Combinatorial Approaches in
Heterogeneous Catalysis
Jochen Lauterbach
University of South Carolina
Department of Chemical Engineering
SmartState Center for Strategic Approaches to theGeneration of Electricity
lauteraj@cec.sc.edu

1Catalysts: At the Heart of Refineries,
Chemicals, Energy, Environment

90% of all chemical products involve
catalysts at some stage
2010: Catalysts involved in ~ $10 trillion ingoods & services of Global GDP
2010: global catalyst market was $30 bio.
2

Impact of Catalysis• Fertilizer production• Fuels• Emissions reduction• Electronic materials• Plastics• Paint• ………….
3

Annual average of toxic mobile emissions in Los Angeles County from 1975 to 2008.

Somorjai G A , and Li Y PNAS 2011;108:917-924

4©2011 by National Academy of SciencesPollution Control in your Car5

6

Courtesy Mike Davis, Santee Cooper7• 1823 J. W. Döbereiner
discovers that metals glow incontact with air and a
combustible gas• Döbereiner lighter• By 1828 sold over 20,000

9Nano-materials Drive Innovation

10

Multiple Lengthscales in Catalysts

2 cm

11

“Real Catalysts”
Parameter Space
COST & SCALE UP
Precursor
Active metal and loading
Promoter and loading
SupportSynthesis method
Calcination time
Calcination temp
Reduction pretreatment
Temperature
Time
Pressure
Concentration
Space velocity
12

Catalyst Process Development

13

Combinatorial Principle
High-throughput catalyst discovery
and optimization• Parallelization• 10’s to 1000’s of samples/run

Samplesynthesis

Experimental
designTesting

Evaluation

14
14

High-throughput vs. combinatorial• Use of terms is confusing• ‘Combinatorial” should refer to experiments inwhich groups or elements of different materials orcomponents of a recipe (solvents, additives…) are
combined• Change in nature of parameters, not in the value ofthe parameters• Systematic variation of a given composition oroperating parameters to explore a wider parameter
space is a “high-throughput” experiment
15

Maier et al., Angew. Chem. Int. Ed. 2007, 46, 6016 – 6067

Early example• Haber–Bosch process

N2 + 3 H2 → 2 NH3• In 1909, the process made 125 ml per hour
• Reaction is conducted at 150–250 atm and
600–750 K• BASF assigned Carl Bosch to scale up andin 1913 production reached 20 tonnes/day• 131 million tonnes of ammonia in 2010 = ~1 mio. railroad cars

16• Alwin Mittasch (1869 –1953)• Discovery and optimizationof the NH3 synthesis
catalyst• Replace Os/U catalyst(Haber) with commerciallyacceptable materials• “Two dozen or more”
parallel lab scale reactors

17

A. Mittasch, 1950. Adv. Catal. 2:81-104

18

A brief history• 1970 – Hanak makes composition spread /gradient libraries at RCA labs
• 1980 - Parallel reactors for applications inheterogeneous catalysis (Moulijn)• 1986 - Six parallel reactors for the testing ofheterogeneous catalysts (Creer)19

J. G. Creer, et al., Applied Catalysis 1986, 22, 85,“Design and Construction of a Multichannel
Microreactor for Catalyst Evaluation”

20Schultz Science paper21

A brief history• 1995 – Symyx founded based on Schultz paper• 1998 – First paper Senkan group• 1999 – Many other academic groups follow(Lauterbach, Thompson, Bein, Schuth, Crabtree,
Baerns, Maier, and many more…)• 1999 – HTE founded (currently largest provider)• 2000 – Avantium founded22

Catalyst Discovery• Two different scenarios: discovery andoptimization.
• Discovery strategy is applied when totally newmaterials are the target (existing catalysts havelittle potential for improvements / no suitablematerials are known).
– Sampling of broad and highly diverse parameter spaces.
– Experimental conditions are typically compromised forthroughput.
– Risk of many false positives and false negatives
23

Catalyst Discovery• Search conditions are often oriented at the best
conditions for existing materials– Give the latter an advantage and contribute to false
negatives and a significant reduction in the number ofhits.

• Deviations between primary screening conditions
and real reactors– Imperative to reproduce effective hits resulting fromprimary screening data by conventional synthesis– Confirm expected performance by conventional
measurements
24

Catalyst Optimization• Optimization aims to accelerate thedevelopment of known materials– Relatively narrow, well-defined parameter
spaces around known materials are sampled
under conditions close to conventional
experimentation– The known catalyst to be optimized may be a hit
discovered by primary screening or it may be
another well-known catalyst.

• High accuracy of the data needs reduction in
the number of samples studied
25

Combinatorial Cycle

Model Predicted NOx storage

70

Design Points
Validation Points

60
50
40

Hattrick-Simpers, J. R., Wen, C., and Lauterbach, J.
“The Materials Super Highway: Integrating HighThroughput Experimentation into Mapping theCatalysis Materials Genome” Catalysis Letters
145, no. 1 (2014): 290–298.

Hypothesis
Generation

Rapid
Synthesis

Data
Minimization
and Analysis

ParallelScreening

30
20
10
0
0

10

20

30

40

50

60

70

Experimental NOx storage

26

HT Synthesis• Thin-film techniques– Continuous composition spreads: cosputtering and co-evaporation.

• Solution-based methods• Challenge is to create samples with thesame properties when prepared underidentical conditions– Variations between runs will mask systematic
trends.
27

Sputtering Chamber

28

Discrete sample libraries

29Compositional Gradient Films

30

Primary Optical Screen of Activity
Bulk Support Verification

Sample Synthesis
Al2O3

SiO2

-Al2O3
Substrate

Optical measurements

Nb2O5
Expose to JP-8
Darkening  Carbon coating  Activity

31

Inkjet printing of catalysts

Xiang et al.,ACS Comb Sci, 201432

Ink-jet printing assisted cooperativeassembly method (IJP-A)(Fe-Co-Ni-Ti)Ox pseudoquaternary catalyst library
Metal precursor formulations
contain block copolymer structure
directing agents

Drying/precipitation andcalcination protocol to yield
porous metal oxide thin-ﬁlm
samples

Gregoire et al.,Journal of The Electrochemical
Society, 160 (4) F337-F342 (2013)33

Solution-based “Bulk” Methods• Solution methods are fairly complex andsensitive to handling procedures– Experimental conditions– T, ramp rates, p, pH– Nature of solvents– Preparative procedures (mixing order,
washing, filtering, and drying)– Human factor– Scalability and reproducibility– Formation of metastable structures
34

Automated Preparation Options

35

Microemulsion Synthesis
Microemulsion Phase Diagram

Reverse Micelles as “Nanoreactors”
ReverseMicellesMicellesTwo Phase

Water

Oil

Bicontinuous phase

Two Phase
S. Eriksson, U. Nylen, S. Rojas, M. Boutonnet, Applied Catalysis A: General 265 (2004) 207

100% Oil

N2 H4
Ru

Ru
Ru

Ru

Ru

Ru

+
Powder-Al2O3

Ru+3
Ru+3
Ru+3

Wash with acetone tobreak reverse micelle
Ru

Nanoclusters settle
onto support

Ru
Ru

Ru

Ru

Ru

Support

Reverse Micelle Diameter (nm)

100% Water

20
18
16
14
12
10
8

ω

[H 2 O][Surfactant]6
2 4 6 8 10 12 14 16 18 20



J. Hoefelmeyer, H. Liu, G. Somorjai, T. Tilley, J. Colloid and Interface Science 309 (2007) 8636

ω=1.0Effect of ω on Ru particle size

Dynamic Light
Scattering (DLS)
used to determine
reverse micelle size

ω=1.1

ω=1.2

ω

[H 2 O][TritonX  2 - propanol]

ω=1.3
Surfactant:

Triton X-100 (polyethylene glycol p(1,1,3,3-tetramethylbutyl)-phenyl ether)

Co-Surfactant

2-propanol

Oil Phase:

Cyclohexane

Water Phase:

RuCl3 (active metal) or N2H4 (reductant)37

Parallel Microwave Synthesis• Hydrothermal synthesis ofzeolites• MARS Microwave Oven dual purpose synthesis &acid digestion

Split and Pool Methods

39

Split and Pool Methods

40Screening Approaches• In situ vs. post-reaction methods• Serial vs. parallel techniques• Factors to be considered for HT tools– Analysis speed– Sample size
– Quantification precision and accuracy– Need for the availability of samples for further characterization• If a method has not been used in HT mode, anappropriate conventional technique using larger sample
sizes or other required parameters should be chosen as
reference41

Parallel Reactors

42

Microfluidics• Involves the handling of fluids indevices containing channels in the
micrometer-size regime.
• Parallel microfluidic reactor
system, which consists of a
US6902934B1
microfluidic flow distribution
system, a 256-element catalyst
array, and colorimetric detection
methodology to allow parallel
reaction and parallel detection– Gas-phase oxidation of ethane toacetic acid
– Oxidative dehydrogenation of ethane
to ethylene– Selective ammoxidation of propane toacrylonitrile

43

HTE Wheel reactor

44

High Throughput Reactor
Simultaneous testing of 16 powder catalysts

Reactor conditions
1. Flowrate 100 sccm,
GHSV= 40,000 mL/(hr*gcat)
Carrier gas = He
2. Atmospheric pressure

RJ Hendershot, et al. Applied Catalysis A, 2003

45

High-Throughput Reactor
•
•
•
•

•
•
•

16 parallel plug flow reactors
Capillary flow distribution system
Individual catalyst bed thermocouples
Four furnaces with PID control(Tmax=950°C)
Powder catalysts: 0.05-1 g
Space velocities: 3,000-240,000 ml∙hr-1
g-cat-1
Moving top plate and winch system for
efficient loading/unloading

Sasmaz et al., Engineering 1(2), 2015

Monolith HT
Reactor System
267.3

o

Temperature (C)

267.2

0.7oC

267.1

Capillaries forconsistent flow
profile

267.0
266.9

K TypeThermocouple

266.8
266.7
266.6
266.5
0

1

2

3

4

5

6

7

Reactor Number

18 mm

450

o

Temperature (C)440

Reactor 1
Reactor 2
Reactor 3
Reactor 4

Heating tapefor inlet gas
preheating

430

420

410

400
1000

1020

1040

1060

Time (sec)

1080

1100

47
J.C. Dellamorte, et al., Rev Sci Instru 78

Reactor Setup
HTR outlet flow rates 2/19/2014
set point = 125 ccm

160
140

Flow rate (ccm)

120
100
80
60
40
20
0
0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Channel #Parallel temperature control

Parallel flow rates and feed gas
compositions48

Liquid/vapor reactions• 16-channel condenser fabrication• Removal of heavier hydrocarbons
from reactor effluent• IR imaging and GC-MS analysis• Quantitative gas productmeasurement

49

50

Catalysts 2016, 6(2), 23

Sequential Screening• GC or GC/MS via automated valves• Arrays of GCs• Scanning mass spectrometry with concentric
capillaries and xyz stage• Screening time ~ to number of samples

Orschel, et al.,Angew. Chem. Int. Ed 38 (1999) 2791

51

52

Catalysts 2016, 6(2), 23

53

High-throughput Gas Chromatography (HT-GC)• 20% CH4+ 80 %N2Single chromatogram• 6 bit, 63 elements

Convoluted chromatogram• 1 min interval between injections

Pseudo random injection
sequence[1, 1, 1, 1, 1, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,
1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,
0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0,
1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,
0, 0, 1, 1, 0, 1, 0, 1, 0]

Apply Hadamardtransformation todeconvolute

To increase throughput :1) Long binary pseudo-random sequences
2) Short injection intervals3) Stable and reproducible sample injections onto the separation column

Validation of HT-GC via Ethylene Epoxidation
1
2
3
4
5
6
7
8
9
10
11
12
13
14

Blank
USC-10067 12wt% Ag,
350ppmCs,150ppmRe
Cu-(Au-Ag) 500 ppm Au
Cu-(Re-Ag) 25 ppm Re(Cu-Au)-Ag 500 ppm Au(Cu-Re)-Ag 25 ppm Re(Cu-Cs)-Ag 50 ppm Cs
Cu-(Re-Ag) 125 ppm Re
Cu-(Sn-Ag) 50 ppm Sn
USC-OA5 14 wt% Ag,
200 ppm Sn
Cu-(Sn-Ag) 450 ppm Sn
1%Cu-Ag
0.2%Cu-Ag
250 ppm Cu-(Sn-Ag)••

13 catalysts
10%C2H4, 10%O2 at 250C

Fast-sequential and Simultaneous Gas Analysis

Ethylene Concentration
0.035

0.15

0.03

Concentration

Concentration

ETO Concentration

0.1

0.025

0.05

0.02

0.015

0
1 2 3 4 5 6 7 8 9 1011121314
Reactor Channel

Single Run

HT GC

HT FTIR

0.01

0.005
0

1 2 3 4 5 6 7 8 9 1011121314
Reactor Channel
Single RunsHT GC
HT FTIR• Accurate measurements can be made using HT-GC• Results are comparable with the FTIR measurementsComparison of most active catalysts for ethylene
epoxidation• In comparison to single GC
60

measurements, HT-GC

Etylene Conversion

50
Single Run

40

HT GC

30

HT FTIRdecreases analysis time by 3.2x
• Analysis time can be decreased20

by 5x using the current setup (5h10vs 1h)

02

10
Reactor #13• Possibilities for kinetic

measurements.

Parallel Screening Approaches
Screening time ~ number of samples in field of view
• Infrared thermography (Wilson group,
1996)– Emission corrected IR thermography(ecIRT, Maier group) enables temperature
differences down to 0.02 K to be detected
and the heat evolution identified from
catalyzed gas-phase reactions with small
catalyst amounts (<20 mg).
– Reactions have been observed attemperatures up to 650oC– Cannot differentiate between desired andundesired reactions– Detect activity, but not selectivity

From Maier group web page58

Chemically Sensitive, Parallel Screening
A

A

Radiation from

B
B

SpectrometerCC

Throughput ~ number of samples in the field of view 59

Parallel Screening Approaches• REMPI (Senkan group)• LIFI (Yeung group, 2000)– Detect fluorescence emission among all possible
products (Selective Oxidation of Naphthalene)• Dye molecules & filter paper (Schüth group, 2002)

60

Parallel Screening Approaches• FTIR Imaging
4mm

4mm

• Conventional FTIR Spectrometer– One sample, one detector, one spectrum• Focal Plane Array Detector (FPA)– Infrared sensitive analog of a video camera– 128x128 elements from HgCdTe – sensitive over
4000-900cm-1• Conventional FTIR Spectrometer + FPA =FTIR Imaging

MCT

Indium“bumps”

Silicon– Acquire many spectra simultaneously from over a
region of a sample
61

FTIR Imaging

Spatial, x

Absorbance

128 x 128MCT
FPA

Spatial, yHyperspectral
Data Cube

Wavenumber (cm-1)

SampleSnively, C.M. and J. Lauterbach Applied Spectroscopy 59(2005)
Snively, C.M., S. Katzenberger, G. Oskarsdottir, and J. Lauterbach Optics Letters 24 (1999)

FTIR
Spectrometer62

Resin-Supported Libraries• Solution phase combinatorial organic
synthesis• Reactions carried out on ligands supportedon polymer beads• Final products are attached to beads or insolution

In-Situ Reaction Kinetics• Analyze reactions occurring on multiple supported ligands• Methodology– Place beads in a flow cell in the field of view of the instrument– Introduce reactant solution and acquire data over time

~1mm

J. Lauterbach, C.M. Snively, G. Oskarsdottir,

3.0

Introduce solvent
Introduce reactants

Acquire data
during reaction

Normalized Peak Area

Dry beads placedin a 50mm flow cell

2.5
2.0
1.5
1.0
0.5

k = 2.3 +/- 0.3 x10-3 s-1

0.0
0

400

800

1200

Time (s)

1600

2000

Parallel Transmission Gas Analysis• 16-element gas-phase array• IR gas-phase spectroscopy– Works for any gas with IR signature– Analyze all 16 product streams
in parallel in < 2 sec
Spatially resolved IR spectra
128 x 128 = 16,384 detectors

Absorbance [A.U.]

0.16

H2O

0.12

• Chemometrics needed for complex
spectraNOC2H4H2O
H2O
NO
C2H4

C2H4
0.08

NO
0.04

NO + C2H4 + H2O

0.00
2000

1800

1600
-1

Wavenumbers [cm ]128pixelsFPA
128
pixelsSnively, C.M. and J. Lauterbach Applied Spectroscopy 59(2005)
Snively, C.M., S. Katzenberger, G. Oskarsdottir, and J. Lauterbach Optics Letters 24 (1999)

65

Transient IR Data From One Channel

Switch from fuelrich to fuel lean;
Introduce oxygen

66

Multi-channel Transient Analysis Capacity
450

Reaction progress with time

425
400
375
350

T [ C]

325
300
275
250
225
200
175
150
0

6

12

18

24

30

36

time [minute]

42

48

54

60
67

5% Rh/Al2O3 T = 650oC
Hydrocarbons
CH4

69

70

71

“A poorly designed […high-throughput…]
experiment will give us bad information
with unprecedented speed
and in outstanding quantities”

James S. Cawse, Experimental Design for
Combinatorial and High Throughput Materials Development, p. 2
Wiley 2003

Computational Techniques• “A major bottleneck in high-throughput
techniques is … is often data management anddata analysis.”• The development of the computational methods
has progressed rapidly• Many laboratories are hesitant to use thesemethods because of a lack of manpower,
complexity, and lack of access to establishedsoftware.”

Maier et al., Angew. Chem. Int. Ed. 2007, 46, 6016 – 6067

73

Design of Experiments
Maximize experimental efficiency
Quantify main effects and interactions: empirical models
SCREENINGDESIGNINTERACTION
DESIGNRESPONSE
SURFACEDESIGN

Progression of Experimentation In Practice# Variables

6 or more

3-6

2-6

Type of

Crude
Information

Quantitative
Information

Empirical models
estimates of effects

TypicalObjectives

Rank variables
in order ofimportance

Understand systembehavior, includinginteractions

Develop high quality
predictions, property
optimization74

Screening Design
X3

X1

X2

Linear effects

Y: performance criterion
xi: parameters
i: coefficients fitted to experiments

75

Response Surface
RESPONSE
SURFACEDESIGNS

LINEARDESIGNS

X3

X1

X2

Y  C   1 X 1   2 X 2   3 X 3  ...   12 X 1 X 2   13 X 1 X 3  ...   123 X 1 X 2 X 3  ...

Y  C   1 X 1   2 X 2  ...   12 X 1 X 2  ...  1 X 12   2 X 22  ...

Example: Response Surface Design: NOx Storage
Parameter

Pt (%)

Rh (%)

Co (%)Ba (%)T (ºC)

Low

0

0

0

0

300

Mid

1

0.5

2.5

7.5

350

High

2

1

5

15

400

Full Central Composite Design (5 factors)

Reaction conditions

NO = 0.15%
5

O2 = 6% or 0%
CO = 0.9 %

Co
(wt%)

C2H4=0.15%
Data points tested for design= 45
Validation points =21

0

0

Pt (wt%)1

Work funded by National Science Foundation
77

R  C  1 ( Pt )   2 ( Rh)  12 (Co)......  1 ( Pt )   2 ( Rh) ......  1 ( Pt * Rh)  2 ( Pt * Co).....
22

Model Prediction vs. Experimental Values
100

Model development points
Validation points

Coefficient

Constant

44.68

Pt

3.72

Rh

2.97

Ba

30.6

Co

-1.05

T

-2.69

Pt*Pt

-12.63

Rh*Rh

-7.93

Ba*Ba

9.34

Co*Co

-14.38T*T-24.06

Pt*Ba

13.19

Pt*T

-7.12

Rh*Ba

9.54

80
70

-6

Model Predicted NOx storage [10 moles NOx]

90

Term

60
50
40
30
20
10

All catalyst loadings are actual loadings
0
0

20
40
60
80
-6
Experimental NOx Storage [10 moles NOx]

Optimum Catalyst Composition

100

1.4Pt/0.9Rh/4Co/23Ba

78

Optimum Catalyst Testing for NOx Storage
1Pt/15Ba
1Pt/5Co/15Ba
1.4Pt/0.9Rh/4Co/34Ba
1.4Pt/0.9Rh/4Co/23Ba

Switch fromrich to lean
1600

T=375C

1400

Total cycle time = 45 min
Lean phase = 30 min (1800 sec)
Rich phase = 15 min (900 sec)

NOx concn. [ppm]

1200
1000

Reaction conditions
NO = 0.15%
O2 = 6% or 0%
CO = 0.9 %T= 375ºC
C2H4=0.15%

800
600
400

Stores NOx for ~ 15 minutes

200
0
0

3

6

9

12

15

18

Time [min]

21

24

27

30

79

Genetic algorithms• GAs are ideally suited for high-throughput experiments
since they require a population of individual samples• GAs have been applied to combinatorial materials
research for over ten years• Nonlinear, adaptive, and often heuristic methods forsolving optimization and search problems• In nature, populations evolve over many generations
following the principles of natural selection• Gas generate artificial populations to undergo an
evolution that can approach an optimal solution
80Genetic algorithms• The aim of applying a GA is to improve a starting
solution (library) for a given problem within each iteration• The individuals are evaluated by the fitness function(desired property)• The best individuals are used to produce an offspring
generation with the help of selected algorithms– Mutation– Cross-over– Recombination

81

The choice of algorithms and the strategy applied to generate the offspring
generations characterize each GA and are responsible for success or failure.
Good review: M. Holena in High-Throughput Screening in Chemical Catalysis(Eds.: A. Hagemeyer, P. Strasser, A. F. Volpe), Wiley-VCH, Weinheim, 2004, pp.
153 – 174.

82

J. M. Caruthers, J. A. Lauterbach, K. T. Thomson, V.
Venkatasubramanian, C. M. Snively, A. Bhan, S. Katare, G. Target Catalyst
Oskarsdottir, J. Catal. 2003, 216, 98 – 109.

Catalyst LibraryComparePerformance

HTE

XYZ

{k}

C +R

A-S +D

k3

A-S

Statistical Analyzer
Pseudo Global Optimizer

Feature Extractor

D

Pseudo English
Rule Compiler

F

Rate/Selectivity

Target Catalyst Performance

Model
Revision

k1
Statistics/Neural-Nets
A +S
Physical Modelk2

Rate/Selectivity

Catalyst Design Framework

XYZ

Kinetics

Catalyst

Hybrid Model

AI/Systems Tools

Reaction Modeling Suite

Forward Model

Recombination

Selection

Genetic Algorithms
Inverse Model

Catalyst Performance

Acceptance• Well accepted in industry (UOP, ExxonMobil,
Shell, BASF, DOW, Mitsubishi Chemicals,
Toyota,…..)• Often seen by academics as a means to replace
intelligence by a large number of experiments• Reproducible preparation of materials andcomparability of measured data
• Allows the study of correlations and trends inways not possible by any conventional one-at-atime experimentation
84

Advantages• Reduced development times for new andoptimized materials• Reduced time to market• Rapid sampling of large parameter spaces• Rapid collection of comparable data
• Acceleration of basic research

85Problems• HT generates good data rapidly, but it also
generates false positives and negatives very well
– How many HT attempts had to be abandoned becauseof the lack of agreement with data from conventional
experiments?

• It is crucial to verify the quality of the HT data withthose obtained from conventional tests• Time and $$$ for development of suitablescreening techniques– Universality of the techniques
86Some Suggested Reading• J. Hattrick-Simpers, C. Wen and J. Lauterbach, “TheMaterials Super Highway: Integrating HighThroughput Experimentation into Mapping theCatalysis Materials Genome”, Catalysis Letters, 145(1), 290-298, 2015
• E. Sasmaz, K. Mingle, and J. Lauterbach, “HighThroughput Screening Using Fourier-Transform
Infrared Imaging”, Engineering 1 (2), 234-242, 2015.
• Maier et al., Angew. Chem. Int. Ed. 2007, 46, 6016
• S. Kang et al., Top Catal (2010) 53:2–1287