known that varying the CO:O2 ratio influences the TOFs of Au catalysts
significantly.188 Figure 6.11 and Figure 6.12 demonstrate that the medium throughput
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XAS/MS reactor array system allows detecting such differences with high
sensitivity. In particular, the data obtained here indicate that Al2O3-supported Au
catalysts perform better with O2 rich stoichiometry (Figure 6.12).
0.12
C O :O 2
2 :1
C O :O 2
1:1
C O :O 2
1:2
4wt% Au/A
½wt% Au/A
6wt% Au/A
2wt% Au/T
1wt% Au/T
TOF s -1
0.08
0.04
0.00
0.0
2.0
4.0
6.0
8.0
Time (h)
Figure 6.12. Carbon monoxide turnover frequencies TOF as a function of
reactant ratio and time. Note how the TiO2 based catalysts exhibited a higher
efficiency in converting CO to CO2 molecules in a leaner oxygen environment.
Although the absolute CO conversion of the 4 wt% Au/Al2O3 catalyst is higher than
that of the 2 wt% Au/TiO2 (Figure 6.11) the latter has a higher TOF because it
contains only half the Au concentration (Figure 6.12). However, the TiO2-based
catalyst had higher activity than the Al2O3-supported catalyst only in an O2-lean
environment. A gradual reduction in the conversion rate over time due to catalyst
deactivation is also expected and can be followed by the multiplexed MS. For
example, the most active catalyst (4 wt% Au/Al2O3 large triangle, Figure 6.12)
shows a large reduction in activity when subjected to oxygen rich environments and
may be related to the effect that increased oxygen coverage might have on the Au
atom active sites (catalyst poisoning).
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6.8.2. OSCILLATIONS DURING THE CO OXIDATION OVER AU
It is well established that the oxidation of CO with heterogeneous noble metal
catalysts can exhibit oscillatory reaction rates (Chapter 4).1 However, to date there
has not been any report of sustainable rate oscillations over Au catalysts. During the
characterisation of the library of the 8 Au catalysts7 the 4 wt% Al2O3-supported Au
catalyst exhibited highly regular and sustained rate oscillations (Figure 6.13).
CO:O2
0.5:1
Ion Currents (au)
Normalised CO2 ion currents (au)
4
3
He
100%
CO:O2
0.5:1
4
CO
1.0
3
0.5
CO2
0.0
2
2
23
CO:O2
1:1
1
24
25
Time (h)
CO:O2
0.5:1
26
1st reaction cycle
1
2nd reaction cycle
CO:O2
0.5:1
0
0
5
6
7
8
Time (h)
9
10
11
Figure 6.13. CO2 ion currents measured using MS during room temperature
CO oxidation over a Au/Al2O3 catalyst. Note the sudden onset of sustained
oscillations after 8.7 h during the first reaction cycle. The trace for the second
reaction cycle was obtained after interrupting the first cycle and exposing the
catalyst to a stream of 10% CO in He at room temperature. The inset shows
that oscillations can also be recovered after interrupting the oscillatory reaction
and flushing the reactor with He.
The rate of CO conversion by the catalyst at 298 K was initially approximately
0.06 molCOs-1 × molAu-1, which compares well to previously reported values for
Au/Al2O3 catalysts.8 The catalyst deactivated gradually, resulting in reduced reaction
rates of approximately 0.03 molCO × s-1 × molAu-1 after 5 h (Figure 6.13). Similar
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deactivation of Au/Al2O3 catalysts has previously been reported to be associated with
a combination of carbonate deposition and Au sintering.9
After operating the catalyst under several different CO oxidation conditions we
observed the onset of rate oscillations with a period of typically 5-15 min during
operation under O2-rich conditions. The highly regular oscillations could be
sustained for at least 24 h, with a stable waveform throughout (the data in the inset of
Figure 6.13 stem from the end of an experimental run over a whole day). The
oscillations observed in the CO partial pressure were exactly the inverse of the
oscillation of CO2 (inset of Figure 6.13), indicating the absence of significant
amounts of intermediate species. This is perhaps not surprising, because the turnover
frequencies with which individual CO molecules are converted into CO2 are on the
order of 0.05 s-1, which is much faster than the observed rate oscillation periods. As
expected for an exothermic reaction, the rate oscillations were also approximately in
phase with slight oscillatory variations of the catalyst temperature, between 296.7 K
and 297.0 K, as monitored by a thermocouple inserted into the catalyst bed.
The gas phase composition was varied to address the origin of the oscillatory rate
variations. First, after an extended period of oscillatory reaction the reactor was
purged with He for approximately 45 min. When the reactant gas stream was reintroduced no oscillations were immediately observable (inset of Figure 6.13).
However, the rate of CO oxidation was higher than the maximum oscillatory rate
before the He treatment of the catalyst. Over a period of approximately 1 h the
catalyst deactivated again slowly, until the oscillatory CO oxidation regime was
recovered (inset of Figure 6.13). This sequence of observations suggests that the
replacement of reactants by inert gas changes the catalyst surface composition and/or
its microstructure and facilitates the recovery of higher catalytic activity. Structural
changes at the Al2O3 surfaces, their interface with the Au particles, or of the
morphology of the Au particles, including their dispersion, may occur.10
Alternatively, the decomposition or desorption of blocking species, such as
carbonates, or of promoting species, such as sub-surface oxygen species in the Au
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MEDIUM THROUGHPUT IN SITU XAS
particles11 may take place. Data for the catalyst microstructure under reaction
conditions would be required to confirm or rule out any of these possibilities.
Additional experiments established that the waveform, the amplitude and the
frequency of the oscillations were sensitive to the partial pressure of O2 (Figure
6.14).
CO:O2 Ratio
40%
0.33
CO conversion %
30%
0.4
20%
0.44
10%
0.5
0%
0
5
10
15
20
25
Tim e (m in)
Figure 6.14. Non linear CO conversion response of a 4wt% Au/Al2O3, recorded
using different reactant ratios. Sustained oscillations were observed at CO/O 2
ratios between 0.4 and 0.5. Note how the overall reaction rate decreases as the
CO:O2 ratio increases.
Most importantly, rate oscillations were stable only during the transition from nearstoichiometric O2 partial pressures to O2-rich gas mixtures (Figure 6.15). The CO
conversion at near-stoichiometric CO:O2 ratios of 2 and 1 were essentially the same,
while decreasing the CO:O2 ratio to 0.66 approximately halved the reaction rate.
Further increase of the O2 excess, lowering the CO:O2 ratio to 0.50, was
accompanied by the development of strong rate oscillations between low conversions
of 15% and 3%. Under even more O2-rich conditions the reaction rate rose steeply as
a function of O2 partial pressure. Oscillatory rates were maintained up to CO:O2
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MEDIUM THROUGHPUT IN SITU XAS
ratios of approximately 0.40, but their amplitude became increasingly weaker, until
only non-oscillatory CO conversion rates were observable at higher O2 excess.
Oscillatory Region
40%
35%
Tc=297 K
20%
15%
10%
5%
20%
4
15%
3
10%
2
5%
1
0%
0
0.60 0.45 0.30
Frequency (m Hz)
25%
Amplitude (%)
CO Conversion %
30%
0%
1.8
1.4
1
CO:O2 Ratio
0.6
0.2
Figure 6.15. Dependence of CO conversion on gas phase stoichiometry. Filled
circles represent steady-state reaction rates under non-oscillatory and
maximum reaction rates under oscillatory conditions. Open circles denote
minima of the oscillatory reactions rate. The inset summarizes oscillation
amplitudes (open squares) and frequencies (filled squares) as a function of
CO:O2 ratio.
The slight temperature variations we detected in the catalyst bed show that the
reaction system is somewhat non-isothermal. In non-isothermal systems transport
limitations can arise because of the interplay of adsorption, desorption and diffusion
events at the level of the catalyst grains, the catalyst bed, or of the entire reactor.12 A
thermo-kinetic mechanism arising from heat and/or mass-transport limitations may
possibly provide an explanation for the observed oscillations. In contrast, for
isothermal systems the mechanistic origin of CO oxidation rate oscillations over Pt
and Pd has been attributed to mechanisms involving surface reconstructions and the
population of subsurface oxygen sites.1-3,12,13
Interestingly, our observation of the distinct variation of the reaction rate and
oscillatory frequency as a function of O2 pressure (Figure 6.15) parallels the O2pressure dependence in Pt systems.12 In fact, the rate oscillations observed on Au
could also be rationalized in terms of recently reported11 enhancements of CO
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MEDIUM THROUGHPUT IN SITU XAS
sticking on Au in the presence of sub-surface oxygen species: CO is known to adsorb
on Au more strongly than O214, so that its concentration will likely exceed that of O2
species under O2-lean, stoichiometric and near-stoichiometric conditions. Increasing
the O2 partial pressure may facilitate more efficient competition for adsorption sites,
and may therefore explain the decrease of the reaction rate at CO:O2 ratios between
1.0 and 0.6. However, very high O2 surface concentrations may facilitate the
population of sub-surface sites in the Au particles, which can stabilize adsorbed CO11
and may allow the recovery of activity at CO:O2 ratios below 0.6. Oscillatory
behaviour could be associated with the bi-stability during the partial population of
subsurface sites in the intermediate pressure range.
6.9. MEDIUM THROUGHPUT IN SITU XAS
EXPERIMENTS
6.9.1. IN SITU CATALYST ACTIVATION
The freshly prepared catalysts contain AuxOy and AuxOHy species on the oxide
supports. The activation of the catalysts was accomplished by subjecting them to a
reducing environment (10% CO/He) at elevated temperatures (380 K). This caused
the formation of reduced Au species which are, for the catalysts investigated here,
the most active form of Au in the oxidation of CO.288 The evolution of the chemical
state of the Au component during temperature programmed reduction (TPR) of the
most active catalyst was followed by XANES measurements (Figure 6.16). An
important indicator for catalyst activation is the reduction of the oxidation state of Au
atoms. In particular it can be seen how Au3+ species are reduced in 10% CO as
indicated by a decrease of the near-edge feature at 11925 eV. Linear combination
analysis (LCA) using the Athena program300,311 was employed to help determine the
fraction of cationic Au3+ species present at different temperatures (Figure 6.16,
inset). No evidence for the formation of significant amounts of Au in an oxidation
state of +1 (Au1+) was evident from the linear-combination analysis, and the
existence of 3 isosbestic points in the data. This absence of evidence for Au1+ species
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is perhaps not too surprising, as Au favours the formation of the oxidation states 0
and +3 in the environment of oxygen and hydroxide species.
A u2O3 (A u3+reference)
2.5
303K
333 K
2.0
Absorption (au)
363 K
378 K
1.5
383 K
A u 4wt% (A u0 reference)
0.5
Au 3+ Fraction
1.0
60%
40%
20%
0%
290 315 340 365 390
Temperature (K)
0.0
11900
11950
12000
12050
Energy (eV)
Figure 6.16. Au LIII XANES series of the Temperature Programmed Reduction
of a 4 wt% Au/Al2O3 catalysts under a CO:He stream. Inset: Linear
Combination Analysis of the XANES series taken during reduction. At the end
of the reduction procedure the catalyst is composed almost entirely of zerovalent Au atoms.
It shall be noted that the catalyst was already partially reduced before exposure to
CO as indicated by the weakness of the near edge resonance relative to that of the
Au2O3 standard. The LCA analysis indicates that the untreated catalyst contains Au3+
and Au0 approximately in a 1:1 ration. The thermal decomposition of Au3+ to Au0
begins at ca. 320-330 K (Figure 6.16, inset). A temperature of 370 K was sufficient
to convert Au quantitatively (>99.7 ± 4.9 wt%) to the zero-valent state.
These findings are in line with experimental results from the preparation of CO
oxidation Au catalysts reported elsewhere217 and demonstrate that the HT XAS/MS
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reactor array system can follow quantitatively and with high sensitivity transient
structural phenomena using X-ray absorption spectroscopy.
6.9.2. QUANTIFICATION OF SPECTRAL QUALITY
Standard statistical tools were employed to assess the quality of the collected XAS
data body.312 Figure 6.17 provides a representative overview of the quality of the
data acquired during our demonstration run. The left hand side contains normalised
XAS data from a TiO2-based catalyst that were obtained under various input stream
conditions, whereas the right hand side spectra were recorded from an Al2O3-based
catalyst.
3.0
CO:O| 1:1
CO:O| 1:1
4.0
CO:O| 2:1
CO:O| 2:1
CO:O| 1:2
2.5
3.5
CO:O| 1:0
CO:O| 1:2
He
3.0
CO:O| 1:0
CO:O| 1:1
Normalised Intensity (a .u.)
Normalised Intensity (a .u.)
2.0
He
CO:O| 1:1
1.5
CO:O| 2:1
303 K
CO:O| 2:1
2.5
303 K
383 K
2.0
378 K
363 K
1.5
1.0
333 K
303 K
1.0
0.5
6 w t% Au/Alum ina
0.5
0.0
11900
12000
Energy (eV)
12100
0.0
11900
1 w t% Au/Titania
12000
Energy (eV)
12100
Figure 6.17. XAS spectra at various conditions. Left: Titania (TiO2) based
catalyst Right: Alumina (Al2O3) supported catalysts respectively.
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It is well known313 that for fluorescence yield detection the intensity is proportional
to the X-ray absorption coefficient and as an extension to the metal concentration of
the absorber. Photon re-absorption and scattering, interference effects (EXAFS) and
sample inhomogeneities can affect the measured intensity of the reemitted
photons.314 The quality of the data was automatically assessed using the absolute
edge step values of all measured XAS spectra (Figure 6.18.).
2
0.08
Standard deviation
Edge Step (a.u.)
0.04
0
0%
2%
4%
6%
1
1.00
R2
0.95
0.90
Titania Alumina
0
0%
2%
4%
Au Concentration
Total
6%
Figure 6.18. Absolute edge step values extracted from 56 XAS spectra at various
reactant conditions and plotted against corresponding nominal concentrations
of the Au catalysts. Top Inset: Standard deviation of edge step values for each
catalyst. Bottom inset: Comparison of the correlation coefficient (R2) for TiO2
and Al2O3supported catalysts. Bright red squares and blue circles indicate TiO2
and Al2O3 supported catalysts respectively.
There is a multitude of materials characterisation techniques used for measuring the
loading of catalyst constituents such as flame ionisation,315 inductively coupled
plasma-atomic emission spectrometry (ICP-AES),316 inductively coupled plasmamass spectrometry (ICP-MS),316 and energy-dispersive X-ray analysis EDX.317 Yet
these techniques require transferring the sample to different instrument. This is not
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always feasible in high throughput characterisation experiments because numerous
samples are synthesised in an in situ array.
As a result all additional information gained from X-ray absorption measurements
such as detector saturation effects/non-linearity, reproducibility of preparation
techniques and approximate metal loading is highly desirable.
Visual examination of the fluorescence XANES spectra of the Au catalysts revealed,
as expected, detector saturation effects at high metal concentrations. A linear
correlation between edge step and nominal Au concentration was found up to a metal
loading of approximately 4 wt% (Figure 6.18, dotted line).
Standard statistical tools such as standard deviation (σ2) and the coefficient of
variation (R2) helped quantify observed variations and trends.312 The standard
deviation of the edge step values was calculated for each Au catalyst (Figure 6.18,
top inset). The σ2 values of the edge step heights can be used as a measure of detector
stability i.e. the edge step should remain constant for the same sample under the
same conditions. The standard deviation values vary for each catalyst, however, most
σ2 values were below a predefined limit or baseline which was unique for that
experimental setting. In the demonstration experiment the baseline was c.a. 0.02 a.u.
(Figure 6.18, top inset).
Interestingly, the edge step values of XAS spectra corresponding to a 4 wt% catalyst
yield a three fold higher σ2 than other catalysts which indicates that the photon
counts may have been close to the saturation point of the detector electronics (SSD
specific, here 75000 counts × s-1).
Additionally, the X-ray absorption cross-section of highly inhomogeneous or mixed
metallic species can result in variability of the edge step values. Other signal
distortion effects such as self absorption (SA)314 can also play an important role on
the quality of XAS spectra especially when higher concentrations of the main
absorber are being examined. Closer examination of spectra corresponding to the
4 wt% and 6 wt% Au/Al2O3 catalyst, revealed higher σ2 and R2 values which
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corresponds well with observed SA effects and the intense detector saturation for
samples over 4 wt%.
The coefficients of variation (R2) values were evaluated from edge step versus
concentration plots as an additional objective measure of spectral quality. A first
order polynomial was fitted to edge step values from different catalyst groups and
excluding the contributions from the 6 wt% Au/Al2O3 sample which revealed intense
detector saturation. The three groups of goodness of fit (R2) values (Figure 6.18,
bottom inset) correspond to the edge step fit quality of Au on TiO2 (Group 1), Au on
Al2O3 (Group 2) and also the total goodness of fit using the combined AuTiO2 and
AuAl2O3 edge step values.
Au/TiO2 supported catalysts approximated better than Au/Al2O3 to the theoretically
achievable first order polynomial, under the specific experimental, synthetic
conditions and with Au loading between 0.5-4 wt% (Figure 6.18, bottom inset). This
was found to be due to the intermediate washing step in the synthesis of the Al2O3
supported catalysts created a quantitative uncertainty to the actual amount of the
adsorbed Au.
The medium throughput XAS/MS reactor array system allowed the acquisition of a
large data body which, as shown, can be used quantitatively to assess the quality of
the XA spectra.
6.10. CONCLUSION
Incorporation of the medium throughput-XAS system in the existing beamline
infrastructure of station 9.3 of the STFC Synchrotron Radiation Source (SRS) at
Daresbury Laboratories (UK) allowed the efficient use of synchrotron resources and
increased the repeatability of in situ X-ray absorption spectroscopy experiments. Our
system facilitated the discovery of rate oscillations in the low-temperature oxidation
of CO by a Au/Al2O3 catalyst during the transition from stoichiometric to O2-rich
operation.
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We have hypothetically suggested that the underlying bi-stability could be
rationalized by a mechanistic switch due to the population of sub-surface Au sites
under O2–rich operation. The plausibility of this hypothesis should be experimentally
assessed by spectroscopic in situ studies.
The incorporation of statistical benchmarks facilitated the quantitative analysis of the
spectral quality of X-ray absorption data.
The findings demonstrate that the medium throughput XAS/MS reactor array system
can be used quantitatively and with high sensitivity in the characterisation of
transient structural phenomena using X-ray absorption spectroscopy.
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7. HIGH THROUGHPUT IN SITU XAS
This chapter describes the latest and completely updated versions of the control
software and XAS and MS results which were collected from experiments that took
place in beamline 12BM of the Advanced Photon Source (Chicago, US). The
infrastructure had a logical progression towards complexity and scaling to a full high
throughput XAS screening system capable of automatically collecting simultaneous
catalytic and structural data from 96 catalysts. The first part of the chapter is focused
on the software and hardware infrastructure: (i) the positioning software system, (ii)
software for automated analysis of the gas products, gas control and temperature
control and (iii) software for mass spectrometric analysis on multiple reactors (Figure
7.1). The second part contains results on high throughput mass spectrometry and Xray absorption analysis.
Parallel Flow Reactor
Communication
Gas Flow
Multiple
Gas Inlets
Output
Streams
Gas 1
……
……
Gas 2
Multiplex
Mass Flow
Control
Multiplex
Distribution
Valve
4 Channel
Temperature
Control
PROFIBUS
Gas 3
Serial
XYZ control
Serial
Gas 4
OPTICAL FIBER
Gas Analysis
System (MS)
Main Control System
Daresbury Intranet
GATE
WAY
Station 9.3 Computer
Figure 7.1. The overview of the last generation of an in situ HT XAS screening
system.
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The software base of the high throughput experiments consists of more than 60 subprograms and for the purpose of clarity discussion will be limited to an overview of
the main functions.
7.1. CONTROL AND ACQUISITION SOFTWARE (CAAS)
FOR X-RAY ABSORPTION SPECTROSCOPY
The main function of the control and acquisition software or CaAS is the ability to
create
virtual
four
dimensional
locations
where
individual
spectroscopic
measurements can be captured. The high positional resolution of the stage provides
the possibility to create high density two dimensional maps of various chemical and
structural properties at the sub micron level (subject to beam size, sample type etc).
The positioning functions are synchronised with the beamline servers in Daresbury
SRS and Argonne APS synchrotrons and allow complete automation using a number
of characterisation techniques such as ex situ and in situ XANES, EXAFS and XRD.
Please note that not all combinations of different motors with different beamline
protocols and different movement profiles have been implemented.
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EXAFS Acquisition
(16)
HIGH THROUGHPUT IN SITU XAS
XRD Scan
Setup (15)
XML
Info File
(17)
Customisable
scripts (GDA) (15)
Plate/Step
selection
(4)
Plate angle
selection
(5)
Manual
Control
(6)
Plate
Orientation
(8)
Beamline
Synchronisation
(14, 15)
Absolute
position
setup
(10-13)
Cell setup
and scan
start
(7 -9)
Low level
Motor
settings
(1 -3)
Figure 7.2. The User interface of the CaAS software. Functions are numbered
and correspond to explanations in the text.
What follows is an overview of the available functions and the combinations that
work (Figure 7.2). After the overview, two case studies are going to be presented to
allow a better understanding of the software.
Please note software functions in Figure 7.2 are consecutively numbers and
correspond to the numbering of the functions described below.
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7.1.1. MAIN PROGRAM FUNCTIONS
CaAS is compatible with Parker and McLennan type motor drives. Communication
is accomplished via the RS232 protocol. The following functions are applicable to
both McLennan and Parker motor drives.
1. RS232 port initialisation and automatic discovery of a motor drive connected
to the serial port. The Parker motors have been also installed with “position
maintenance” feature. In the scenario that a stall has occurred (miscalculation
of distance due to high opposing force i.e. large weight) the servo motors will
perform corrective movements until the encoder position feedback matches
the given position values.
2. A User interface that allows low level communication with the motor drives
according to the communication protocol.
3. Ability to create various stepper motor translation settings (steps/mm) for up
to four motors (X, Y, Z, Theta).
4. Ability to save new array dimensions such as SBS standard 96 well-plate and
others.
5. Automatic translation of distances when array is placed at a known angle
with respect to the X-ray beam.
6. These buttons allow manual movement control in relative mode in: X+,X,Y+,Y-,Z+,Z-,Theta-, Theta+, Diagonal+ and Diagonal-.
The following functions (7-9) are related to automatic array positioning and
assume the use of an accurately machined sample plate such as a SBS standard
96 well plate or equivalent. XYZ coordinates for each of the positions are created
relative to each other and are not editable by the user.
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7. Predefined movement profiles can be created based on rectangular arrays of
arbitrary size. The movement profiles are based on a horizontally aligned
detector only.
8. The orientation function defines the direction of a movement profile.
9. A movement profile can be followed either Manually, with automatic
synchronisation with XAS and XRD data acquisition or by timer.
The following functions are available to Parker drives only.
10. Absolute positions for all 4 motors are updated continuously using values
from the internal position encoders. The position values have an uncalibrated
resolution of 100 nm and are presented in mm (e.g. X =-10.9993 mm).
11. In absolute positioning mode the software uses the coordinates of the
measurement points in the table. These can be created manually by defining
X, Y, Z and theta positions. They can also be edited and saved for later use in
a spreadsheet compatible format.
12. The positions table can be followed completely automatically. An arbitrary
number of XAS spectra (and XRD patterns) can be recorded on each
individual point on the table. The stage can collect spectra on samples of
arbitrary shape.
13. Setting a calibration point allows movement repeatability when coordinates
saved for a particular array are recalled in different experiments.
The following functions are related with the beamline synchronisation at
different synchrotrons.
14. This function is specific to beamline 12BM at APS. The acquisition of XAS
data can be controlled simultaneously with positioning. The program
communicates directly with the “back end” control and acquisition software
which controls the detector and monochromator instrumentation of beamline
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12BM. This function works by using a script (.mac) which is incorporated in
the current scan recording parameters. The script sends rudimentary “start”,
“finished” clear text commands through the internal TCP/IP network to allow
the CaAS software to be able to start a scan and recognise when the scan is
finished.
The following functions are specific to Daresbury STFC and Diamond Light
Source.
15. XAFS and XRD data can be recorded simultaneously. The program
communicates directly with the GDA control and acquisition software which
controls all the instrumentation beamlines in Diamond and Daresbury Station
9.3. The program was tested in Daresbury station 9.3 using the GDA server.
All functions are transferable to Diamond beamlines with minimal software
changes. The user can also manually send fully customisable scripts which
are compatible with the GDA server Jython interpreter.
16. XAS raw data can be collected during acquisition. Although development is
still needed in this part, the program already records the XAS data points and
performs eV conversions according to current monochromator settings. It
also calculates concurrently the X-ray absorption in transmission and
fluorescence mode according to the user selected detector channels. This part
could allow effectively adaptation of recording parameters (scan time, auto
calibration etc) and recognition of interesting features while the data are
being recorded. Also, by incorporating the ifeffit library and additional
modules CaAS could export and visualise the normalised spectrum the
moment the raw spectrum is being recorded.
17. Ability to create an information file about the materials that are being
examined and their associated XAFS, XRD files. The file contains
information on time, well position, current file being recorded, type of scan,
and other info. This file is readily converted to XML, with tools we have
from IT Innovation, and the resulting file can facilitate data mining. There is
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also a web interface in place for uploading the collected data in a central
secure database server. This function works only if CaAS is allowed to
automatically control the XAS scan acquisition (instead of the beamline
software). If manual control is required (no subsequent automatic
movements) it can be accomplished by setting up and starting one single scan
in the CaAS. This would allow the software to harvest information from the
GDA server and collect XAS/XRD data only at the current position.
7.1.2. SOFTWARE OPERATION USING MCLENNAN MOTOR DRIVES
The software operation will be explained from the user point of view using 2 case
studies. The first case study assumes that the combined XAS, XRD experiment takes
place at station 9.3, of Daresbury STFC using McLennan motor drives.
Communication and drive setup Instructions
The initial operation of the software is dependent upon setting up the RS232 com
port which the motor drive is connected to. If no motor drive is connected on the
selected port a warning will appear (Figure 7.3).
1
2
4
5
3
6
Figure 7.3. The initialisation of motor drive communications in CaAS. Numbers
correspond to explanations in the text.
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The user follows the instructions as numbered (1-6). In the “Motor Controller Setup”
tab the drive and the motor translation pre-selected settings can be chosen (steps/mm
ratios)(McLennan an Parker). The communication can then be restarted. If no
warning appears the communication with the drives was successful. The translation
settings are customisable for any gear combination for a Parker or McLennan drives.
Plate setup and Manual Movement Control Instructions
1
4
g
2
6
5
3
7
Figure 7.4. Setting up the movement control instructions in CaAS. Numbers
correspond to explanations in the text.
The user can then setup the cell to cell distance and solid detector angle. All units are
set in mm, mdeg. There are already pre-selected settings for an SBS standard,
BiMoVOx arrays etc. If any of the selections are clicked the settings are sent
instantly to the motor drives (Figure 7.4).
The user highlights the desired plate configuration, and enter other details such as
“Z step to define the row-row distance, Y and X steps which correspond to the
column - column distance (10 mm), the angular step of the sample holder angle (e.g.
1000 mdeg) (Figure 7.4). The plate angle selection (e.g. 45 deg) allows geometric
calculation of a plane parallel to the detector.
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The position the corresponding axis will change once any of the appropriate “Move”
buttons is clicked.
The “X-Y Axis correlated” enables or disables the angular correction when the plate
is on an angle. It can be left correlated (blue) in all cases. If the detector / sample
angle is not crucial then it can be set to 0 deg and the plate at 90 degrees with respect
to the X–ray beam.
Automated Movement Control
If the xyz stage is rotated 180 degrees (X-rays coming from the left) then select
Orientation number 2 so that the automatic movement direction is from the top right
hand cell to the bottom left hand side (looking from the detector side). At present, the
software supports only horizontally placed fluorescence detectors.
If the actual xyz stage remains physically with the same orientation then changing
the orientation setup to 2 will change the direction of movement from top left hand
side to bottom right hand side (Figure 7.5).
1
2
Figure 7.5. Orientation setup in CaAS. Numbers correspond to explanations in
the text.
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The direction of movement can be defined by clicking on the “Orientation Setup”
tab. This defines the automatic movement as in “Orientation 1” (Figure 7.5). If
“Orientation 2” is selected then the movement is reversed.
1
4
2
8
5
3
6
7
Figure 7.6. The initialisation of automatic movements in CaAS. Numbers
correspond to explanations in the text.
Once the calibration process is complete i.e. aligning the top right cell with the X-ray
beam (and laser beam). A trial run can be setup as following:
1. The user sets up the scan (“Define & Start Scan” tab)
2. The user then enters the required parameters (i.e. number of columns, number
of rows (4 × 4),“Start column” and “Start row” to “1”) (Figure 7.7).
3. Click the “Positioning is Enabled/Disabled” so that the green Positioning is
enabled” is visible.
4. Set the time interval defines the delay between the automated movements.
5. The “Reset Timer” stops and then initialises the timer.
6. The refresh reset array button resets the internal positions and sends the latest
“Plate setup values” (cell centre-centre distances) to the stage.
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7. Click on the “Next cell” button. The stage should move immediately to the
next cell i.e. column 1, row 2.
1,1
1,1
1,2
1,2
Figure 7.7. Testing the automated movement at different orientations.
8. By pressing the “Next cell” button (one every 2 sec) the plate positions
changes in the following fashion: (Column, Row) (1,1) (1,2) (1,3) (1,4) (2,1)
(2,2) (2,3) (2,4) … (4,1) (4,2) (4,3) (4,4) (1,1).
9. Clicking the “Start timed loop” will initiate a timed loop which will repeat the
movement profile as explained in point 8.
Setting up the beamline synchronisation
Provided a network connection with a compatible beamline computer is established
sample positions can be synchronised with the acquisition of XAS and XRD data.
1
5
2
3
4
6
Figure 7.8. Synchronisation of CaAS with the beamline computer. Numbers
correspond to explanations in the text.
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The procedure follows the guidelines below:
Selection of the appropriate communication protocol in the “Connect to Beamline
Server” tab. The “GDA with display” protocol provides the most functionality
(Figure 7.8).
The “GDA with Display” protocol enables acquisition of XRD and XAS data.
Additionally, it is compatible with meta-data archiving and with the GDA server in
Station 9.3, Daresbury SRS.
The “GDA with Script” protocol enables the fully customised setup of any scan that
is supported by the GDA server. “Daresbury 9.3” is a legacy protocol which was
used before the GDA server was developed.
“APS 12BM” is a rudimentary protocol which supports simple “start XAS scan”,
“XAS scan finished” feedback commands. It works only in conjunction with a
specially prepared script and is compatible with the EXAFS acquisition server in
beamline 12BM, APS Chicago.
The “Connect” button establishes a clear text TCP network connection between the
CaAS, and the GDA server through the HTP gateway computer. If the connection is
successful the disconnect button will become activated.
The connection can be validated by sending a random string e.g. “hello” and
observing the response of the terminal window on the beamline GDA UI. It should
respond with an error stating it cannot recognise the “hello” string. All the responses
from the GDA server (or another beamline computer connected to that port) can be
seen in the “GDA terminal window”.
Setting up XAS, XRD parameters in CaAS
The following setup assumes the use of a HOTWAX XRD detector and a multi
element solid state detector (Figure 7.9).
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1
4
2
8
3
5
6
10
7
11
19
9
20
12
13
14
15
16
17
18
Figure 7.9. Initialising the monochromator, XAS and XRD parameters. CaAS.
Numbers correspond to explanations in the text.
All the settings define the acquisition and conversion of EXAFS scans from the XAS
subprogram. This is accessible from the top level menu selection “XAS”. The default
settings (10-16) define the variable definitions in the GDA file format and the way
the program will convert energy, and X-ray absorption values. The user cannot plot
or save the extracted XAS data, nonetheless current data can be viewed in array
form.
Setting up the XML file parameters of the comma separated info (csv) file in
CaAS
The following settings allow the user to create automatically a clear text file which
contains information such as current XRD file, XAS file, time stamps, file
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description, scan type and dataset. Optional definitions such as temperature and gas
compositions are blank. The program creates the text file according to the definition
of the xsd schema which was developed by IT Innovation at Southampton University
for the High Throughput Project. This text file can be subsequently converted to
XML and uploaded to the HTP project data server (Figure 7.10).
2
1
3
4
Figure 7.10. The initialisation of the archiving facilities in CaAS. Numbers
correspond to explanations in the text.
Button 4 (Figure 7.10) gives information on the way the wells are represented in the
XML file. This setting has no effect anywhere in the program. It is used only as an
additional reference. However, it shall be noted that the SBS standard dictates letters
A-.. to rows and numbers 1-... to columns. The internal program reference point (at
orientation 1) is Column 1 Row 1 at the top right hand side corner (Figure 7.11).
5
6
7
8
9
Figure 7.11. Manual insertion of meta-data information (“Current Cell
Description”).
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Button 9 manually appends the comma separated (csv) file with current information.
A subset of a typical csv file that the program creates can be seen in Table 7.1.
dataset
64
A
1
R4567.dat
dataset
65
A
2
R4568.dat
dataset
66
A
3
R4569.dat
dataset
67
A
3
XRD.xyz
dataset
68
A
4
R4571.dat
dataset
69
B
1
R4572.dat
2007-0401T19:05:01
2007-0401T19:05:06
2007-0401T19:05:08
2007-0401T19:05:37
2007-0401T19:05:41
2007-0401T19:05:44
1
1
1
1
1
1
Test Dares
9.3
Dares 9.3
Test
Test Dares
9.3
Dares 9.3
test
Dares 9.3
Test Dares
9.3
XAS
XAS
XAS
XRD
XAS
XAS
Table 7.1.Typical subset of the archiving of data according to an xml schema.
The file definition is compatible with the HTP XML schema. More information on
the schema can be found in the “read me” files that came with the XML wrapper (IT
Innovation).
Automatic acquisition of XAS and XRD data
Assuming the user has gone through the entire procedure of setting up motor drive
values, aligning the XYZ stage, tested the clearance to sensitive equipment, checked
hardware limits, setup XRD and XAS parameters in GDA software and CaAS
software, has tested the network connections between the various software, initiated
a TCP connection to GDS, and has ran a series of test spectra, it is now possible to
start the automated routine (Figure 7.12).
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1
I
n
r
e
2
l
a
3
4
ti
v
e
(
i
n
c
r
5
e
m
6
e
9
7
8
n
t
a
l
Figure 7.12. Starting the automatic XAS, XRD data acquisition in CaAS.
)
Numbers correspond to explanations in the text.
p
Recording Status. This represents the acquisition ando processing of XAS spectra and
s
file information data.
it
i
o
Active Status. The LED represents when TCP communication
exchange is initiated.
n
This is after a small time (10-15 seconds) delay to allow
for the time uncertainty in
i
n
monochromator movement.
g
m
The automated acquisition routine will conclude after the required number of XAS
o
and XRD spectra multiplied by the number of cellsd have been recorded. The XYZ
e
stage will return to the initial position and all the automated routine variables will be
m
o
reset.
v
e
m
e
n
t
s
168
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r
e
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Absolute Mode of Motion
The CaAS motor driver provides additional functions for Parker drives such as
absolute positioning, absolute automated positioning and positioning status. In
absolute positioning mode movements are defined with respect to a reference point.
6
I
3
n
1
I
r
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Ir
I
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n
a
4 rl
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n
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Absolute
positioning
in
CaAS
provides
v
(i
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positioning
correspond to explanations in the text.e n
n
l) ematrix. Numbers
n
a
c
p (i
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angle. The
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oit
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it
it
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i ncalibrationa button (2) resets the reference
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i
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p
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it nn
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o (6)
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g
subsequently
be recalled by selecting “Open/New XYZ
file”. The matrix
i
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i m
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from
common
m it formato(.asc) and can be edited directly
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i
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169
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d
t m
om
m
n
e
s o
ve
CHAPTER 7
HIGH THROUGHPUT IN SITU XAS
The positional matrix (4) can be followed automatically by selecting absolute motion
(7). Subsequently, select the first required point on the positional matrix and the
number of points the automated routine should follow. The stage will also follow the
position table step by step when the user presses the “Next cell” button.
7.2. GAS AND TEMPERATURE CONTROL WITH
PROFIBUS
A high throughput infrastructure usually requires concurrent control of tens of
instruments. Normal communication methods such as RS232 are not sufficiently
expandable to HT needs. Profibus (PROcess FIeld BUS) is the European standard
related to field process buses. It is defined in the Profibus standard (EN 50170)
which specifies the functional, electrical and mechanical characteristics for a serial
transmission field bus. The purpose of this standard is to network programmable
logic controllers and field devices from different manufacturers, without the need for
time-consuming and complex adaptation work (Figure 7.14).
Figure 7.14. Profibus functionality faced with the OSI model. Layer 2 can be
accessed in different ways. DP, MPI, S7 Standardized Profibus messaging. S5
Messaging developed by Siemens France.
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As Figure 7.1 shows, our high throughput workflow uses the Profibus protocol for
communication between a control computer and various instruments. The control
computer contains a Profibus PCI card which can be setup through an interface
which runs an OPC server (OLE for Process Automation) (Figure 7.15). A Profibus
compatible instrument possesses variables that are called “items”. For example a
MFC controller contains items ranging from flow and temperature to curve
calibration and firmware information. Items can be accessed by an OPC client
computer program.
Figure 7.15. The Applicom® PROFIBUS console. This program is responsible
for setting the PCI card and providing an OPC server that can be accessed from
remote client applications.
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7.3. GAS AND TEMPERATURE CONTROL
Gas and temperature control of catalytic reactions was accomplished by 16 mass
flow controllers (Bronkhorst®, Figure 7.16) and a 4 channel temperature controller
(Eurotherm® Figure 7.17). Profibus compatible instruments were connected with
each other by a 9 pin RS485 type cable which forms a daisy chain (purple cable
Figure 7.16).
Figure 7.16. The external and internal design of the Profibus controlled gas
control box with MFCs. The purple cable and red cable carry the Profibus and
power signals respectively. Two identical units were constructed.
More information about the construction of the MFC boxes can be found in the
instrumentation section of Chapter 6. The temperature controller of Figure 7.17 was
based on a Eurotherm® Mini8 programmable controller with Profibus connectivity.
It has 4 thermocouple inputs and 4 heating element outputs which are controlled by 4
proportional, integral, differentiation (PID) loops.
Figure 7.17. The external and internal design of the Profibus-controlled
temperature control box. A Mini 8 Eurotherm® controller was used.
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Most instruments developed in this project operated on 24 V DC (Figure 7.18). Two
units supplied power to the MFC’s, temperature controller, solenoid valve control
unit (Figure 7.19) and multi-valve assembly discussed later.
Figure 7.18 Switched input power units. Each can supply up to 1 kW of power.
The heating elements of the temperature controller are independently supplied
by the top unit.
Figure 7.19. The solenoid valve control box. It contain transistor-based current
amplifiers. The box is controlled through 24 bit digital signals using a digital
PCI card from National Instruments® (Chapter 6).
The above instruments were controlled by software developed in Labview. The
program of Figure 7.20 is capable of controlling up to 24 solenoid valves, 16 mass
flow controllers and a 4 channel temperature controller.
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Figure 7.20. The gas control interface was made in Labview. Communication
with the MFC’s is accomplished through the PROFIBUS protocol.
The Profibus controlled instruments can also be accessed by any OPC compatible
client. All instruments were initially tested using an OPC client supplied by
Applicom® (Figure 7.21).
Figure 7.21. OPC client supplied by Applicom®. It enables remote access to all
items made available by the OPC server. The operation of 16 MFCs and 4
temperature control loops requires 48 OPC items.
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The PID control loops of the temperature controller of Figure 7.17 were programmed
using
the
iTools®
programming
environment.
Programming
involves
interconnecting control PID loops with input (thermocouple) and outputs (heating or
cooling load, Figure 7.22).
The PID loops contain also an automatic tuning capability based on measuring the
frequency response of the system when a step input is applied. The auto-tuning
facility was accessible through the Profibus protocol and PID loops could be
remotely tuned (Profibus Item “AT enable”).
Figure 7.22. The iTools program by Eurotherm®. It enables configuration of up
to 4 PID control loops for the Mini 8 temperature controller used in the project.
This example contains one input, a control loop, one output and the associated
settings of the control loop.
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Testing of the temperature controllers was accomplished using the iTools OPC
scope. It is an OPC based client that can visualise and access any Profibus
instrument. It also provided logging and charting facilities when first deploying the
controller (Figure 7.23).
Figure 7.23. OPC client with a temperature graph (Eurotherm®). It was used to
verify the operation of the Mini 8 temperature controller.
7.4. MS VALVE DISTRIBUTION MODULE
A high throughput micro-reactor flow cell was developed318 that allows XAS
measurements of 96 catalysts under different configuration of reactants and flow
conditions (Figure 7.24). The cell has provisions for a temperature gradient by
utilising cooling and heating channels.
The glass cell is organised in 4 groups of 16 samples each. Each group has a
common gas input which is connected to 4 MFCs (Figure 7.16). Effluent gases from
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all 96 cells are addressable by multiple input mass spectrometry. Details on the mass
spectrometer data acquisition have already been discussed in Chapter 6.
Figure 7.24. The prototype gas reactor cell enables 96 concurrent reactions
which are individually addressable by XAS and MS. 318
The 96 outputs from the gas cell are directed through 1/16” Teflon tubes to an
automatic multi-valve control apparatus which enables the selection of one gas
output at a time. 10 Klohen® valve modules were incorporated in the system (Figure
7.25).
Each module contained one “1 of 12” valve system. Interconnecting 9 of those
modules allows the selection of 1 out of 96 gas inputs. The gas output of this
apparatus is directed to the mass spectrometer for analysis.
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The valve modules communicated with each other using a RS485 link.
Communication with the control computer was accomplished using one RS232 port
for all 10 modules.
A Labview based program allowed the multiplexing of the valve operation with the
acquisition of MS data (Figure 7.26).
Figure 7.25. The distribution valve system. It is based on 10 Klohen® valve
modules.
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Figure 7.26. The Labview based control system of the MS distribution valves.
Each time a valve changes the control program saves the time stamp and valve and
cell position in a separate file (Figure 7.26). A time server is used for frequent time
calibrations. The recorded file is later used for de-multiplexing the MS data.
7.5. MS ANALYSIS SOFTWARE
For all HT experiments MS data acquisition of 96 catalytic reactions was
accomplished by saving a single file containing the MS ion currents recorded over
time and for all catalysts. This file contains data that have been multiplexed in space
by the multi-valve apparatus.
The MS data file is then combined with the time stamp values produced by the valve
control program. MS ion currents corresponding to individual reactors are joined
together and subsequently can be visualised and automatically analysed using the MS
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analysis software (Figure 7.27). Typically, the program calculates conversions from
96 catalysts from a 3 day experiment in 10 mins (Figure 7.28).
Figure 7.27. Snapshots of the MS analysis software. The user has the ability to
visualise and concurrently analyse catalytic data of up to 96 samples.
Figure 7.28. Using the MS analysis program the user can immediately visualise
in 2D and 3D and subsequently export the catalytic performance of the selected
catalysts (white denotes high activity).
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7.6. HT IN SITU X-RAY ABSORPTION SPECTROSCOPY
7.6.1. INTRODUCTION
The feasibility and applicability of the developed system was assessed using in situ
XAS on alumina supported, Au-Pt-Cu tri-metallic catalysts. Experiments took place
in beamline 12BM of the Advanced Photon Source. During the experiments a
multitude of XAS and MS data were collected some of which will be discussed in the
following pages.
7.6.2. CATALYST PREPARATION
In order to avoid the presence of chlorides and to eliminate washing steps that can be
time and chemical consuming nitrate salts were used instead. Catalysts were
prepared by a modified impregnation method based on Cu(NO3)2, Pt(NO3)4 and
HAu(NO3)4. Aliquots of these solutions were taken to prepare a series of catalysts
with combinations of 1 wt% and 3 wt% of Cu, Pt and Au metal content in Al2O3.
Sample pellets (diameter = 5.6 mm) were prepared using a hydraulic press and
applying 0.5 metric ton for 3 min. The list of samples produced is shown in Table
7.2. Using the above method 24 tri-metallic combinations were synthesised. They
were split in three identical groups: Columns AB, are duplicates of samples in
column CD and EF. Please note that rows G and H contain samples that have not
been created using the above method.
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Table 7.2. The catalyst composition of the 96 member library. Samples in
columns A-F were synthesised by impregnation.
7.6.3. REDUCTION PROCEDURE AND SAMPLE CONDITIONING
Catalysts were activated by exposing them to a reducing atmosphere in an 8%
CO/He mixture before the CO oxidation experiment. Precursors were reduced at
125 oC for 6 h. CO was removed by purging He (250 ml/min) at 125 oC for 1 h. The
samples were allowed to cool to room temperature in a Helium flow of 10 ml/min.
Subsequently each sample was exposed to the series of CO oxidation conditions at
room temperature. The following conditions were applied: a catalyst bed volume of
0.4 cm3, a space velocity of 4140 h−1 and all gas reactants were diluted 8% in He.
The CO:O2 ratio was varied by changing the individual flow rates keeping a total
flow of 40 ml/min. On changing conditions the samples were monitored in situ by
X-ray absorption spectroscopy. No helium purge was applied between the different
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gas conditions: pure O2, CO:O2=1:1, pure CO. The catalytic conversion, water
content and any leakage in the stream was continuously monitored using the
quadrupole mass spectrometer (MS) described in Chapter 6.
7.6.4. XAS EXPERIMENTAL SETUP
Experiments were carried out at station 12BM of the Argonne Photon Source (APS),
Chicago, USA. The APS storage ring operated with a beam current of 150-220 mA.
We used a Si (111) double crystal monochromator, which was detuned to 50%
intensity in order to minimize the presence of higher harmonics. A horizontal plane
Pd-coated mirror was used to provide harmonic rejection as well as vertical
collimation and focusing. The beam size was 1.0 mm × 1.0 mm. An ion chamber
filled with Ar was used to measure the intensity of the incident beam I0. Cu K, Pt LIII
and Au LIII XANES and EXAFS spectra were collected in fluorescence-yield mode
using a 9-element Ge solid state detector.
The detector was positioned approximately 5 cm from the sample optimised to obtain
the best S/N ratio. Care was taken to avoid saturation of the detector. The EXAFS
region was scanned in the k-scan mode from 2 to 14 Å-1 using k-steps of 0.05 Å-1. In
the pre-edge region, data were recorded on an energy grid of 5 eV/step and with
0.25 eV/step in the XANES region. These parameters resulted in data acquisition
times of 6 min per XANES spectrum and 15 min per EXAFS spectrum. An energy
calibration of the beam was carried out by collecting the transmission spectra of Au
reference foil at the Au LIII edge.
Analysis of EXAFS spectra was performed using the IFEFFIT319 library and the
modules
ATHENA
and
ARTEMIS.319,320
The
pre-edge
background
was
approximated by a linear function. The edge-position was determined by finding the
zero crossing of the first derivative of the spectra. Subsequently, the absorption
spectra were normalized through division by the height of the absorption edge.
Theoretical amplitudes and phase functions used in the fitting procedure were
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calculated with FEFF6.321 All the fits were performed using multiple k-weightings of
1, 2 and 3.
7.6.5. CATALYST PERFORMANCE
The MS analysis program was used to calculate the catalyst conversions. These were
obtained from the mass spectrometry traces simultaneously with the collection of in
situ XAS data.
Cell
Conversion
%
Cell
Conversion
%
Cell
A6
A2
A5
B12
B11
A12
B2
B10
A9
A11
A1
A8
A4
A3
B7
A7
B9
B4
B8
B6
B5
A10
B3
B1
13.3
12.9
12
11.4
10.8
10.7
10.6
10.4
10.4
10.4
10.1
9.8
9.7
9.6
9.5
9.4
9.3
9.3
9
8.9
8.5
8
7.5
7.1
C6
D11
C9
C5
C7
C11
C2
D12
C12
D8
D7
C1
C10
D5
D3
D9
D4
C8
D10
D2
C4
C3
D1
D6
16.1
15.4
15.0
14.7
14.6
14.4
14.3
14.3
14.2
14.1
13.9
13.6
13.4
13.1
12.7
12.3
12.3
12.3
11.7
10.9
10.7
8.6
7.3
0.0
E6
F8
E9
E5
F2
F6
E2
F12
F4
E11
E1
E4
F9
E12
F7
E10
F5
E3
F11
E7
F1
F10
F3
E8
Conversion
Conversion
%
Cell
%
G9
44.8
14.7
H9
40.6
11.1
H8
36.6
10.4
G8
18.6
10.2
G10
14.3
9.0
H10
5.4
8.5
G11
4.7
8.5
G7
4.6
8.1
H12
3.1
7.8
H6
2.9
7.6
G5
2.6
7.4
G4
2.5
7.3
G12
2.4
7.2
G1
2.4
7.2
H7
2.0
7.1
G2
1.9
7.0
H11
1.7
7.0
H1
1.6
7.0
H4
1.6
6.9
H3
1.5
6.8
H5
1.2
6.6
G6
1.1
6.1
G3
1.1
5.8
H2
0.3
5.5
Table 7.3. The average conversions of the catalysts during the screening
experiment (1.5 h). Catalysts with the same colour have identical compositions.
The catalyst conversions of the Cu, Pt and Au based catalysts are summarised in
Table 7.3. The catalysts containing Cu: 1 wt% and Au: 1 wt% (shaded green)
showed consistently the highest activity. In general, CO conversion rates of Cu-Au
(shaded green, yellow and blue) catalysts were 2 times higher than the ones reported
for pure Au catalysts. It is also interesting to note that Au catalysts presented lower
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activity than pure Cu 1 wt% catalyst. Investigation of the conversions revealed that
the presence of Cu in this set of catalysts led predominantly to high activity.
12
11
10
9
8
7
6
5
4
3
2
1
A
B
C
D
E
F
Figure 7.29. A 2D map of the CO conversion of 72 catalysts. One can identify
that catalysts containing Cu (A6 C6 E6, A2 C2, A9 C9 E9, and D8 F8) have
considerably higher activity.
It shall be noted that in order to assess the design quality of the prototype 96 well
plate, 24 catalysts were duplicated over columns AB, CD and EF (Table 7.2). The
duplication also enabled the structural characterisation with XAS on three absorption
edges (Cu, Pt, Au) with an unexposed set of catalysts each time.
The CO conversion efficiency of 72 samples can be seen in Figure 7.29. The MS
analysis program uses a baseline of CO which is defined by the user as the CO ion
current before the reaction.
The advantage of a 2D representation approach will be discussed below. It can be
seen that unlike any other sample D6 shows a 0% CO conversion. This is clearly
below the baseline of the entire array and is highly improbable since the other
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identical catalysts show some activity. Individual MS analysis showed that that cell
was blocked and no reaction took place. It can also be seen that the baseline activity
in columns C D is higher than the adjacent AB and EF. This was caused from the
discrepancies of the initial mechanical design of the prototype array. As it can be
seen the inherent redundancy built into the high throughput system helps reduces the
chances of overestimating catalyst activity.
Table 7.4 summarises the properties and average catalytic performance of some of
the best and worst investigated samples. Turnover frequencies (TOFs) for the Cu-Au
system are referred to the total metal loading using an average molecular weight. The
catalyst containing Cu: 1 wt% and Au: 1 wt% showed the highest activity. These
samples were use later for more detailed manual analysis of their corresponding XAS
structures.
Catalyst
Cu-Au (1-1) (A6)
Cu-Au (1-3) (C9)
Cu-Au (3-1) (F7)
Cu-Au (3-3) (E2)
Au (1) (F3)
Au (3) (F1)
Cu (1) (D11)
Cu (3) (E8)
Copper
loading
(%wt)
1
1
3
3
1
3
Gold
loading
(%wt)
1
3
1
3
1
1
-
Conversion
(%CO)
TOFs
(s-1)
16.1
15.0
7.1
8.5
5.8
6.9
15.4
5.5
1.1 x 10-1
0.6 x 10-1
0.15 x 10-1
0.2 x 10-1
1.2 x 10-1
0.5 x 10-1
1.0 x 10-1
0.1 x 10-1
Table 7.4. Results from high throughput MS analysis revealed the activity of the
above catalysts. These were later selected for additional individual screening.
7.7. ASSESSMENT OF XAS DATA QUALITY
Using the HT system we have automatically recorded XAS data on three absorption
edges, namely Cu, Pt and Au. The high brilliance of the 3rd generation synchrotron
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source allowed very fast acquisition of fluorescence data with exceptional quality
(Figure 7.30 -7.35).
.
B3
A11
Figure 7.30. X-ray absorption spectra from the Cu K edge of 24 samples
(Columns A, B).
It can be clearly seen that the data collected by the system allow the user to
immediately identify sample irregularities. For example, one can see that sample
A11, as expected, has no Cu and hence there was no spectrum (noise not shown,
Figure 7.30). However, this is not the case for B3 which should also have no Cu. It
was found that a small spillage resulted in approximately 0.1 wt% of Cu impurity
(compared with the level of noise of a 1 wt% Cu catalyst).
Preliminary visual inspection also shows that the Cu environment has great variation
between spectra. The amplitudes of near edge resonance between different spectra
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show variations which are typical for varying orbital occupancies (Figure 7.31).
Correlations with Au spectra will be discussed in the XAS analysis section below.
III
Figure 7.31. X-ray absorption spectra from the Pt LIII edge from 24 samples
(Columns C, D).
The high photon flux produced a high S/N ratio, even at low Pt concentrations.
Visual inspection of the Pt absorption spectra shows that the Pt environment has
great variation between spectra. Specifically there is large variation to the near-edge
part which signifies different oxidation states between samples (Figure 7.31).
Typically Pt oxidation varies between Pt+4 and Pt+2. One should consider, however,
that samples may also oxidise during beam exposure. Time resolved XAS testing
over different beam exposures revealed no changes of the near-edge resonance
amplitude (Figure 7.32). A comparative view of Pt and Cu XANES data can be seen
below (Figure 7.32 and 7.33). In contrast to Pt XAS, the Cu data indicate a rich
formation of pre-edge feature which can be resolved with high resolution. Cu spectra
have a considerably higher noise baseline than the Pt spectra.
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III
Figure 7.32. Repeat XANES spectra from the Pt LIII edge of 24 samples taken at
different beam exposure (Columns C, D).
Figure 7.33. The near-edge detail of X-ray absorption spectra from the Cu K
edge of 24 samples (Columns A, B).
An overview of the quality of the XAS Au data can be seen in Figure 7.34 and 7.35.
The first 6 spectra were recorded from reference catalysts synthesised by Dr Norbert
Weiher. The best performing, reference catalysts were based on 4 and 6 wt%
Au/Al2O3 (olive and yellow colour H9, G9, Table 7.2 and 7.3).
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Reference
Catalysts
III
Figure 7.34. The Extended X-ray absorption spectra from the Au LIII edge from
24 samples (Columns E, F) (Reference catalysts H8, G8, H9, G9 etc).
As indicated by the absence of an intense near-edge resonance the most active
reference Au based catalysts are primarily at the zero valent state (Figure 7.34).
III
Figure 7.35. The near-edge detail of the X-ray absorption spectra from the Au
LIII edge of 24 samples (Columns E, F).
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7.8. XAS ANALYSIS OF SELECTED SAMPLES
During this HT study a substantial body of time resolved XANES spectra has been
recorded. Detailed analysis and correlations will be drawn only on a subset of the
samples.
MS analysis helped identify the most promising candidates (Table 7.4). From a
catalytic point of view the best performing combinations proved to be the Cu–Au
based complexes. Cu-Au catalysts containing Cu: 1 wt% and Au: 1 wt% presented
the highest activity. Catalysts containing Pt had a predominantly low conversion and
Au catalysts were less active than Cu. To allow a quantitative comparison, pure Au
and pure Cu catalysts were also included in the analysis (Table 7.4).
7.8.1. XANES ANALYSIS OF THE COPPER COMPONENT
Cu K edge XANES spectra of reduced catalysts and standards322 can be seen Figure
7.36 (taken at room temperature in He flow). CuO has two distinguishable features
which are characteristic of CuII(3d9)323 compounds. There is a small pre-edge
absorption at 8978 eV (position A) corresponding to the 1s  3d dipole forbidden
transition and there is a shoulder at ~ 8986 eV attributed to the 1s  4p (position B)
"shake down" transition.324,325 All catalysts show the pre-edge feature at 8978 eV,
however, only the sample containing Cu: 3 wt% presents a shoulder at 8986 eV. The
strong peak at 8983 eV observed for Cu2O is characteristic of Cu+ species with low
coordination associated to transitions from the 1s to the 4p orbitals
(position C).326,327 However, this feature is not present in any of the catalysts.
XANES spectra of Cu(OH)2 shows the characteristic Cu2+ pre-edge peak at 8978 eV
and the 1s  4pxy (position D) transition at 8998 eV.
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Figure 7.36. Normalised XANES spectra at the Cu K edge of reduced catalysts
taken at room temperature in He flow and standards322 from metallic Cu, Cu2O,
CuO and Cu(OH)2. Dotted lines A, B, C, D, E indicate the energy position of
electronic transitions characteristic of copper oxide species. Solid lines show the
data and dashed lines show the linear combination fitting results (Table 2).
Cu K edge XANES spectra from the catalysts presented here are different from those
of both CuO and Cu2O. The absence of a pre-edge structure is in line with Cu-OH
species328 found in Cu catalysts in zeolites329 and in Cu/Si - Cu/SiAl systems,264 used
in the NOx catalytic reduction. The XANES spectra were analysed by LCA using the
component standards Cu, Cu2O, CuO and Cu(OH)2 using the LC fitting facilities of
the ATHENA software package. The results are presented in Table 7.5. It was found
in all cases that CuO and Cu(OH)2 were the major components contributing to the
spectra. It appears that a decrease in activity is associated to an increase in the
Cu(OH)2 component and a decrease in the CuO spectral contribution as evidenced by
Figure 7.37.
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Sample (Al2O3)
Cu-Au (1-1)
Cu-Au (1-0)
Cu-Au (1-3)
Cu-Au (3-3)
Cu-Au (3-1)
Cu-Au (3-0)
HIGH THROUGHPUT IN SITU XAS
Linear Combination Fit Results
Cu
Cu2O
CuO
0.001
0.056
0.407
( 0.005)
( 0.010)
( 0.029)
0.001
0.074
0.383
( 0.005)
( 0.037)
( 0.040)
0.001
0.042
0.420
( 0.005)
( 0.024)
( 0.033)
0.079
0.050
0.436
( 0.023)
( 0.025)
( 0.024)
0.082
0.113
0.444
( 0.012)
( 0.018)
( 0.027)
0.215
0.044
0.470
( 0.024)
( 0.043)
( 0.047)
Cu(OH)2
0.536
( 0.038)
0.543
( 0.040)
0.537
( 0.043)
0.435
( 0.042)
0.362
( 0.034)
0.271
( 0.068)
Table 7.5. Linear combination (LC) fitting results of the Cu K XANES spectra
of the reduced catalysts, calculated using the ATHENA LC analysis function.
Figure 7.37. Comparison of the catalyst activity with the Cu(OH)2 and CuO
spectral components (from LC analysis).
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Figure 7.38 shows the normalised Cu K XANES spectra of the reduced catalyst
containing Cu: 1wt% and Au: 1wt% sequentially exposed to O2, CO:O2 and CO. The
spectra varied with reaction conditions, in an O2 atmosphere the shoulder at ~8986
eV associated to 1s  4p transition is suppressed. In contrast, the 1s  4pxy
transition at 8998 eV is favoured. The change of reaction conditions to CO:O2 and
CO does not have a clear effect on the 1s  4p transition, however, these changes
are evident in the peak intensity at 8998 eV. An oxidative atmosphere promotes
electronic transitions from 1s to 4pxy (8998 eV). At a reducing atmosphere this
transition is less favoured and the catalyst spectrum returns to its reduced form.
Figure 7.38. Normalised Cu K XANES spectra of the reduced catalyst
containing Cu: 1 wt% and Au: 1 wt% sequentially exposed to He (solid), O2
(long dash), CO:O2 (dot) and CO (short dash). Inset: Difference spectra between
He and O2/ CO:O2 /CO respectively.
Details of this effect are shown in the insets of Figure 7.38. These plots show the
difference spectra between the data taken under reaction conditions and the spectrum
of the reduced catalysts. To gain a quantitative idea of the extent of these spectral
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changes, the difference intensity at the maximum of resonance were fitted to
Gaussian functions from where maximum intensity values were obtained.
Figure 7.39 shows these intensities including the catalysts activity for comparison. It
is observed that an increase in activity is associated to the probability of 1s  4pxy
(8998 eV) transitions during different gas reaction conditions. Catalysts with higher
activities show a clear intensity change at 8998 eV following the O2  CO:O2 
CO reaction sequence. Figure 7.40 shows the spectral changes during reaction
conditions at the Cu K pre-edge region for Cu: 1 wt% catalyst (other catalysts follow
the same trend). From this figure the appearance of a small feature at ~ 8983 eV
typical of Cu+, indicates that intermediate reduced species are present during CO
oxidation.
Figure 7.39. Intensity of difference spectra (O2/ CO:O2 /CO - He) at Cu K edge
within the 1s  4pxy region of all catalysts containing Cu.
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Figure 7.40. Normalised XANES spectra at the Cu K edge in the pre-edge
region. The Cu: 1 wt% catalyst is under reaction conditions. The vertical lines A
B C indicate the energy position of electronic transitions characteristic of
copper oxide species.
7.8.2. XANES ANALYSIS OF THE GOLD COMPONENT
Figure 7.41 shows the normalised Au LIII XANES spectra of the reduced catalysts
taken at room temperature in He flow. The XANES spectra of standard metallic Au
reference
322
is also shown. The first feature in the spectra is a small shoulder at
~11925 eV associated to a dipole 2p3/2  5d (position A) electronic transition. The
probability of this transition is a function of the d-band occupancy which determines
the spectral intensity in this region. After reduction all spectra resemble that of Au
foil indicating that gold is in metallic state. The XANES features are as prominent as
in Au foil, but slightly shifted to higher energies (~1.5 eV) (positions B and D). This
can be attributed to the metal-support interaction and to the difference in particle
size.204,330,331
In order to compare the occupancy of the d-band among the catalysts, integrated
intensities were measured between 11919 eV - 11928 eV and plotted in Figure 7.42.
This figure shows that as the amount of Cu increases the intensity of the feature at
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11925 eV is slightly stronger which suggests that gold impregnation on Cu-Al2O3
promotes 2p  5d transitions.
Figure 7.41. AuL3 edge normalised XANES spectra of reduced catalysts in He
flow at room temperature. Reference spectra for Au is also shown. Dotted lines
A and C indicate the energy position of electronic transitions characteristic of
Au. Dotted lines B and D indicate a shift of the catalyst spectra with respect to
Au foil.
Figure 7.42. Integrated area of AuL3 XANES spectra in the region of maximum
resonance (11919 eV -11945 eV).
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Figure 7.43 shows the normalised Au LIII XANES spectra of the reduced catalyst
containing Cu: 1wt% and Au: 1wt% sequentially exposed to O2, CO:O2 and CO. The
inset shows the difference spectra between the data taken under reaction conditions
and the spectrum of the reduced catalysts. Exposure to O2 leads to a depletion of the
d band and consequently an increase in intensity associated to the 2p  5d
transition.332 This effect is also observed during exposure to CO:O2 mixture and to
CO in agreement with previously reported investigations.333
Figure 7.43. Cu-Au catalysts (Cu: 1 wt%, Au: 1 wt%) normalised XANES
spectra at the AuL3 edge under reaction conditions: reduced in He (solid), O2
(long dash), CO:O2 (dot) and CO (short dash). The inset details the difference
spectra between O2/ CO:O2 /CO and the spectra in He at the 2p  5d region.
Integrated areas of the difference XANES spectra were calculated and plotted in
Figure 7.44. A positive difference is observed in all catalysts which appears to
increase with the amount of Au for a given wt% of Cu. Cu-Au catalyst containing
Cu: 3 wt % and Au: 1wt% shows a small difference between the reduced spectra and
the spectra during exposure to O2, CO:O2 and CO. This improves when Au content is
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increased to 3 wt% but still only minor spectral differences are observed during the
successive change of gas conditions. In contrast, Cu-Au catalysts containing
Cu: 1 wt% show clear spectral differences between O2 and CO conditions suggesting
that depletion of the d band due to CO adsorption 332-334 takes place.
Figure 7.44. Area of difference spectra (O2/ CO:O2 /CO - He) at the AuLIII edge
within the 2p  5d region of all catalysts containing gold.
Pure Au catalysts also show spectral differences between reaction conditions. In
catalysts with only 1 wt% Au, exposure to oxygen leads to an increase in intensity in
the d-band region which is pronounced when exposed to CO:O2. However, exposure
to CO reduces slightly the spectral intensity but inside the noise margin. Catalyst
containing only Au: 3 wt% shows the highest difference between the reduced spectra
and the spectra during reaction conditions similar to the Cu-Au catalysts containing
Cu: 1 wt%.
7.8.3. EXAFS ANALYSIS
First shell fitting results of Cu K and Au LIII EXAFS spectra of the reduced catalysts
are given in Table 7.6. Figures 7.45 (a) and (b) show the Fourier-transformed k2weighted Cu K and Au LIII EXAFS fitted spectra. Reference paths were calculated
using the crystal structures of CuO335 (Cu-O shell, Reff = 1.9481 Å) and Au (Au-Au
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shell, Reff = 2.8842 Å).336 The overall amplitude reduction factors S02 were kept fixed
at a value of 0.83334 for Au and at 0.865 for Cu323. Fits were performed in R-space
using Hanning windows for the Fourier filtering.
Sample
shell
Cu-O
Cu-Au (1-1)/Al2O3
Au-Au
Cu-Au (1-0)/Al2O3
Cu-O
Au-Au
Cu-O
Cu-Au (1-3)/Al2O3
Au-Au
Cu-O
Cu-Au (3-3)/Al2O3
Au-Au
Cu-O
Cu-Au (3-1)/Al2O3
Au-Au
Cu-Au (3-0)/Al2O3
Cu-O
Au-Au
Cu-O
Cu-Au (0-1)/Al2O3
Au-Au
Cu-O
Cu-Au (0-3)/Al2O3
Au-Au
N
R(Å)
2(Å2)
E0 (eV)
Rfactor
2.76
(0.32)
8.60
(0.55)
2.71
(0.41)
2.44
(0.17)
7.29
(0.80)
2.02
(0.23)
8.26
(0.42)
1.80
(0.26)
8.48
(0.90)
1.81
(0.21)
9.42
(0.80)
8.61
(0.85)
1.909
(2)
2.832
(9)
1.925
(2)
1.920
(1)
2.838
(16)
1.909
(2)
2.813
(7)
1.920
(2)
2.788
(15)
1.936
(3)
2.842
(11)
2.837
(15)
0.002
(3)
0.008
(1)
0.004
(3)
-
-8.4
(1.3)
0.0052
8.7 (0.9)
0.0077
0.002(2)
0.008
(2)
0.005
(3)
-6.8
(1.5)
-6.5
(0.7)
0.0083
0.0018
9.0 (1.4)
0.0204
-8.0
(1.2)
0.0044
0.008(1)
7.9 (0.7)
0.0047
0.002
(3)
0.007
(2)
0.006
(4)
0.006
(2)
0.008
(2)
-6.0
(1.2)
0.0053
7.7 (1.5)
0.0209
-4.8
(1.5)
-
0.0220
-
9.0 (1.1)
0.0119
-
-
9.0 (1.4)
0.0190
Table 7.6. Fitted values for coordination number N, nearest neighbor distance R
and Debye-Waller factor 2 obtained from the analysis of EXAFS spectra from
the reduced catalysts taken at the Cu K and Au L3 edges. The spectra were
fitted using the first Cu-O, and Au-Au backscattering paths centred at 1.9481 Å
and 2.8842 Å respectively. A Hanning window determined the k-range and the
boundaries of the k2-weighted Fourier transform (FT). All spectra were fitted
using multiple k-weightings of 1, 2 and 3. In all cases the Debye-Waller factors
were constrained to a lower limit of 0.001 Å2.
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The fitting ranges applied in k- and R-space were at Cu K edge: krange= 3.185 - 7.862
Å-1, Rrange = 0.8 - 2.5 Å and at Au LIII edge: krange= 3.3 - 8.2 Å-1, Rrange = 1.63 - 3.79
Å. In all cases, a stable fit was obtained by varying all the shell parameters at the
same time.
The Cu-O distances reduce with decreasing copper content Table 7.6. The catalyst
containing 3 wt% of Cu presents the highest Cu-O distance of 1.936 Å. This is
followed by the 1 wt% Cu catalyst with a Cu-O distance of 1.925. Higher
concentration of Au atoms decreases this distance further 1.910 (0.01) Å.
Table 7.6 also shows that catalysts containing Cu: 1 wt% present significantly higher
coordination number values than the ones with Cu: 3 wt% content.
Figure 7.45. Fourier transformed k2(k) functions (lines) and the fit (points) of
the EXAFS spectra taken at the Cu K edge (a) and Au L3 edge. The spectra
were fitted using the first Cu-O (a) and Au-Au (b) backscattering paths centred
at 1.9481 Å and 2.8842 Å respectively. A Hanning window determined the krange and the boundaries of the k2-weighted Fourier transform (FT). All
spectra were fitted using multiple k-weightings of 1, 2 and 3.
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EXAFS fits from Au LIII spectra show a contraction of the Au-Au distance and
coordination number compared to Au bulk indicating a small particle size. A
contraction of Au-Au distances with the incorporation of Cu is also observed,
ranging from 2.840 (0.003) Å for pure Au catalysts to 2.800 (0.013) Å for Au
catalysts containing Cu: 3 wt%. Table 7.5 suggests that the Au-Au coordination
numbers are affected by the amount of Au in the catalyst, i.e. catalysts with 1wt% Au
present higher coordination numbers than those with 3 wt% Au, for a given wt% Cu.
Summary
Cu-Au catalysts with Cu: 1 wt % present the same spectral features in reaction
conditions as those found in pure Au or in pure Cu active catalysts (Figures 7.39 and
7.44). Mechanisms on gold catalysis involving OH groups have been proposed by
several groups.205,206,337,338 In the Cu-Au system examined here, the presence of CuOH groups in the vicinity of gold particles may favour the activation of oxygen257
and/or adsorption of CO339 on the Au surface leading to an increase in catalytic
activity.
In general, activities of Cu-Au catalysts are favoured by the presence of Cu-OH sites
as demonstrated by Figure 7.37. The Cu-O distances remain constant at a value of
1.91 ( 0.01) Å indicating an intermediate CuxO or Cu(OH)x phase.
The average Au-Au coordination number among these catalysts is 8.16 ( 0.87) Å
and the small differences between them reflect a similar particle size distribution
which may influence only slightly their catalytic activity.
The results suggest that copper dominates the CO oxidation reaction mechanism in
bimetallic Cu-Au catalysts.
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8. ADDITIONAL WORK
This work has been conducted by the author and a Master student, Helen Tatton.
Helen has expanded my initial 2D correlation analysis program and included more
functions which she later submitted as part of her Master degree dissertation.
8.1. 2D CORRELATION ANALYSIS USING LABVIEW
The developed program is applicable for analysis of XAS data in individual text
files, each containing two columns, the energy in eV and the normalised absorption
in arbitrary units, x and y, respectively. The XAS tab of the Front Panel, shown in
Figure 8.1, is used to select the number of spectra for correlation; then the “Open
files” button is pressed, resulting in a file dialog box opening for selection of the files
for correlation, one file at a time. The operation of the program can be stopped by
pressing the “STOP” button (Program, Virtual Instrument or VI).
Figure 8.1. The Front Panel of the 2D correlation analysis program. The
program allows 2D correlation analysis of XAS spectra.
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The structure of this program is based around a while loop, with the conditional
terminal set to “stop if true” and linked to the STOP button control on the Front
Panel. Outside this “while loop”, there is a “for loop”, which has been set to execute
once to reinitialise each of the graphs, so that there is no confusion between the
results from the previous and the present correlation, and sets the default view to the
XAS plot tab from which the initial selections are made.
Figure 8.2. Graph initialisation using the “Reinit To Default” property nodes.
Figure 8.2 also shows the tab control of the Front Panel tabs and a wait function to
ensure that processes outside Labview can use the CPU.
The remaining structure comprises a series of “case structures” within the “while
loop”. For each of these, exactly one of the cases executes when the structure
executes. The main case structure is controlled by the “Open files” button, which
switches the case from false to true when pressed. No actions are associated with the
false case. The code within the true case is contained within a flat sequence structure.
Code elements in Labview are executed sequentially along the wires. It is good
practice to position the functions in the program so that the wires and therefore the
data and order of operation flow from left to right. The frames within the flat
sequence structure also operate from left to right, with data only passing out of the
frame when it has finished executing. This ensures that all the data is passed into the
next frame at the same time and therefore the functions perform as expected.
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Figure 8.3. The first frame of the main flat sequence structure in the program.
As shown in Figure 8.3, above, the first frame of the flat sequence structure contains
the “No of spectra” control, a series of for loops and the terminal for the XY graph
shown in Figure 8.1. The first for loop controls file opening. For each file, the
number of which are specified by the control, a file selection box is opened,
containing the dialog “Select a file to read”, then the file path is used to specify the
file to open, the size of the file in bytes (equivalent to the number of characters in the
file) is determined and this number of bytes are read from the file in a “string” format
(coloured pink) before it is closed. The automatic indexing upon leaving this loop
creates a 1D array of string from the individual string from each file. The next for
loop then cycles through the rows of the array, converting the spreadsheet string to a
2D array (rows and columns) of the elements contained therein and separating out
the columns with index 0 and 1, the x and y columns, respectively. The x column is
valuable for setting data ranges and labelling graph axes in a meaningful manner, but
only the y data, i.e. the normalised X-ray absorption, is correlated in the generalized
2D correlation method. Both data sets essentially become a function of the array
indices, and as such remain related to each other throughout the calculations. The
columns are each converted from a 1D array of string data type to a 1D array of
double data type (coloured orange) so that they may be used with numeric functions
(Figure 8.3).
XAS data is not always evenly spaced. In order to represent each energy region
equally in the 2D correlation spectra, it is necessary to convert the measured data to
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ADDITIONAL WORK
equally spaced data via interpolation. The simplest form of interpolation is linear
interpolation. This approximates an unknown value of y at a specified value of x by
creating a straight line function between the known value either side of the unknown,
using this to calculate the new value. The linear interpolation function available in
Labview requires input of arrays of the original x (independent) and y (dependent)
data and also an array of the x values for which values of y are needed, in this case
an array of equally spaced points.
Following conversion of the data to a 1D array of double, the x data range was found
using the “Array Max & Min” function and a further for loop inserted to create a new
array of x with 0.25 eV spacing, starting at the minimum value of the original array
of x, and finishing not more than 0.25 eV below the maximum value. An evenly
spaced array of y was then created via linear interpolation and the size of this array
measured using the Array Size function. Finally, the original x and y arrays were
bundled together, producing an array of (x, y) data which was auto indexed to plot
the spectra from all files loaded onto the same graph.
For this application, the average spectrum was used as the reference spectrum for the
correlation. The remaining two “for loops” in the first frame of the flat sequence
structure calculate this reference spectrum and subtract it from the interpolated
spectra by calculating the mean of each y value over all N spectra, using the size of
the new array of y to control the number of loops, then using auto indexing to create
the reference spectrum and the second loop to subtract it from each of the spectra in
turn.
Figure 8.4 shows the second and third frames of the flat sequence structure. These
control the production of the synchronous and asynchronous spectra displayed in the
2D Spectra tab of the Front Panel, as shown in Figure 8.5, and reset the value of the
case structure to false after all calculations have been completed. The three data
inputs are top, integer number of spectra loaded (blue), middle, 2D array of
interpolated values of y and bottom, 2D array of evenly spaced values of x.
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Figure 8.4. Second and last frames of the main flat sequence structure in the
program.
Figure 8.5. Setting up the 2D correlation spectra on the Labview® “Front
Panel”.
Calculation of the synchronous spectrum from a discrete matrix of dependent data is
explained in detail in Appendix 2. The inner product of the dynamic spectra is
produced by multiplying the transposed matrix of the first dynamic spectrum with
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ADDITIONAL WORK
the original matrix. The matrix product is then multiplied by (1/N-1), where N is the
number of spectra, producing a matrix of values (intensities). The data generated
from 2D correlation of the discrete data points measured in XAS is a matrix of
intensities, which is automatically converted to a 2D array for graphical
representation, where the indices of the array in both dimensions relate to the same
energy scale in eV.
The three dimensions of the data (two indices and one set of data) may be
represented in several ways. These include a 3D surface graph, a contour plot and an
intensity graph. The latter was chosen because none of the data is occluded by higher
data regions as might occur in the 3D surface and it provides a good representation of
both positive and negative values, which may be difficult to distinguish in a contour
plot.
An example of an intensity graph is shown in Figure 8.6. The red regions are positive
peaks whilst the blue regions are negative peaks, with lower values represented by
less intense shades. The white regions represent zero amplitude.
Figure 8.6. Example of a Labview intensity graph. Note both axes are in energy
units.
The assignment of colours to data values is governed by the colour scale shown to
the right of the graph. The maximum and minimum value of the scale may be varied,
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as for any scale. A smaller scale range would lead to lower values taking on more
intense colours, whilst a wider scale range would lead to less intense colours. This
scale was generated using the “color table generator” from the Labview example
Create IntGraph Color Table.vi.
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9. FINAL CONSIDERATIONS AND FUTURE WORK
9.1. INTRODUCTION
This chapter will focus on the impact each synthesis, screening, analysis and
optimisation steps had on the efficiency and quality of the overall high throughput
workflow. Possible improvements will elaborated further.
9.2. OPTIMISING CATALYST SYNTHESIS
A modified, wet impregnation method was used to synthesize the 96 catalyst
precursors (ex situ results, Chapter 5). The precursors were synthesised directly in
the wells of the SBS standard 96 well plate using small amounts of catalysts (200
mg). Similar to synthesis robots, metal chloride solutions were first manually
delivered to each well. Alumina powder was subsequently deposited in each well and
was allowed to be absorbed by the pores of the solid support. The impregnated
powder was not mixed during impregnation and it was assumed that due to the small
volume of each of the wells the liquid phase would be homogeneously distributed to
the pore volume of the support.
The deviation of the synthetic procedure from standard practice was unavoidable due
to the large number of diverse samples that had to be created. Multiple liquid pipettes
for concurrent dispensing of 8 or more liquid volumes and the use of a powder
pipette, provided a consistent and repeatable catalyst synthesis environment.
Despite, the complexity of the multiple concurrent synthesis, EXAFS screening of
the catalyst precursors showed that the modified impregnation procedure was reliable
and repeatable. No precipitation or inhomogeneities were detected by visual
inspection or EXAFS.
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XPS was also employed for the fast characterisation of the catalysts (49 samples/day)
but did not yield quantifiable results, possibly due to the high surface sensitivity. The
metal precursor is adsorbed on the μm sized pore surfaces of alumina. As a result the
XPS spectra demonstrated peak contributions which were dominated by the support
(Al2O3).
Image based techniques can also be used for measuring the colour components for
each of the catalyst precursors. For example, if liquid catalytic precursors do not
react with each other they have a characteristic composite colour, PtCl4 is red, AuCl3
is yellow etc. An optical camera with calibrated colour intensities can allow initial
catalyst screening (catalyst synthesis, Chapter 5).
9.2.1. PRECURSOR DRYING
The drying step of the impregnated Al2O3 is essential since it enables evaporation of
the metal acid solution and the adhesion of the metal particles. Due to the individual
well dimensions in 96 SBS plate, the precursor drying step requires 24 to 48 hours,
which is a limiting factor in the synthetic capability of our infrastructure.
An automatically controlled atmosphere oven can accelerate the precursor drying.
Temperatures of 120 oC and a constant flow of dry air have already been used in
literature.340 It is envisaged that delays due to drying can be reduced to 3-4 hours.
9.2.2. PRECURSOR WASHING
Precursor washing is an important step since it enables the removal of surface
poisoning species from the catalytic formulation. This is essential, as in the case of
Au, Cl- species bind to the active sites of Au and Pt based catalysts and hinder the
oxidation of CO molecules.250 However, consecutive washing steps potentially
remove a fraction of the adsorbed metal particles and decrease the total metal weight
content of the catalyst. The effect can be quantified with statistical analysis by
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screening a series of formulations with different washing steps and measuring the
absorption step height of XAS.
Correlation of the edge step height with actual concentration depends on numerous
factors such as exact photon flux, sample state, incident beam angle, self absorption
etc. However, when all the parameters are identical and the only variable is the metal
concentration of the main absorber, it is possible to have a qualitative estimate on the
total metal loading of the catalyst and the signal quality of the acquired spectra as
shown in Chapter 6.
9.3. AUTOMATED CATALYST SCREENING
9.3.1. FASTER XAS CHARACTERISATION
The most serious drawback of XAS is that high quality data require photon fluxes
such as that produced by a synchrotron source. Gaining access in a synchrotron is
very costly, even for a limited time (usually days), for industrial applicants and
comprises a selection process for academic institutions. Consequently, it is very
important to automatically screen the highest possible number of samples while
obtaining good quality spectra in the allocated time frame. A detailed experimental
workflow must be sought for before any experiment occurs. Especially in the case of
high throughput XAS experiments, the experimental workflow cannot be
implemented without a high degree of robotic automation.
Automated experimental control has advantages even with less tedious experiments,
that require long acquisition times and less sample changes. Automatic computer
control of all the experimental parameters enables the scientist to concentrate on
other operations such as data analysis and experimental planning.
With HT XAS the efficient use of the synchrotron facilities is ensured. However, it is
important to establish a balance between acceptable data quality and acquisition
times for the entire catalyst library. This depends on several factors such as absorber
concentration, physical state, excitation energy, beam photon flux, sample
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orientation, sample-detector distance and medium, detector sensitivity and
acquisition time. Nevertheless, the higher fluxes produced by 3rd generation
synchrotrons will ensure even better signal to noise ratio, faster acquisition times and
more samples tested in each experiment.
It shall be noted that the developed system can be installed in different synchrotrons.
A number of catalysts (up to 96) and samples were tested by HT XAS in facilities
such as Daresbury STFC, Diamond Light Source and the Advanced Photon Source
with small software changes.
9.3.2. AUTOMATIC ALIGNMENT WITH X-RAY BEAM
X-ray beam alignment is usually performed manually by optical observation of the
location of the X-ray beam using photographic paper superimposed to a laser beam.
The entire procedure of alignment usually requires 10 min per monochromator
setting and approximately 30 min or more when small beam spots are required. In
any case, manual laser/photographic paper alignment is no longer accurate for
resolutions below 1 mm. In cases of monolayers the array has to be manually moved
by trial and error until the solid state detector and/or ionisation chambers produce the
correct reading.
For the purpose of facilitating X-ray beam alignment, a 85 μm2 modified visible light
photodiode array, has been tested. Initial investigations revealed that it is possible to
use this inexpensive method for detecting the X-ray beam, without the need of an
X-ray camera, to within a few micrometers. However, further testing is needed to
validate the long term effects of energetic photons on the silicon matrix of this type
of photodiode. Testing of an inexpensive high resolution optical camera would also
be advantageous so that its use in an online auto calibration positioning system can
be assessed.
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FINAL CONSIDERATIONS
9.3.3. AUTOMATIC EXAFS ANALYSIS
X-ray Absorption spectroscopy has a number of key advantages (Chapter 1);
however, it requires very tedious analysis and interpretation. To a large extent, the
overall throughput of the developed HT XAS system is depended upon the time
required for analysing the XAS spectra. This is correlated to the user experience,
quality and the accuracy of the theoretical model that will be fitted.
Reports in the peer-reviewed literature include automatic fitting procedures268 and
regularisation of EXAFS analysis312,341 as well as recognition of subtle features in
XANES spectra.157 However, despite the expanding popularity of EXAFS by many
scientific communities there have been no recent reports of automated EXAFS
analysis.157
It shall be noted that the procedural high throughout XAS analysis script reported in
Chapter 5, assumes that all the samples require the same initial parameters for fitting
and there is no automatic way of determining noisy or non-noisy data. In addition,
values are not checked for consistency. If an unreasonable coordination number is
produced (with Debye Waller factor > 0) the program assumes the fit is correct and
will not alert the user.
However, despite the limitations, the automated EXAFS scripts helped analyse 100
spectra in approximately 10 min. This is a dramatic improvement of 2 orders of
magnitude over an experienced user. Colour map representation of fitting parameters
facilitates the elucidation of trends and correlations in the data and supports the
liability of the HT analysis procedure. In addition, the colour maps demonstrate that
HT measurements coupled to a simple automated analysis procedure give physically
meaningful results.
Please note that the above HT XAS analysis scripts were used offline; after the
allocated time at the synchrotron. Spectrum acquisition and concurrent XAS analysis
could be another useful extension of the system.
214
CHAPTER 9
FINAL CONSIDERATIONS
Initial attempts using the beamline communication protocol showed that the
positioning system control system can also be used to acquire and collect file
information and actual XAS data online (tested at station 9.3, Daresbury STFC).
It is envisaged that further research on HT XAS acquisition and analysis will enable
in the future, direct, user independent analysis of XAS data. HT XAS could provide
an immediate structural feedback during crystallisation processes, homogeneous,
heterogeneous catalysis and other transient molecular phenomena.
The XANES part of a XA spectrum could also play an important role for providing
intelligent feedback to the process control systems. Structural changes can be used as
a guide to more preferential conditions.
XANES feature recognition algorithms utilising neural networks and 2D correlation
analysis can be explored further for recognising the onset of electronic events such as
metal reduction or subtle adsorption events on the surface of a catalyst.
For many scientific investigations it is merely sufficient to obtain an optimised
catalyst or material. It is imperative to quantify the underlying structural changes to
allow the construction of a solid theoretical framework for future in silico material
selection. A promising tool can be neural network based feature recognition in
combination with a genetic optimisation algorithm. This may help automate the
correlation of feature changes to structural changes during the optimisation process
(more information on GA, NN and 2D correlation can be found in Appendix 1).
9.3.4. EXAFS DATA ARCHIVING
An important issue with the collection of a multitude of in situ reaction data lies with
the archiving and tagging and sharing of the data locally (group) and globally (across
different scientific communities). Our implementation of an automated meta–data
tagging system tried to address some of the above drawbacks.
215
CHAPTER 9
FINAL CONSIDERATIONS
As indicated in the introduction, chemical informatics efforts should concentrate on
format unification and tagging of spectroscopic data. A set of worldwide accessible
programs (GRID based) could allow users to upload, convert and archive important
scientific data. This could be accompanied by web tools or conversion widgets which
perform conversion of all data to a universal scientific format.
Large repositories of data kept in synchrotrons and in other large scientific research
centres could also be ported to the new worldwide accessible platform.
This could provide a way to allow present and future generations of scientists to
share, compare and analyse otherwise inaccessible data.
216
CHAPTER 10
CONCLUSION
10. CONCLUSION
A versatile 96-reactor prototype platform has been developed that allows highthroughput characterisation by in situ XAS under control of environmental
conditions. The hardware infrastructure consists of a XYZ positioning system, a gas
control system, a multiplexed MS gas analysis system, a prototype 96 reactor flow
cell and multi-channel temperature control. The software infrastructure consists of a
positioning and XAS synchronisation control module, gas/temperature control
software, an HT XAS analysis script and a high throughput MS analysis module.
Using the developed automated methodologies more than 500 different samples have
been screened, yielding.ca 2000 XA spectra. Besides the materials described in this
thesis investigations have also included:




Other multi-metallic catalysts containing Au, Cu, Pt and Ag, on a number of
different support materials..
Thin films of Pt–U multi-layers on steel supports, as well as Au films.
Complex BiMoVOx oxide materials.
Historic Prussian Blue pigments used in 18th-19th century watercolours..
The results obtained with this apparatus demonstrate that automated high-throughput
acquisition of in situ XAS data is feasible; for sufficiently concentrated materials is
can be carried out even at a 2nd generation synchrotron radiation source. Despite
current limitations, the automated EXAFS scripts facilitate a single-shell analysis of
100 spectra in approximately 10 min; in terms of speed this represents a dramatic
improvement by approximately 1-2 orders of magnitude as compared to analysis of
individual spectra by a trained user. This achievement signals that further
development of automated data analysis scripts can realistically be expected to lead
to a more user-friendly service environment at synchrotron radiation sources, and
hence to a widening of the XAS community. Overall coordination, inspection and
analysis of the statistically important changes should ultimately become the only
functions that the researcher performs. Taking a long-term view, the structural results
obtained by automated XAS analyses should ultimately facilitate ‘closed-loop’
automated intelligent target searches.
217
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231
APPENDIX 1
OPTIMISATION TECHNIQUES
APPENDIXES
1. OPTIMISATION IN CATALYSIS
1.1. MONTE CARLO SIMULATIONS
Monte Carlo Simulation (MCS)106,107 is a modelling approach commonly used in
cases where a catalytic system can be simulated by rules and probabilities that model
the various reaction steps like adsorption, dissociation, surface diffusion, reactions or
desorption of reaction products. It results in a minimisation of dynamical rules that
can incorporate the knowledge of the individual reaction steps and substrate
properties obtained from different studies. The dynamic behaviour of the system can
then be expressed in terms of a master equation for the rate of change of the
probabilities of observing each of the reaction events. Simulated annealing is an
example of a Monte Carlo derived modelling method in which one of the synthetic
variables is randomly and/or deterministically altered. If the resulting catalytic
system performs better then the formulation is approved. Alternatively it is accepted
with a probabilistic weighing factor.
Monte Carlo simulations have been extensively used in research for simulating the
activity of a catalytic surface. McLeod 342 produced an implementation of a model for
catalytic reactions that follow the Langmuir-Hinshelwood bimolecular kinetics
(Figure A1.1).
Α  Σ1  ΑΣ1
CO(g)  Σ 1  CO(ads)Σ 1
Β  Σ 2  ΒΣ 2
O 2 (g)  Σ 2  O(ads)Σ 2
ΑΣ1  ΒΣ 2  Σ1  Σ 2  ΑΒ 
COΣ 1  OΣ 2  Σ 1  Σ 2  CO 2 
Figure A1.1. Langmuir-Hinshelwood kinetics and the extension of Ziff-GulariBarshad for describing CO oxidation on a catalytic surface. Σ1, Σ2 are
neighbouring sites.
232
APPENDIX 1
OPTIMISATION TECHNIQUES
It is considered to consist of three irreversible steps: (i) two adsorption events, (ii)
one reaction step immediately followed by (iii) desorption of the product. The
reaction was simulated by performing adsorption attempts on random sites on an
orthogonal grid which had a predefined distribution of the different active sites.
The distribution of the active sites was optimised by the use of a genetic algorithm
(GA) which is discussed later in this chapter. The activity of a surface was defined as
the ratio of the number of AB molecules produced to the adsorption attempts of
molecules A and B. The production of AB molecule was successful only when A and
B were located in adjacent locations. Although the above approximation might be
considered simplistic the resulting surface has the appearance of a checkerboard (1:1
site ratio) when the ratio of A/B= 1:1 and a 2:1 site ratio when the ratio of molecules
is A/B 2:1 (Figure A1.2).
Figure A1.2. Left: Optimisation of catalytic active site distribution, Middle:
Optimal distribution for A/B 1:1 and Right: Optimal distribution when A/B 2:1.
Adapted from 342.
Other researchers63,343 explored the dependence of particle size, geometry,
distribution of the active sites, surface defects and the activity/selectivity of a
polyatomic catalytic reaction. MC simulations with different metal particle sizes and
various reactants indicated that relative size of metal particles to reactant molecules
size has great correlation with the yield and selectivity of a reaction and was in
agreement with the standard analytical kinetic models.343
233
APPENDIX 1
OPTIMISATION TECHNIQUES
1.2. NEURAL NETWORKS
Recently, artificial neural networks (ANN) have been used successfully in catalyst
modelling.108-110 Kito applied a neural network for estimating catalyst deactivation.
109
His team developed dealuminated mordents (a type of zeolite, ABO3) containing
different Si/Al ratios and used these for methanol conversion into hydrocarbons. The
study showed agreement of the predicted first order deactivation rate constant (k)
with the experimental data. Other examples are focused on preferential oxidation
(PROX) and the reaction of carbon monoxide (CO) with stoichiometric amounts of
oxygen in excess hydrogen atmosphere. With the help of ANN it was discovered that
the optimal concentration and pre-treatment temperature of Co/SrCO3 is 3 mol% and
340 0C respectively.110,344 There are various other applications of ANN but the most
interesting results are produced by combining this method with GA and statistical
analysis.
A neural network consists of a number of similar processing units named neurons in
close analogy to the function of the biological neurons. This network of neurons can
be interconnected in different ways and the type of architecture is called topology.
The topology can adapt according to the target application the NN is meant to be
implemented for. A neuron is compiled from the following elements:
1. A number of input connections xj(t) each of which is described by a synaptic
weight wij. The synaptic weight scales the output response of the previous
layer neuron j before it arrives to the actual neuron i.
2. The propagation rule which scales the input of all the individual inputs to the
neuron.
3. The activation function Fk which describes the output yk of neuron k with
respect to its excitation.
The above rules can be contained in the response of each neuron and can be
otherwise expressed by the following equation.
y k  Pk Fk  1 w ijx j (t)   w k bias
i
Equ A1.1
234
APPENDIX 1
OPTIMISATION TECHNIQUES
The output response yk of a neuron depending on its synaptic weight wij, its input
vector xj (t), the propagation coefficient Pk and the activation function Fk.
An important feature of NN is the ability to map an unknown parameter space and
then generalise their answer providing statistically acceptable results when the input
patterns are not known. The NN has to undergo a training process which aims at the
controlled adjustment of all the synaptic weights according to the error of the
response output to the expected output for a given input value. In other words
weights are changed by an amount proportional to the error at that unit (neuron)
multiplied with the output of the unit feeding into the weight.345 This learning
process is an inherent part of developing a supervised neural network where the
information accumulated in the neurons by continuous trial and error attempts to
describe the unknown input. The standard multilayered network (Figure A1.3) is the
standard topology used in supervised learning because of its simplicity and fast
learning curve.
Input Layer Hidden Layer Output Layer
Figure A1.3. An example of a standard multilayer perceptron, a type of neural
network structure used in supervised learning.
In catalyst modelling a neural network can be trained to reproduce the response of a
catalytic system from a minimal amount of input training data. This has the
advantage that the general behaviour of the system can be modelled, provided there
is adequate training. The response of the simulated system can then be used to
optimise the operational characteristics of other systems.
235
APPENDIX 1
OPTIMISATION TECHNIQUES
As it will be discussed further in the genetic algorithm (GA) section, GA
optimisation is prone to incorrect setting of the genetic operators. Therefore, it is
highly desirable to use a subset of the catalyst formulations and subsequently
perform catalyst screening and train the NN. If the NN response is then used to
adjust all the characteristics of the GA to create the optimal configuration, then
testing real catalyst generations will produce better convergence. A NN can be used
as a benchmark for assuring the best configuration of an optimisation algorithm.130
For example Corma and co-workers346 utilised a neural network to model the
response of catalyst comprised from 13 elements in different compositions in the
oxidative dehydrogenation of ethane. The neural network was trained by initially
carrying out catalytic testing in a 64 reactor assembly. The experimental data were
reduced for the training process by using the conversion of ethane and oxygen, the
yield of ethylene, and the selectivity of ethylene, CO2 and CO. In each training step
the neural network adjusts its weights according to the empirically recorded catalytic
information in order to reduce the prediction error. In the input layer the NN consists
of 13 input neurons, each one representing the percentage concentration of each of
the 13 elements. The output layer consists of 6 neurons which correspond to the
above mentioned data (X (Ethane), X (O2), S (Ethylene), S (CO2), S (CO), Y
(Ethylene). Seven generations of catalysts were screened for catalytic activity and
were subsequently used for training. Subsequently the NN was tested using different
element concentrations as the input and observing the six predicted catalytic reaction
values.
The prediction accuracy of a NN becomes problematic when singularities exist in the
parameter space of the reaction, which is indeed the case in catalysis reaction
modelling. The results showed that the predictions of the NN although poor in some
occasions, are adequate for pre-screening of combinatorial libraries, since general
trends can be readily extracted with small experimental effort and interesting areas of
the parameter space can be identified and screened in depth.
This chapter explored the various methods for designing and optimising
automatically a library of material with desired performance criteria. It was seen that
236
APPENDIX 1
OPTIMISATION TECHNIQUES
MCS and NN are useful tools for modelling a catalytic reaction and intelligently
targeting catalyst screening into interesting areas of the vast parameter space.
Combining the above methods with genetic algorithms enables faster and more
efficient discovery of novel materials, through optimisation of the GA engine
parameters. Extracting knowledge, while optimising the catalytic libraries is the
focus of research nowadays and will be discussed below.
1.3. GENETIC ALGORITHMS IN CATALYSIS
A stochastic method that is widely used in catalyst discovery and optimisation and
has been inspired by nature is the genetic algorithm (GA). Initially named
evolutionary algorithm (EA) by Holland,111 the Darwinian rules of natural selection
and survival of the fittest were employed as the theoretical basis of this method. The
use of genetic operators (GO’s) in living species like mating, crossover and mutation
are responsible for the selection of the individuals with the most promising
performance. The genetic material of these individuals will contain genetic
information that if kept there is a high probability that the overall performance of the
next generation will increase. From each generation individuals are chosen according
to the performance criteria (function) and GO’s are applied so that a second
generation is produced with higher average fitness from the last (Figure A1.4). This
iterative performance screening and application of GO’s for the creation of new
generations continues until the average fitness of each consecutive generation
converges to a maximum value. The advantage of GA’s is that they enable the search
of a vast parameter space with small computational effort and reach “very good”
solution(s) but not the global optimal. The evolutionary strategy is bound to explore
a large number of individual combinations by utilising the GO’s.
237
APPENDIX 1
Initial
Population
OPTIMISATION TECHNIQUES
Catalyst
Evaluation
Scale Fitness Function
Crossover
Mutation
GA
Figure A1.4. Flow diagram representation of the Genetic Algorithm
optimisation method.
Optimisation results from the selection of only best performing candidates whose
genetic information perpetuates through the generations.
The catalysis analogy to nature’s biological individuals are catalyst formulations,
which are optimised (evolve) according to their yield and selectivity. Employing
genetic optimisation requires the initial design of a library of catalysts (1st
generation) which are chosen heuristically according to performance or other criteria.
This a priori information that we inject to the genetic system actually determines the
parameter space of the search. By applying the genetic operators a second library
will be produced with higher average fitness than the last. The performance criteria
will be satisfied better in consecutive generations and they will converge to a few or
a single optimal catalyst.
The algorithm steps of a GA are summarised below.
1.
[Start] Generate random population of n chromosomes (catalysts) (suitable
solutions for the problem)
2. [Fitness] Evaluate the fitness (yield) f(x) of each chromosome (catalyst) x in
the population
3. [New population] Create a new population by repeating following steps until
the new population is complete
1. [Selection] Select two parent chromosomes (catalysts) from a
population according to their fitness (yield) (the better fitness (yield),
the bigger chance to be selected)
2. [Crossover] With a crossover probability cross over the parents to
form new offspring (children). If no crossover was performed,
offspring is the exact copy of parents.
3. [Mutation] With a mutation probability mutate new offspring at each
locus (position in chromosome).
4. [Accepting] Place new offspring in the new population
238
APPENDIX 1
OPTIMISATION TECHNIQUES
4. [Replace] Use new generated population (of catalysts) for a further run of the
algorithm
5. [Test] If the end condition is satisfied, stop, and return the best solution in
current population
[Loop] Go to step 2
A number of examples in literature utilised the optimisation features of GA for
finding optimal catalytic compositions. Omata142 and Watanabe347 used HT
screening to gain activity and selectivity information on methanol synthesis by using
CuO catalysts. The reaction information was then used to train a NN which
subsequently provided the response surface for the GA to optimise upon. The
theoretical
optimum
catalyst
was
predicted
to
have
a
yield
of
495 g-MeOH / (kg-cat × h) by a combination of 43 Cu / 17 Zn /23 Al/11 Sc/0 B/6 Zr,
calcinated at 605K. Subsequently, the catalyst was synthesised and testing showed a
yield of 427 g-MeOH / (kg-cat × h).
A detailed application of a GA in the optimisation of a selective oxidation (SELOX)
of CO in H2 was shown by Clerc et al.347 High throughput screening was employed
for the characterisation of 189 catalyst formulations containing Au, Pt, Cu, Mo, Nb,
V on different supports of CeO2, ZrO2 and TiO2. Initially, the sol-gel deposited
catalysts were screened for X (CO) conversion rate and S (CO2) selectivity towards
CO oxidation, at three temperatures. A response surface was created from the data
containing 81 polynomial functions that was used as a bench mark for optimising the
characteristics of the GA algorithm. Thereafter, differently set up GA’s were run to
investigate the robustness to noise, reliability. It was found that the GA optimal
parameters were a three point crossover, the use of elitism, tournament selection type
and a population size larger than 48 catalysts.
If a wrongly configured GA is implemented then the time consuming optimisation
process, where possibly thousands of components and several generations have been
synthesised and screened over months, might lead to non-conclusive results. The
efficiency of GA algorithm typically relates to the complexity and size of the surface
response. A surface response which is constructed by screening and optimising
catalysts, usually in multiples of SBS (Society of Biomolecular Science) standards
239
APPENDIX 1
OPTIMISATION TECHNIQUES
(48, 96 etc), is very scarcely sampled. For this reason optimising the configuration of
the genetic algorithm in the design stage is a crucial parameter for a successful
convergence of the GA and has been researched intensively.112,113,347,348
Despite the valid examples in literature of discovering new improved catalysts by the
use of GA’s for the oxidative dehydrogenation (ODH) of ethane and propane112,113
the scientific community is still apprehensive of the use of “black box” type
techniques.
This response is due to the inability of a GA to provide quantitative information on
the decision making process or in other words does not provide a transparent
mathematical model for extracting rules that describe the selection process of an
improved catalyst.
240
APPENDIX 2
2D CORRELATION ANALYSIS
2. GENERALISED 2D CORRELATION ANALYSIS
2.1. INTRODUCTION
The generalized 2D correlation analysis (2D correlation) is a mathematical technique
that can be used to expand and correlate information which is contained in closely
related data-series.349,350 The technique requires a series of dynamic spectra, which
are generated by systematically applying an external perturbation to the system under
investigation. The nature of the external perturbation must be such that it causes
selective effects on the system. Dynamic spectra can be measured as a function of the
quantitative measure of the perturbation like changes in temperature, pressure,
concentration and electrical field (Figure A2.1).
Perturbation
System probe (e.g.
IR, UV, XAS)
Mechanical, electrical,
chemical, magnetic,
optical, thermal, etc.
System
Dynamic
spectra
Figure A2.1. A black box representation of a perturbed system (Adapted 349,350).
There are diverse applications of 2D correlation due to the broad formal definitions
and ability to use the calculations for a wide range of spectroscopic techniques and
using physical parameters which can be systematically applied to produce a
perturbation in the system.
2.2. CONTINUOUS DATA FORMALISM
The mathematical derivation of the 2D correlation analysis has been formalised by
I.Noda.349 A perturbation induced variation in the intensity, y, of a single feature,
241
APPENDIX 2
2D CORRELATION ANALYSIS
measured at v, over a fixed interval of an external variable, t, for example time,
temperature or concentration, between Tmin and Tmax is given by y (v, t).
The nature of the variable v depends upon the type of system probe being used, for
example in IR this would be the wavenumber whilst in XAS it is the energy in eV.
The formal definition of the dynamic spectrum ỹ(v, t) is given in Equation A2.1
 y , t   y   for Tmin  t  Tmax
~
y  , t   
0
otherwise

Equ A2.1
The average spectrum of the system is often used as the reference spectrum y   , as
shown in Equation A2.2, below. A measure of the system well before the
perturbation is applied or well after it is removed are alternative reference spectra.
y   
Tmax
1
T min

Tmax
T min
y  , t dt
Equ A2.2
2D correlation analysis is the quantitative comparison of each pair of different
spectral variables, v1 and v2 at every value of t between Tmin and Tmax. This is given
mathematically in Equation A2.3.
X  1 , 2   ~
y  1 , t   ~
y  2 , t 
Equ A2.3
X  1 , 2  is the intensity of the 2D correlation spectrum which is dependent on the
similarity or dissimilarity of the changes of v1 and v2 with respect to t.
is a cross-
correlation function which compares the changes in these two variables.
Simplification of the mathematical manipulation is achieved by treating the intensity
term as a complex number, given in Equation A2.4.
X  1 , 2    1 , 2   i 1 , 2 
Equ A2.4
The real and imaginary components of this function are orthogonal. They are
respectively defined as the synchronous and asynchronous 2D correlation intensities,
242
APPENDIX 2
2D CORRELATION ANALYSIS
with the synchronous spectrum containing information about similar changes in v1
and v2 as t is varied and the asynchronous spectrum containing information about
dissimilar changes over t.
A formal definition of these intensities is given in Equation A2.5, where Ỹ1(ω) and
Ỹ2*(ω) are the forward and conjugate Fourier transforms, respectively. Equations 9
and 10 define these Fourier transforms (FT). Re and Im indicate real and imaginary
components of the FT and the Fourier frequency ω, is an individual frequency
component of ~
y  1 , t  or ~
y  2 , t  with varying t.
 ~
1
~
Y1    Y2*  d

 Tmax  Tmin  0
 1 , 2   i  1 , 2  
Equ A2.5

~
~
~
Y1     ~
y  1 , t e it dt  Y1 Re    iY1 Im  
Equ A2.6

~
~
~
Y2*     ~
y  2 , t e it dt  Y2Re    iY2Im  
Equ A2.7


Application of the appropriate FT to the dynamic spectrum directly yields the
synchronous and asynchronous correlation spectra, defined by their intensities in
Equation A2.6 and A2.7.
2.3. FORMALISM FOR DISCRETE DATA
Cross correlation analysis theory may be used to simplify the above equations so that
the synchronous 2D correlation spectrum is expressed as a product of the variation in
two different spectral variables over the perturbation range of the dynamic spectrum,
as shown in Equation A2.8.
 1 , 2  
Tmax
1
 Tmin

Tmax
Tmin
~
y  1 , t   ~
y  2 , t dt
Equ A2.8
Similarly, the asynchronous 2D correlation spectrum can be expressed as a product.
In this case it is the product of the dynamic spectrum and the orthogonal spectrum
243
APPENDIX 2
2D CORRELATION ANALYSIS
which is produced by introducing a phase shift into the components of the Fourier
transform, as shown in Equation A2.9, where ~
z  , t  is the orthogonal spectrum.
1
  1 , 2  
Tmax
1
 Tmin

Tmax
Tmin
~
y  1 , t   ~
z  2 , t dt
Equ A2.9
Data gathered from XAS is discrete, not continuous, so the integral in Equation A2.8
and A.2.9 may be replaced by a discrete summation and the perturbation interval
may be expressed as the number of spectra (m) minus one, resulting in Equation
A2.10 and A2.11, with i = 1, 2, … m.
 1 , 2  
1 m ~
 yi  1   ~yi  2 
m  1 i 1
Equ A2.10
  1 , 2  
1 m ~
y i  1   ~
z i  2 

m  1 i 1
Equ A2.11
Calculation of the synchronous spectrum is thus straightforward. Using the vector
notation for the discrete data in Equation A2.12, the synchronous spectrum in
Equation A2.13 is equal to the inner product of two dynamic spectra vectors,
averaged over all the spectra.
 ~y  , t1  
~

~y   y  , t 2  
  
~

 y  , t m 
Equ A2.12
 1 , 2  
Equ A2.13
1 ~

y  1  ~
y  2 
m 1
The situation for the asynchronous spectrum is slightly more complex because it is
first necessary to produce the orthogonal spectrum. Fourier transformation was used
in early work on 2D correlation. However the Hilbert transformation has since been
proposed and used to directly produce the orthogonal spectrum via matrix
multiplication.351 This is much more computationally efficient than the Fourier
244
APPENDIX 2
2D CORRELATION ANALYSIS
transform except at very high values of m (resolution of measured variable). The
asynchronous spectrum may therefore be expressed as in Equation A2.14, where the
vector N is the Hilbert-Noda transformation matrix in which the conditions in
Equation A2.15 exist.
  1 , 2  
1 ~

y  1  N~
y  2 
m 1
if j  k
0
N jk  
1 /  (k  j ) otherwise
Equ A2.14
Equ A2.15
The inner product is calculated by multiplying the matrix containing the spectra (in
one dimension) at each interval of the perturbation (the second dimension),
transposed, by the original matrix. A multiplication using matrices will produce the
required 2D spectra.
2.4. ANALYSIS OF SYNCHRONOUS 2D CORRELATION
SPECTRA
Synchronous 2D correlation spectra contain information about simultaneous or
coincidental changes v1 and v2 during the interval Tmin to Tmax. The peaks along the
diagonal indicate all features which change during the course of the experiment.
Positive and negative cross peaks contain information on the relationship between
the features in spectra. Spectral features which do not change during the experiment
do not appear in either the synchronous or asynchronous spectra. A schematic
representation of a synchronous spectrum, measured by IR is shown in Figure A2.2.
245
APPENDIX 2
2D CORRELATION ANALYSIS
Figure A2.2. Schematic diagram of synchronous correlation spectrum (a, left)
and asynchronous correlation spectrum (b, right).3
Positive cross peaks indicate that the spectral features change in the same direction as
each other i.e. the intensity of the two correlated features either both increase or
decrease due to the same change in the external perturbation. Conversely, negative
cross peaks indicate features where, as an increase in one feature occurs, a decrease
in the other is found.
Using these rules, it can be seen that the changes occur at peak A at a similar value of
t compared to C, but in opposite directions and at peak B similarly to peak D, in the
same direction.
2.5. ANALYSIS OF ASYNCHRONOUS 2D CORRELATION
SPECTRA
The peaks in asynchronous 2D correlation spectra indicate sequential or successive
changes in features as a function of t. No diagonal auto-peaks are present because it
is not possible for the same feature to meet this condition. The spectrum is also
anti-symmetric about the diagonal as shown in Figure A2.2 (b).
The cross peaks are interpreted alongside those in the synchronous spectrum. If the
change at v1 occurs largely before that at v2 then the cross-peak will be positive, as
246
APPENDIX 2
2D CORRELATION ANALYSIS
long as the corresponding cross peak in the synchronous spectrum is positive. If the
latter is negative then the rule is reversed. Likewise, if the change at v2 occurs largely
before that at v1 then the cross-peak will be negative, but if the corresponding cross
peak in the synchronous spectrum is negative, the former will instead be positive.
This complication in the interpretation of the asynchronous spectrum means that
cross peaks in this spectrum which do not have corresponding cross peaks in the
synchronous spectrum cannot be assigned.
A more detailed analysis of Figure A2.2 (b) would show that there are positive cross
peaks for (v1,v2) pairs (B,A), (D,A) and (C,B), a negative cross peak for (D,C) and
the expected anti-symmetric pairs for the top left portion of the spectrum. If the
synchronous spectrum was not used for comparison, this would indicate a change in
B and D before A, and C before B, but also C before D. However, the lack of cross
peaks in the synchronous spectrum at these values means that these changes cannot
be definitively assigned.
2.6. COMPLEXITY IN INTERPRETATION
Distortions of the one dimensional dynamic spectra, which may include baseline
shifts and fluctuations, noise and changes in peak positions and widths can cause
difficulty with the interpretation of 2D spectra if corrections are not made to the
original spectra.352 A failure to make corrections of baseline fluctuations and noise
can cause a simple asynchronous spectrum with two discrete cross peaks to become
smeared and therefore very difficult to interpret. Removal of the effect of the
baseline fluctuations may be achieved through use of the second derivative of the
spectrum.
Changes in peak position and peak width can occur in the same spectra and cannot be
removed without loss of data. A peak shift may be distinguished from changes in two
separate, heavily overlapped bands, because the former will have distinctive
elongated contours near the diagonal, as shown in Figure A2.2,353 whilst the latter
247
APPENDIX 2
2D CORRELATION ANALYSIS
will have only two discrete peaks. This difference may not be distinguished from the
synchronous spectrum.
Figure A2.3, Asynchronous spectrum characteristic of a shifting band. This
usually is indicated by a “butterfly” pattern.353
A change in bandwidth may also produce a distinctive pattern, with two auto-peaks
and two cross peaks in the synchronous spectrum and an additional two peaks in the
asynchronous spectrum (Figure A2.3). The exact pattern, however, depends on the
size of the change in intensity and peaks in XANES spectra may be broader leading
to merging and overlapping of bands even in the 2D spectra.354
248
APPENDIX 3
PUBLICATIONS
3. PUBLICATIONS
Refereed proceedings
1. High Throughput In Situ XAFS Screening of Catalysts
Nikolaos Tsapatsaris A. M. Beesley, Norbert Weiher, Moniek Tromp, John Evans, A. J.
Dent, Ian Harvey, and Sven L. M. Schroeder
AIP Conf. Proc. 882, 597 (2007)
2. High Throughput In Situ EXAFS Instrumentation for the Automatic Characterization of
Materials and Catalysts
Nikolaos Tsapatsaris, A. M. Beesley, Norbert Weiher, Moniek Tromp, John Evans, A. J.
Dent, Ian Harvey, and Sven L. M. Schroeder
AIP Conf. Proc. 879, 1739 (2007)
3. High-Throughput Structure/Function Screening of Materials and Catalysts with Multiple
Spectroscopic Techniques
Moniek Tromp et al.
AIP Conf. Proc. 882, 858 (2007)
4. Moniek Tromp*, Sergio Russu, Andy J Dent, J Fred Mosselmans, Ian Harvey, Shu
Hayama, Andrea E Russell, Sam Guerin, Brian E Hayden,
Ken Meacham, Panagiotis Melas, Mike Surridge, Jeremy G Frey, Nikolaos Tsapatsaris,
Angela M Beesley, Sven M L Schroeder and John Evans
Materials Chemistry 8, RSC, MC8: Advancing Materials by Chemical Design (2007)
5. Moniek Tromp, Sergio Russu, Andy J. Dent, Jeremy G. Frey, Ian Harvey, Nikolaos
Tsapatsaris, Angela M. Beesley, Sven M. L. Schroeder,
J. Fred. Mosselmans, A. E. Russell, Mark Weller, John Evans, University of Southampton
EUROPACAT- VIII, P4-7, (2007)
6. In Situ XAS Studies on the Structure of the Active Site of Supported Gold Catalysts
Norbert Weiher, Angela M. Beesley, Nikolaos Tsapatsaris, Catherine Louis, Laurent
Delannoy, Jeroen A. van Bokhoven, and Sven L. M. Schroeder
AIP Conf. Proc. 882, 600 (2007)
7. High-Throughput Synthesis and Characterization of BiMoVOX Materials
Sergio Russu, Moniek Tromp, Nikolaos Tsapatsaris, Angela M. Beesley, Sven L. M.
Schroeder, Mark T. Weller, and John Evans
AIP Conf. Proc. 882, 535 (2007)
8. Microreactor Cells for High-Throughput X-ray Absorption Spectroscopy
Angela Beesley, Nikolaos Tsapatsaris, Norbert Weiher, Moniek Tromp, John Evans, Andy
Dent, Ian Harvey, and Sven L. M. Schroeder
AIP Conf. Proc. 879, 1735 (2007)
9. Reducibility of supported gold (III) precursors: influence of the metal oxide support and
consequences for CO oxidation activity
Laurent Delannoy, Norbert Weiher, Nikolaos Tsapatsaris, Angela M. Beesley, Luanga
Nchari, Sven L.M. Schroeder, Catherine Louis,
249
APPENDIX 3
PUBLICATIONS
Université Pierre et Marie Curie - Paris VI
EUROPACAT- VIII, P4-9
Unrefereed Proceedings
Published in the Proceedings of the Fifth International Conference on Synchrotron Radiation
in Material Science SRMS5
To appear in an ANL report and available on their website.
10. High throughput In Situ EXAFS instrumentation for the automatic characterization of
materials and catalysts.
Nikolaos. Tsapatsaris, 1A. Beesley, N. Weiher, M. Tromp, J. Evans, A. Dent, I. Harvey, S.
L.M. Schroeder
11. High Throughput in Synchrotron Science: A New Approach in X-ray Absorption
Spectroscopy
A. Beesley, N. Tsapatsaris, N. Weiher, M. Tromp, J. Evans, A. Dent, I. Harvey, S. L.M.
Schroeder
National Instruments Days 2007
12. High throughput automatic characterisation of materials using the Labview platform
Nikolaos Tsapatsaris, Angela Beesley, Sven L.M. Schroeder
Refereed Journal Publications
13. Activation of Oxygen by Metallic Gold in Au/TiO2 Catalysts
Norbert Weiher, Angela M. Beesley, Nikolaos Tsapatsaris, Laurent Delannoy, Catherine
Louis, Jeroen A. van Bokhoven, and Sven L. M. Schroeder
J. Am. Chem. Soc.; 2007; 129(8) pp 2240 - 2241; (Rapid Communication) DOI:
10.1021/ja067316c
14. Reducibility of supported gold (III) precursor: influence of the metal oxide support and
consequences for CO oxidation activity
Laurent Delannoy, Norbert Weiher, Nikolaos Tsapatsaris, Angela M. Beesley, Luanga
Nchari, Sven L.M. Schroeder Catherine Louisa
Topics in Catalysis Vol. 44, Nos. 1–2, June 2007 (2007)
To be submitted to Review of Scientific Instruments
15. Novel beamline instrumentation for the high throughput characterisation of materials and
catalysts under process conditions using hard X-ray synchrotron radiation
Nikolaos Tsapatsaris, Angela Beesley, Norbert Weiher, Moniek Tromp, Sergio Russu, John
Evans, Andy J. Dent, Frederick J.W. Mosselmans, Shusaku Hayama, Ian Harvey, Sven L. M.
Schroeder*
To be submitted to the Journal of the American Chemical Society
16. Novel beamline instrumentation for the high throughput characterisation of materials and
catalysts under process conditions using hard X-ray synchrotron radiation
Nikolaos Tsapatsaris, Angela Beesley, Norbert Weiher, Moniek Tromp, Sergio Russu, John
Evans, Andy J. Dent, Frederick J.W. Mosselmans, Shusaku Hayama, Ian Harvey, Sven L. M.
Schroeder*
250