development of new materials by combinatorial techniques

International Conference on Engineering Education July 21–25, 2003, Valencia, Spain.1

Development of New Materials by Combinatorial Techniques

Authors:

José M. Serra, Instituto de Tecnología Química, UPV-CSIC, Valencia, 46022, Spain [email protected] Corma, Instituto de Tecnología Química, UPV-CSIC, Valencia, 46022, Spain [email protected] Argente, Departamento de Sistemas y Computación, UPV, Valencia, 46022, Spain, [email protected] Valero, Departamento de Sistemas y Computación, UPV, Valencia, 46022, Spain, [email protected] Botti, Departamento de Sistemas y Computación, UPV, Valencia, 46022, Spain, [email protected]

Abstract This paper shows an example of integration of the different techniques involved in the combinatorialdevelopment of new catalytic materials, showing the joint effort of different scientist and engineers in combined projectsbetween academia and industry. The application of different engineering fields in the discovery an development of newmaterials, specially of new catalyst, is changing the conventional research methodology in materials science. Indeed, theintegration of robotics, computer science, electronics, mechanical and chemical engineering in a research laboratory isincreasing significantly the processing throughput and the degree of automation, generating new automated high speedtechniques for the experimental work. It is described the development of new experimental tools (robotics systems formaterial syntehsis and testing equiment) and new software tools for design of experiments and data analysis/minig.

Index Terms Combinatorial Catalysis, Hight-Throughput Experimentation, Materials Science, Catalysts, Automation,Robotics, Chemistry Laboratory

INTRODUCTION

The application of different engineering fields in the discovery an development of new materials, specially of new catalyst, ischanging the conventional research methodology. Indeed, the integration of robotics, computer science, electronics,mechanical and chemical engineering in the research laboratory is increasing significantly the processing throughput and thedegree of automation, generating new automated high speed techniques for the experimental tasks.

Combinatorial catalysis [1-6] is a methodology where a large number of new materials are prepared and tested in aparallel fashion. The global search/optimisation strategy is the main difference with the traditional catalyst research andshould allow to reduce the number of experiments needed to find an optimal catalyst composition. Combinatorial catalysisinvolves the co-ordination of (see Figure 1): high-throughput systems [8-10] for preparation, characterisation and catalytictest; large information data management; and rapid optimisation techniques. This promising approach requires therefore thedevelopment and optimisation of the following items: (i) high-throughput equipment, which allows the reliable preparationand characterisation/testing preferentially under realistic conditions of larger quantities of materials (ii) optimisationtechniques, adapting their structure and parameters by implementing the chemical knowledge/experience of the experts. Withthis, it would be possible to increase the number of variables to study and this would result in a potentially rather morepowerful final catalyst and shorter search times. Indeed, if this methodology is properly followed it can be very helpful in thescientific understanding of catalysis.

The drugs development has known a drastic and successful change in the ’90 by means of fast synthesis and screeningof large libraries of diverse formulations on fully automated working stations and analytics. The so-called combinatorialapproach, rapidly extended to other research domains (see Figure 2) such as materials science and catalysis, relies on thesystemactic screening of the population surface by combining all relevant parameters. Thereby, the investigation strategyshifts from essentially qualitative to highly quantitative studies with data throughput increased by orders of magnitude.

The automated screening of large libraries of catalysts is today entirely possible thanks to fast-growing technologies forautomation, miniaturization and computation. The figure 3 shows the new advanced technologies and the tool available forthe application in the combinatorial approach. Fully automated robots specially designed for fast synthesis and testing ofcatalyst are now available. However, the high complexity of catalytic systems makes data management (instrument andsoftware integration, data base construction, statistical studies and data mining functions) a challenge.


RESEARCH LINES

The everyday running of a combinatorial laboratory requires the interdisciplinary work of different technicians, engineers andresearchers, since the essence of this methodology is the combination of all different techniques (software, automation,robotics, process eng., chemistry). The first issue is the development and setting up of the accelerated tools. For example, forthe development of new chemical reactors for the simultaneous test of different catalyst, it is necessary the common work ofmechanical, chemical and electronics engineers during a prolonged time (even years). The figure 4 shows two different HTEreactors designed, constructed and set up in our laboratory. The first reactor has been employed in the search of new catalystfor different processes in oil refining and petrochemistry.

It is as well necessary the same interdisciplinary effort for the development and setting up of new synthesis roboticsystems. The automation engineers have to work in close cooperation with the chemists in order to reproduce as close aspossible the convetional synthesis procedure and jointly evaluate the influence of the operative modification of suchprocedure. The figure 5 shows a picture of different synthesis robots developed in our laboratory. The equipment shown infigure 5a can synthetize new materials under hydrothermal conditions, especially suited for the synthesis of zeolites andmesoporuous materials. The robotic system of figure 5b is employed for catalyst preparation by impregnation or ionexchange with a series of active components.

In the frame of combinatorial catalysis, data management is referred to software techniques for (i) the efficientadministration and schedule of large amounts of experimental data, (ii) the comprehension and modelling of the organiseddata and (iii) the global search strategy to optimise the catalytic performance. Traditionally, the processing and understandingof the experimental outputs (characterisation and catalytic performances) was accomplished by the researchers, who appliedprevious experiences or fundamental knowledge in order to carry out the experimental design and to establish relationshipsbetween the different experimental results. In the case of combinatorial catalysis, the large number of variables in play andthe application of complex optimisation algorithms for the experimental design makes difficult the direct humaninterpretation of data derived from high throughput experimentation. Recently, data mining techniques have been applied[11-12] in order to find relationships and patterns between the input and output data derived from acceleratedexperimentation. Hence, artificial intelligence (AI) techniques have an important potential for modelling and prediction ofcomplex high-dimensional data. Among these techniques, artificial neural networks (NN) could be useful in the chemicalfield. Artificial neural networks have been mainly used in classification problems, in character recognition, in negotiationproblems, in information processing, in control and automation, in prediction problems. Artificial neural networks havesuccessfully been applied to conventional catalytic modelling and design of solid catalysts. Those applications [13] include:design of ammoxidation of propylene catalyst [14], design of methane oxidative decoupling catalyst [15], analysis andprediction of results of the composition of NOx over zeolites [16].

In our research group we have worked on the development of artificial intelligence (AI) techniques for design ofexperiments and for data mining and prediction. One example of the first time of techniques is the application of geneticalgorithms to the design of series of catalysts and optimisation of their catalytic performance. One application of suchalgorithms developed by our group for the development of enhanced catalyst for oil refining process[5]. An adapted geneticalgorithm was applied to the search of new solid bifunctional catalysts. The problem was initially focused by means of aknowledge-based catalyst formulation, in which each component (metal oxide, acidity promoters and metallic promoters) isselected and incorporated to the catalyst formulation in view of its expected catalytic effect. Catalytic evaluation was carriedout by means of a 16 parallel fixed bed reactor system (equipment shown if figure 4a), under 30 bar and 200-240ºC. Thecalytic performance of the best ranked catalyst of each generation are displayed in figure 6. Three evolving cycles have beenrun and an important improvement in the catalyst activity has been found.

The other AI techniques developed in our group are neural networks, i.e., two application of NNs to the prediction ofcatalytic performance: (i) ANN catalyst compositional models, correlating composition and synthesis variables with catalyticperformance and (ii) ANN kinetic models, correlating reaction conditions with catalytic performance. The first reportedapplications include the design of solid catalyst for different reactions of interest, and the integration of ANNs techniqueswith evolutionary strategies in material discovery, allowing the analysis and prediction of catalytic results within a populationof catalysts produced by combinatorial techniques[17]. In figure 7 the prediction performance of a NN model for the reactionof oxidative dehydrogenation of ethane is displayed. This prediction capability suggested us that this NN model can serve astheorical (in silico) pre-screening of different catalyst enabling to save experimental work. The last application [18] isreferred to modelling experimental kinetic data in order to obtain rapidly black box models of the behaviour of a catalyticreactor. These ANN kinetic models could be promptly obtained for a series of catalysts and rapidly determine which are thereaction conditions for optimal catalytic performance of each material. In addition, those models can be applied for furthercatalyst scale up and, process control and optimisation.


We have described an example of integration of the different techniques involved in the combinatorial development ofnew catalytic materials in our research laboratory in the Polytechnic University of Valencia, showing the joint effort ofdifferent scientist and engineers in combined projects between academia and industry. The training of engineering andchemistry students in the different research lines has offered them an interesting view of interdisciplinary teamwork.

ACKNOWLEDGEMENT

Financial Support by the European Commission (GROWTH Contract GRRD-CT 1999-00022) is gratefully acknowledged.

REFERENCES

[1] Senkan, S.,” Combinatorial heterogeneous catalysis-a new path in an old field”, Angewandte Chemie Int. Ed., 40, 2001 312-329

[2] Senkan, S., “High-throughput screening of solid-state catalyst libraries”, Nature, 394, 1998, 350-353

[3] Cong, P. High-throughput synthesis and screening of combinatorial heterogeneous catalyst libraries “, Angewandte Chemie Int. Ed., 38, 1999, 483-488

[4] Jandeleit, B., “Putting catalysis on the fast track”, Chemistry & Industry, 1998, 795-798

[5] Serra, J.M., Chica, A., Corma, A.., “Development of a low temperature light paraffin isomerization catalysts with improved resistance to water andsulphur by combinatorial methods”, App. Catal. A: General, 239, 2003, 35-42

[6] Rodemerck, U., Wolf, D., Buyevskaya, O.V., Claus, P., Senkan, S. et al., ”High-throughput synthesis and screening of catalytic materials. Case studyon the search for a low-temperature catalyst for the oxidation of low-concentration propane”, Chem. Eng J, 82, 2001, 3-11

[7] Cong, P., Guan, S., McFarland, E.and Weinberg, H., US Patent 5959297, 1999

[8] Senkan, S., World Patent 00/29844, 2000

[9] Corma, A., Serra, J.M., Hernández, J. ,World Patent 01/59463, 2001

[10] Hoffmann, C.; Schmidt, H., F. “Multipurpose Parallelized 49-Channel Reactor for the Screening of Catalysts: Methane Oxidation as the ExampleReaction”J. Cat., 198, 2001, 348-354

[11] Wang, K., Wang, L. , Yuan, Q. , Luo, S., Yao, J. , Yuan, S., Zheng, C.,Brandt, J., “Construction of a generic reaction knowledge base by reaction datamining”, Mol. Graph. Model., 19(5), (2001) 427-433

[12] Rajan ,K., Zaki, M., Bennett, K., “Searching techniques for structure-property relationships in materials”, Abstr. Pap.-Am. Chem. Soc., (2001) 221st

[13] Hattori, T., Kito, T., “Neural network as a tool for catalyst development”, Cat. Today, 23, 1995, 347

[14] Hou, Z., Dai, Q., Wu, X. Chen, G., “Artificial neural network aided design of catalyst for propane ammoxidation”, App. Catal. A: General, 161, 2000,183

[15] Huang, K., Chen, F., Lu, D., “Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling”, App. Catal. A:General, 219, 2001, 61-68

[16] Sasaki, M., Hamada, H., Kintaichi, Y., Ito, T., “Application of a neural network to the analysis of catalytic reactions Analysis of NO decompositionover Cu/ZSM-5 zeolite”, App. Catal. A: General, 132, No 2, 1995, 261-270

[17] Corma, A., Serra, J.M., Argente, E., Valero, S., Botti, V., “Application of artificial neural networks to combinatorial catalysis: Modelling andprediction of ODHE catalysts”, ChemPhysChem, 3, No 11, 2002, 939-945.

[18] Serra, J.M., Corma, A., Chica, A., Argente, E., Botti, V., “Can artificial neural networks help the experimentation in catalysis?“, Cat. Today, 2003, inpress, 2003


FIGURES AND TABLES

FIGURE 1CONCEPTUAL SCHEME OF THE COMBINATORIAL APPROACH IN THE DEVELOPMENT OF NEW CATALYSTS.

FIGURE 2APPLICATION OF HIGHTROUGHPUT TECHNIQUES TO DIFFERENT CHEMISTRY FIELDS

FIGURE 3DRIVING TECHNOLOGIES OF HIGHTHROUGHPUT EXPERIMENTATION / COMBINATORIAL APPROACH

Fast Libraysynthesis

HighThroughput

Testing

Preparation Characterisation &Reaction

Data-base &Conclusions

Search Strategy

DataManagement

Microelectronics

Micromechanics

Robotics

Computing

Optics

Automation

� Powerful computers

� New sensor and detector

� Miniaturised devices (MEMS)

� New methods for automated synthesis

� New methods for materials testing

� Experimental data analysis (IA)

� Design of experiments (IA)

� Simulation and Prediction of exp. data

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CChheemmii ss tt rr yyPPoo ll yymmee rr ss BBaa ss ee

CChheemmii ss tt rr yyOO ii ll

RRee ff ii nn ii nngg

1970s �� 1980s �� 1990s �� 1999 �� 2000 �� 2001 �� 2005


FIGURE 4HIGHTHROUGHPUT SYSTEMS FOR CATALYTIC CESTING: (A) 16-REACTOR RIG FOR TESTING UNDER INDUSTRIAL IEACTION CONDITIONS AND (B) 36-REACTORSYSTEM ABLE TO WORK SEQUENTIALLY OR BY PULSES.

(A) (B)

FIGURE 5HIGHTHROUGHPUT SYSTEMS FOR SOLID MATERIAL SYNTHESIS: (A ) ROBOT FOR HYDROTHERMAL SYNTHESIS AND (B) ROBOT FOR CATALYSTS

PREPARATION BY IMPREGNATION/ION EXCHANGE .

(A) (B)


FIGURE 6GENETIC ALGORITHM APPLIED TO ISOMERISATION OF LIGHT PARAFING FOR GASOLINE PRODUCTION: BEST CATALYSTS FOR THE SUCCEEDED GENERATION.

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3rd

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FIGURE 7EXPERIMENTAL AND PREDICTED RESULTS OF O2 CONVERSION OF A NN MODEL FOR THE MEACTION OF OXIDATIVE DEHYDROGENATION OF ETHANE.(TRAINING DATA 40 SAMPLES AND TESTING DATA 10 SAMPLES)

0

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Experimental results Predictions Confidence Interval

development of new materials by combinatorial techniques

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