Combinatorial Approaches in

Heterogeneous Catalysis

Jochen Lauterbach

University of South Carolina

1

Department of Chemical EngineeringSmartState Center for Strategic Approaches to the

Generation of Electricitylauteraj@cec.sc.edu

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 in

goods & 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

©2011 by National Academy of Sciences

4

Pollution Control in your Car

5

6

7Courtesy Mike Davis, Santee Cooper

• 1823 J. W. Döbereiner

discovers that metals glow in

contact with air and a

combustible gas

• Döbereiner lighter

• By 1828 sold over 20,000

9

Nano-materials Drive Innovation

10

Multiple Lengthscales in Catalysts

11

2 cm

“Real Catalysts”

12

COST & SCALE UP

Precursor

Active metal and loading

Promoter and loading

Support

Synthesis method

Calcination time

Calcination temp

Reduction pretreatment

Temperature

Time

Pressure

Concentration

Space velocity

Parameter Space

Catalyst Process Development

13

14

Combinatorial Principle

14

Experimental

design Testing

Sample

synthesis

Evaluation

High-throughput catalyst discovery

and optimization

• Parallelization

• 10’s to 1000’s of samples/run

High-throughput vs. combinatorial

• Use of terms is confusing

• ‘Combinatorial” should refer to experiments in

which groups or elements of different materials or

components of a recipe (solvents, additives…) are

combined

• Change in nature of parameters, not in the value of

the parameters

• Systematic variation of a given composition or

operating parameters to explore a wider parameter

space is a “high-throughput” experiment

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

15

• 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 and

in 1913 production reached 20 tonnes/day

• 131 million tonnes of ammonia in 2010 = ~

1 mio. railroad cars 16

Early example

• Alwin Mittasch (1869 –1953)

• Discovery and optimization of the NH3 synthesis catalyst

• Replace Os/U catalyst (Haber) with commercially acceptable 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 in

heterogeneous catalysis (Moulijn)

• 1986 - Six parallel reactors for the testing of

heterogeneous 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 paper

21

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 founded

22

Catalyst Discovery

• Two different scenarios: discovery and

optimization.

• Discovery strategy is applied when totally new

materials are the target (existing catalysts have

little potential for improvements / no suitable

materials are known).

– Sampling of broad and highly diverse parameter spaces.

– Experimental conditions are typically compromised for

throughput.

– 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 of

hits.

• Deviations between primary screening conditions

and real reactors

– Imperative to reproduce effective hits resulting from

primary screening data by conventional synthesis

– Confirm expected performance by conventional

measurements

24

Catalyst Optimization

• Optimization aims to accelerate the

development 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 studied25

26

Combinatorial Cycle

Rapid Synthesis

Parallel Screening

Data Minimizationand Analysis

Hypothesis Generation

0 10 20 30 40 50 60 70

0

10

20

30

40

50

60

70

Model P

redic

ted N

Ox s

tora

ge

Experimental NOx storage

Design Points

Validation Points

Hattrick-Simpers, J. R., Wen, C., and Lauterbach, J. “The Materials Super Highway: Integrating High-Throughput Experimentation into Mapping the Catalysis Materials Genome” Catalysis Letters145, no. 1 (2014): 290–298.

HT Synthesis

• Thin-film techniques

– Continuous composition spreads: co-

sputtering and co-evaporation.

• Solution-based methods

• Challenge is to create samples with the

same properties when prepared under

identical 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

31

Expose to JP-8

Darkening Carbon coating Activity

Optical measurements

Al2O3 SiO2

Substrate

Sample Synthesis Bulk Support Verification

-Al2O3

Nb2O5

Inkjet printing of catalysts

32

Xiang et al.,

ACS Comb Sci, 2014

Ink-jet printing assisted cooperative-

assembly method (IJP-A)

33

Gregoire et al.,

Journal of The Electrochemical

Society, 160 (4) F337-F342 (2013)

(Fe-Co-Ni-Ti)Ox pseudo-

quaternary catalyst library

Metal precursor formulations

contain block copolymer structure

directing agents

Drying/precipitation and

calcination protocol to yield

porous metal oxide thin-film

samples

Solution-based “Bulk” Methods

• Solution methods are fairly complex and sensitive 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

36

Bicontinuous phase

100% Water 100% Oil

Two Phase

Two Phase

Oil

Microemulsion Phase Diagram Reverse Micelles as “Nanoreactors”

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

Reverse

MicellesMicelles

Water

t][Surfactan

O][Hω 2

2 4 6 8 10 12 14 16 18 20

6

8

10

12

14

16

18

20

Re

ve

rse

Mic

elle

Dia

me

ter

(nm

)

J. Hoefelmeyer, H. Liu, G. Somorjai, T. Tilley, J. Colloid and Interface Science 309 (2007) 86

Ru+3

N2H4

Ru+3

Ru+3

Ru

Ru

Ru

Ru

Ru

Ru

Support

Wash with acetone to

break reverse micelle

Nanoclusters settle

onto support

+

Powder

-Al2O3

Ru

Ru

Ru

Ru

Ru

Ru

Effect of ω on Ru particle size

37

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)

propanol]-2 [TritonX

O][Hω 2

Dynamic Light

Scattering (DLS)

used to determine

reverse micelle size

ω=1.0

ω=1.1

ω=1.2

ω=1.3

Parallel Microwave Synthesis

• Hydrothermal synthesis of

zeolites

• 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, an

appropriate conventional technique using larger sample

sizes or other required parameters should be chosen as

reference

41

Parallel Reactors

42

Microfluidics• Involves the handling of fluids in

devices containing channels in the

micrometer-size regime.

• Parallel microfluidic reactor

system, which consists of a

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 to

acetic acid

– Oxidative dehydrogenation of ethane

to ethylene

– Selective ammoxidation of propane to

acrylonitrile 43

US6902934B1

HTE Wheel reactor

44

High Throughput ReactorSimultaneous testing of 16 powder catalysts

RJ Hendershot, et al. Applied Catalysis A, 2003 45

Reactor conditions

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

2. Atmospheric pressure

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

47

Monolith HT

Reactor System

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

(2007)

0 1 2 3 4 5 6 7266.5

266.6

266.7

266.8

266.9

267.0

267.1

267.2

267.3

Te

mp

era

ture

(oC

)

Reactor Number

0.7oC

K Type

Thermocouple

Heating tape

for inlet gas

preheating

Capillaries for

consistent flow

profile

1000 1020 1040 1060 1080 1100400

410

420

430

440

450

Te

mp

era

ture

(oC

)

Time (sec)

Reactor 1 Reactor 2 Reactor 3 Reactor 4

18 mm

Reactor Setup

Parallel flow rates and feed gas

compositions

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0

20

40

60

80

100

120

140

160

HTR outlet flow rates 2/19/2014

Flo

w r

ate

(ccm

)

Channel #

set point = 125 ccm

Parallel temperature control

48

Liquid/vapor reactions

• 16-channel condenser fabrication• Removal of heavier hydrocarbons

from reactor effluent• IR imaging and GC-MS analysis

• Quantitative gas product measurement

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

51Orschel, et al.,

Angew. Chem. Int. Ed 38 (1999) 2791

52

Catalysts 2016, 6(2), 23

53

• 20% CH4+ 80 %N2

• 6 bit, 63 elements

• 1 min interval between injections

[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]

Pseudo random injection

sequence

Single chromatogram Convoluted chromatogram

Apply Hadamard

transformation to

deconvolute

To increase throughput :

1)Long binary pseudo-random sequences

2)Short injection intervals

3)Stable and reproducible sample injections onto the separation column

High-throughput Gas Chromatography (HT-GC)

Validation of HT-GC via Ethylene Epoxidation

1 Blank

2

USC-10067 12wt% Ag,

350ppmCs,150ppmRe

3 Cu-(Au-Ag) 500 ppm Au

4 Cu-(Re-Ag) 25 ppm Re

5 (Cu-Au)-Ag 500 ppm Au

6 (Cu-Re)-Ag 25 ppm Re

7 (Cu-Cs)-Ag 50 ppm Cs

8 Cu-(Re-Ag) 125 ppm Re

9 Cu-(Sn-Ag) 50 ppm Sn

10

USC-OA5 14 wt% Ag,

200 ppm Sn

11 Cu-(Sn-Ag) 450 ppm Sn

12 1%Cu-Ag

13 0.2%Cu-Ag

14 250 ppm Cu-(Sn-Ag)

• 13 catalysts

• 10%C2H4, 10%O2 at 250C

Fast-sequential and Simultaneous Gas Analysis

0

0.05

0.1

0.15

1 2 3 4 5 6 7 8 9 1011121314Concentr

atio

n

Reactor Channel

Ethylene Concentration

Single Run HT GC HT FTIR

• Accurate measurements can be made using HT-GC

• Results are comparable with the FTIR measurements

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

1 2 3 4 5 6 7 8 9 1011121314

Co

nce

ntr

atio

nReactor Channel

ETO Concentration

Single Runs HT GC HT FTIR

Comparison of most active catalysts for ethylene

epoxidation

0

10

20

30

40

50

60

2 10 13

Ety

lene C

onvers

ion

Reactor #

Single Run

HT GC

HT FTIR

• In comparison to single GC

measurements, HT-GC

decreases analysis time by 3.2x

• Analysis time can be decreased

by 5x using the current setup (5h

vs 1h)

• Possibilities for kinetic

measurements.

Parallel Screening Approaches

• 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 at

temperatures up to 650oC

– Cannot differentiate between desired and

undesired reactions

– Detect activity, but not selectivity

Screening time ~ number of samples in field of view

From Maier group web page

58

Radiation from

Spectrometer

A

B

C

A

B

C

Chemically Sensitive, Parallel Screening

Throughput ~ number of samples in the field of view59

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

• 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

– Acquire many spectra simultaneously from over a

region of a sample

4mm

4m

m

4mm

4m

m

MCT

SiliconIndium

“bumps”

MCT

SiliconIndium

“bumps”

61

Spatial, x

Sp

ati

al,

y

Hyperspectral

Data Cube

FTIR Imaging

FTIR

SpectrometerSample

128 x 128

MCT

FPA

Wavenumber (cm-1)

Ab

so

rba

nc

e62

Snively, C.M. and J. Lauterbach Applied Spectroscopy 59(2005)

Snively, C.M., S. Katzenberger, G. Oskarsdottir, and J. Lauterbach Optics Letters 24 (1999)

Resin-Supported Libraries• Solution phase combinatorial organic

synthesis

• Reactions carried out on ligands supported

on polymer beads

• Final products are attached to beads or in

solution

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

0 400 800 1200 1600 20000.0

0.5

1.0

1.5

2.0

2.5

3.0

Norm

aliz

ed P

eak A

rea

Time (s)

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

Dry beads placed

in a 50mm flow cell

Introduce solvent

Introduce reactants

Acquire data

during reaction

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

Parallel Transmission Gas Analysis

• IR gas-phase spectroscopy – Works for any gas with IR signature

• Chemometrics needed for complex spectra

FPA

• 16-element gas-phase array

– Analyze all 16 product streams

in parallel in < 2 sec

Snively, C.M. and J. Lauterbach Applied Spectroscopy 59(2005)

Snively, C.M., S. Katzenberger, G. Oskarsdottir, and J. Lauterbach Optics Letters 24 (1999)

128pixels

128pixels

Spatially resolved IR spectra

128 x 128 = 16,384 detectors

2000 1800 1600

0.00

0.04

0.08

0.12

0.16

Ab

so

rba

nc

e [

A.U

.]Wavenumbers [cm

-1]

NOC2H4H2O

H2O

NO

C2H4

NO

C2H

4

H2O

NO + C2H

4 + H

2O

65

Transient IR Data From One Channel

Switch from fuel

rich to fuel lean;

Introduce oxygen66

Multi-channel Transient Analysis Capacity

0 6 12 18 24 30 36 42 48 54 60

150

175

200

225

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275

300

325

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375

400

425

450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

175

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275

300

325

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375

400

425

450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

175

200

225

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275

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325

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375

400

425

450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

175

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425

450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

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450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

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T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

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T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

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450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

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450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

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425

450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

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325

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375

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450

T [C

]

time [minute]

0 6 12 18 24 30 36 42 48 54 60

150

175

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225

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275

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325

350

375

400

425

450

T [C

]

time [minute]

Reaction progress with time

67

5% Rh/Al2O3 T = 650oC

CH4

Hydrocarbons

69

70

71

James S. Cawse, Experimental Design for

Combinatorial and High Throughput Materials Development, p. 2

Wiley 2003

“A poorly designed […high-throughput…]

experiment will give us bad information

with unprecedented speed

and in outstanding quantities”

Computational Techniques

• “A major bottleneck in high-throughput

techniques is … is often data management and

data analysis.”

• The development of the computational methods

has progressed rapidly

• Many laboratories are hesitant to use these

methods because of a lack of manpower,

complexity, and lack of access to established

software.”

73Maier et al., Angew. Chem. Int. Ed. 2007, 46, 6016 – 6067

# Variables 6 or more 3 - 6 2 - 6

Type of Crude Quantitative Empirical modelsInformation Information estimates of effects

Typical Rank variables Understand system Develop high quality

Objectives in order of behavior, including predictions, property importance interactions optimization

SCREENING

DESIGN

INTERACTION

DESIGN

RESPONSE SURFACEDESIGN

Progression of Experimentation In Practice

Design of Experiments

74

Maximize experimental efficiency

Quantify main effects and interactions: empirical models

Screening Design

X3

X2X1

Linear effects

Y: performance criterion

xi: parameters

i: coefficients fitted to experiments75

Response Surface

LINEAR

DESIGNS

RESPONSE SURFACEDESIGNS

......... 2

22

2

1121122211 XXXXXXCY

......... 32112331132112332211 XXXXXXXXXXCY

X3

X2

X1

77).....*()*(......)()()......()()( 21

2

2

2

11221 CoPtRhPtRhPtCoRhPtCR

0 1Pt (wt%)

5

Co(wt%)

0

1Pt (wt%)

5

(wt%)

0

Full Central Composite Design (5 factors) Reaction conditions

NO = 0.15%

O2 = 6% or 0%

CO = 0.9 %

C2H4=0.15%

Data points tested for design= 45

Validation points =21

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

Work funded by National Science Foundation

7878

Term 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 -14.38

T*T -24.06

Pt*Ba 13.19

Pt*T -7.12

Rh*Ba 9.54

0 20 40 60 80 100

0

10

20

30

40

50

60

70

80

90

100

Model P

redic

ted N

Ox s

tora

ge [10

-6 m

ole

s N

Ox]

Experimental NOx Storage [10

-6 moles NO

x]

Model development points

Validation points

Model Prediction vs. Experimental Values

Optimum Catalyst Composition 1.4Pt/0.9Rh/4Co/23Ba

All catalyst loadings are actual loadings

79790 3 6 9 12 15 18 21 24 27 30

0

200

400

600

800

1000

1200

1400

1600

NO

x c

on

cn

. [p

pm

]

Time [min]

1Pt/15Ba

1Pt/5Co/15Ba

1.4Pt/0.9Rh/4Co/34Ba

1.4Pt/0.9Rh/4Co/23BaT=375C

Switch from

rich to lean

Optimum Catalyst Testing for NOx Storage

Total cycle time = 45 min

Lean phase = 30 min (1800 sec)

Rich phase = 15 min (900 sec)

Reaction conditions

NO = 0.15%

O2 = 6% or 0%

CO = 0.9 %

T= 375ºC

C2H4=0.15%

Stores NOx for ~ 15 minutes

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 for

solving 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

82

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.

Inverse Model

Genetic Algorithms

SelectionRecombination

Catalyst Library

HTE

Target Catalyst

Model

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Performance

Catalyst Design Framework

Target Catalyst Performance

XYZ

Rate

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ctivity

Forward Model

Hybrid Model

Physical Model

Statistics/Neural-NetsA + S A-S

k1

C + R Dk2

A-S +Dk3

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Kinetics AI/Systems Tools Catalyst Performance

F

Pseudo Global Optimizer

Pseudo English

Rule Compiler

Statistical Analyzer

Feature Extractor

Catalyst

{k}

Reaction Modeling Suite

J. M. Caruthers, J. A. Lauterbach, K. T. Thomson, V.

Venkatasubramanian, C. M. Snively, A. Bhan, S. Katare, G.

Oskarsdottir, J. Catal. 2003, 216, 98 – 109.

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 and

comparability of measured data

• Allows the study of correlations and trends in

ways not possible by any conventional one-at-a-

time experimentation

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Advantages

• Reduced development times for new and

optimized materials

• Reduced time to market

• Rapid sampling of large parameter spaces

• Rapid collection of comparable data

• Acceleration of basic research

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Problems

• HT generates good data rapidly, but it also

generates false positives and negatives very well

– How many HT attempts had to be abandoned because

of the lack of agreement with data from conventional

experiments?

• It is crucial to verify the quality of the HT data with

those obtained from conventional tests

• Time and $$$ for development of suitable

screening techniques

– Universality of the techniques

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Some Suggested Reading

• J. Hattrick-Simpers, C. Wen and J. Lauterbach, “The

Materials Super Highway: Integrating High-

Throughput Experimentation into Mapping the

Catalysis Materials Genome”, Catalysis Letters, 145

(1), 290-298, 2015

• E. Sasmaz, K. Mingle, and J. Lauterbach, “High-

Throughput 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) 53:2–12

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