Development of New Materials by Combinatorial Techniques - iNEER

Development of New Materials by Combinatorial Techniques Authors: José M. Serra, Instituto de Tecnología Química, UPV-CSIC, Valencia, 46022, Spain [email protected] Avelino Corma, Instituto de Tecnología Química, UPV-CSIC, Valencia, 46022, Spain [email protected] Estefania Argente, Departamento de Sistemas y Computación, UPV, Valencia, 46022, Spain, [email protected] Soledad Valero, Departamento de Sistemas y Computación, UPV, Valencia, 46022, Spain, [email protected] Vicente 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 combinatorial development of new catalytic materials, showing the joint effort of different scientist and engineers in combined projects between academia and industry. The application of different engineering fields in the discovery an development of new materials, specially of new catalyst, is changing the conventional research methodology in materials science. Indeed, the integration of robotics, computer science, electronics, mechanical and chemical engineering in a research laboratory is increasing significantly the processing throughput and the degree of automation, generating new automated high speed techniques for the experimental work. It is described the development of new experimental tools (robotics systems for material 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, is changing 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 the degree 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 a parallel fashion. The global search/optimisation strategy is the main difference with the traditional catalyst research and should allow to reduce the number of experiments needed to find an optimal catalyst composition. Combinatorial catalysis involves the co-ordination of (see Figure 1): high-throughput systems [8-10] for preparation, characterisation and catalytic test; large information data management; and rapid optimisation techniques. This promising approach requires therefore the development and optimisation of the following items: (i) high-throughput equipment, which allows the reliable preparation and characterisation/testing preferentially under realistic conditions of larger quantities of materials (ii) optimisation techniques, adapting their structure and parameters by implementing the chemical knowledge/experience of the experts. With this, it would be possible to increase the number of variables to study and this would result in a potentially rather more powerful final catalyst and shorter search times. Indeed, if this methodology is properly followed it can be very helpful in the scientific understanding of catalysis. The drugs development has known a drastic and successful change in the ’90 by means of fast synthesis and screening of large libraries of diverse formulations on fully automated working stations and analytics. The so-called combinatorial approach, rapidly extended to other research domains (see Figure 2) such as materials science and catalysis, relies on the systemactic screening of the population surface by combining all relevant parameters. Thereby, the investigation strategy shifts 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 for automation, miniaturization and computation. The figure 3 shows the new advanced technologies and the tool available for the application in the combinatorial approach. Fully automated robots specially designed for fast synthesis and testing of catalyst are now available. However, the high complexity of catalytic systems makes data management (instrument and software integration, data base construction, statistical studies and data mining functions) a challenge.

International Conference on Engineering Education

July 21–25, 2003, Valencia, Spain. 1

RESEARCH LINES The everyday running of a combinatorial laboratory requires the interdisciplinary work of different technicians, engineers and researchers, 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, for the development of new chemical reactors for the simultaneous test of different catalyst, it is necessary the common work of mechanical, chemical and electronics engineers during a prolonged time (even years). The figure 4 shows two different HTE reactors designed, constructed and set up in our laboratory. The first reactor has been employed in the search of new catalyst for 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 robotic systems. The automation engineers have to work in close cooperation with the chemists in order to reproduce as close as possible the convetional synthesis procedure and jointly evaluate the influence of the operative modification of such procedure. The figure 5 shows a picture of different synthesis robots developed in our laboratory. The equipment shown in figure 5a can synthetize new materials under hydrothermal conditions, especially suited for the synthesis of zeolites and mesoporuous materials. The robotic system of figure 5b is employed for catalyst preparation by impregnation or ion exchange with a series of active components. In the frame of combinatorial catalysis, data management is referred to software techniques for (i) the efficient administration and schedule of large amounts of experimental data, (ii) the comprehension and modelling of the organised data and (iii) the global search strategy to optimise the catalytic performance. Traditionally, the processing and understanding of the experimental outputs (characterisation and catalytic performances) was accomplished by the researchers, who applied previous experiences or fundamental knowledge in order to carry out the experimental design and to establish relationships between the different experimental results. In the case of combinatorial catalysis, the large number of variables in play and the application of complex optimisation algorithms for the experimental design makes difficult the direct human interpretation 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 accelerated experimentation. Hence, artificial intelligence (AI) techniques have an important potential for modelling and prediction of complex high-dimensional data. Among these techniques, artificial neural networks (NN) could be useful in the chemical field. Artificial neural networks have been mainly used in classification problems, in character recognition, in negotiation problems, in information processing, in control and automation, in prediction problems. Artificial neural networks have successfully 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 and prediction 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 of experiments and for data mining and prediction. One example of the first time of techniques is the application of genetic algorithms to the design of series of catalysts and optimisation of their catalytic performance. One application of such algorithms developed by our group for the development of enhanced catalyst for oil refining process[5]. An adapted genetic algorithm was applied to the search of new solid bifunctional catalysts. The problem was initially focused by means of a knowledge-based catalyst formulation, in which each component (metal oxide, acidity promoters and metallic promoters) is selected and incorporated to the catalyst formulation in view of its expected catalytic effect. Catalytic evaluation was carried out by means of a 16 parallel fixed bed reactor system (equipment shown if figure 4a), under 30 bar and 200-240ºC. The calytic performance of the best ranked catalyst of each generation are displayed in figure 6. Three evolving cycles have been run 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 of catalytic performance: (i) ANN catalyst compositional models, correlating composition and synthesis variables with catalytic performance and (ii) ANN kinetic models, correlating reaction conditions with catalytic performance. The first reported applications include the design of solid catalyst for different reactions of interest, and the integration of ANNs techniques with evolutionary strategies in material discovery, allowing the analysis and prediction of catalytic results within a population of catalysts produced by combinatorial techniques[17]. In figure 7 the prediction performance of a NN model for the reaction of oxidative dehydrogenation of ethane is displayed. This prediction capability suggested us that this NN model can serve as theorical (in silico) pre-screening of different catalyst enabling to save experimental work. The last application [18] is referred to modelling experimental kinetic data in order to obtain rapidly black box models of the behaviour of a catalytic reactor. These ANN kinetic models could be promptly obtained for a series of catalysts and rapidly determine which are the reaction conditions for optimal catalytic performance of each material. In addition, those models can be applied for further catalyst scale up and, process control and optimisation.

International Conference on Engineering Education

July 21–25, 2003, Valencia, Spain. 2

We have described an example of integration of the different techniques involved in the combinatorial development of new catalytic materials in our research laboratory in the Polytechnic University of Valencia, showing the joint effort of different scientist and engineers in combined projects between academia and industry. The training of engineering and chemistry 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 and sulphur 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 study on 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 Example Reaction”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 data mining”, 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 decomposition over 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 and prediction 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, in press, 2003

International Conference on Engineering Education

July 21–25, 2003, Valencia, Spain. 3

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

Preparation

Characterisation & Reaction

Data-base & Conclusions

High Throughput Testing

Fast Libray synthesis

Data Management

Search Strategy

FIGURE 2 APPLICATION OF HIGHTROUGHPUT TECHNIQUES TO DIFFERENT CHEMISTRY FIELDS

Pharmacy

1970s

Fine Chemistry


1980s
1990s

Polymers




Base Chemistry


2000
2001

1999

Oil Refining


2005

FIGURE 3 DRIVING TECHNOLOGIES OF HIGHTHROUGHPUT EXPERIMENTATION / COMBINATORIAL APPROACH

Microelectronics Micromechanics


Powerful computers
New sensor and detector
Miniaturised devices (MEMS)

Robotics Automation


New methods for automated synthesis
New methods for materials testing
Experimental data analysis (IA)

Computing

Optics


Design of experiments (IA)
Simulation and Prediction of exp. data

International Conference on Engineering Education

July 21–25, 2003, Valencia, Spain. 4

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

(B)

FIGURE 5 HIGHTHROUGHPUT S YSTEMS FOR S OLID M ATERIAL SYNTHESIS: (A ) ROBOT FOR H YDROTHERMAL S YNTHESIS AND (B ) ROBOT FOR CATALYSTS PREPARATION BY IMPREGNATION/ION EXCHANGE . ( A)

(B)

International Conference on Engineering Education

July 21–25, 2003, Valencia, Spain. 5

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

Relative Activity %

100

75

50

25

0 1

2

3

4

5

3rd 6

7

8

1st Generation 9

10

FIGURE 7 EXPERIMENTAL AND PREDICTED RESULTS OF O2 CONVERSION OF A NN (TRAINING DATA 40 SAMPLES AND TESTING DATA 10 SAMPLES)

MODEL FOR THE MEACTION OF OXIDATIVE DEHYDROGENATION OF ETHANE.

O2 Conversion %

100

75 50 25 0

1

2

3

4

5

6

7

8

9

10

Samples

Experimental results

Predictions

International Conference on Engineering Education

Confidence Interval

July 21–25, 2003, Valencia, Spain. 6