Combinatorial Chemistry & High Throughput Screening - Volume 10, Issue 2, 2007

These special issues (Vol. 10, No. 1 and Vol. 10, No. 2) are devoted to progress in the area of Combinatorial Heterogeneous Catalysis (CHC), which can be considered to be the general application of combinatorial and high-throughput methods and strategies in materials research. Heterogeneous catalysis plays a vital role if our everyday lives. All pollution-free and environmentally friendly industrial processes in oil refineries, chemical plants, and power energy stations need highly active and selective catalysts. Catalysts are used in cars and trucks to remove harmful products of incomplete oxidation of gasoline or diesel fuel. The plastics used in everyday life are prepared by applying very specific highly active and selective catalysts, and this list can be continued. Better catalysts reduce productions costs, reduce the formation of wasteful by-products, and help to decrease atmospheric pollution. These are the main reasons that the search for new and better catalysts is a permanent R & D task. In general, combinatorial approaches are intended to find the optimum formulation in various pharmaceutical or engineering materials including catalysts, with the shortest interval and minimum amount of unit cost. In this case the focus is laid on the optimum performance with decreased overall costs. All combinatorial approaches are based on the diversity of the system investigated. This diversity determines the parameter space where the optimum performance may be found. In combinatorial materials research, including CHC, the following key components of diversity can be distinguished: (i) compositional; (ii) process; and (iii) structural. The structural diversity is determined by the first two components. In order to move in a large experimental space towards the optimum performance, the following requirements must be fulfilled: (i) high throughput methods in synthesis, testing, and analysis; (ii) highly reliable and reproducible analytical methods; (iii) effective optimization and information mining tools; and (iv) appropriate data handling and management methods. Today, combinatorial heterogeneous catalysis is a well-acknowledged area of catalysis sciences, although a definite part of our scientific community still remains quite critical. Methods of combinatorial catalysis are widely used both in applied and academic laboratories. Symyx Technologies was the first company devoted to the area of combinatorial heterogeneous catalysis. Among the followers are the Dutch company Avantium Technologies, the German group “hte GmbH”, etc. In the last 10 years, most of the large oil and chemical companies created their own laboratories in combinatorial catalysis. In academia, various combinatorial heterogeneous laboratories have emerged in various countries, such as Australia, Japan, Korea, Singapore, Germany, the Netherlands, Belgium, France, UK, Norway, Spain, Hungary, Canada, USA, China, India, and Mexico. These laboratories may be considered as the pioneers in this field. Consequently, there is considerable geographical diversity in the location of key laboratories. The importance of this field has been documented by various international meetings fully or partly devoted to CHC. Most of the international meetings in the field of heterogeneous catalysis have an independent section devoted fully to high throughput and combinatorial methods. In several highly ranked meetings (Europacat, International Catalysis Congress), round table discussions took place that were excellent forums for listening to both the pros and the cons of CHC. It should be mentioned that there is a special “heterogeneous catalysis” section at the Gordon Research Conferences devoted to the field of combinatorial materials research. If we look back to the first publications in the area of CHS, we see that the focus has been on the methodology and the use of high-throughput techniques and technologies. In this period, gas phase catalytic reactions were usually investigated. Today, after ten years of practice in this field, the area of activity in CHC has been expanded considerably.......

We discuss thoroughly aspects and issues for the development of a bespoke, but generic, electronic infrastructure designed to cope with the dynamic in high-throughput experimentation and knowledge management, is applicable to large or contract research organizations. We present the first generation of an informatics platform developed for TOPCOMBI, a research project funded by the European Commission for Nanotechnology and Nanoscience. It is composed by an infrastructure and a collection of modules dealing with laboratory analytics, robotics, data handling and analytics, optimization, in-database processing and visualization, which are developed collegially by the partners of the Consortium. This best-of-breed informatics system enables the capture and the re-usage of processes and methodologies, i.e. process and data flows, using the workflow paradigm. Complex workflows designed by power users can be eventually used by either other domain experts or by novices through a web portal. Workflows can also be run interactively to allow visual analytics for instance, or automatically. We present two case studies dealing with the kinetic study of glycerol catalytic oxidation using parallel equipments, and a novel, fully integrated QSAR applied in heterogeneous catalysis, respectively.

Computational chemistry can provide fundamental knowledge regarding various aspects of materials. While its impact in scientific research is greatly increasing, its contributions to industrially important issues are far from satisfactory. In order to realize industrial innovation by computational chemistry, a new concept “combinatorial computational chemistry” has been proposed by introducing the concept of combinatorial chemistry to computational chemistry. This combinatorial computational chemistry approach enables theoretical high-throughput screening for materials design. In this manuscript, we review the successful applications of combinatorial computational chemistry to deNOx catalysts, Fischer-Tropsch catalysts, lanthanoid complex catalysts, and cathodes of the lithium ion secondary battery.

Partial oxidation of propylene was investigated at 1 atm pressure over Rh/TiO2 catalysts as a function of reaction temperature, metal loading and particle size using high-throughput methods. Catalysts were prepared by ablating thin sheets of pure rhodium metal using an excimer laser and by collecting the nanoparticles created on the external surfaces of TiO2 pellets that were placed inside the ablation plume. Rh nanoparticles before the experiments were characterized by transmission electron microscopy (TEM) by collecting them on carbon film. Catalyst evaluations were performed using a high-throughput array channel microreactor system coupled to quadrupole mass spectrometry (MS) and gas chromatography (GC). The reaction conditions were 23% C3H6, 20% O2 and the balance helium in the feed, 20,000 h-1 GHSV and a temperature range of 250-325 oC. The reaction products included primarily acetone (AT) and to a lesser degree propionaldehyde (PaL) as the C3 products, together with deep oxidation products COx.

In this study, artificial neural networks (ANNs) were used to reveal a quantitative relationship between catalytic composition and catalytic activity. This relationship was predefined using a hypothetical experimental space described by a multidimensional polynomial. The predictive ability of ANNs was investigated, i.e. an attempt was done to evaluate how ANNs can envisage a given hypothetical experimental space. Data sets for training, validation and testing of ANNs were obtained from the hypothetical experimental space using two different ways of sampling. Data were selected, (i) by means of our optimization algorithm called Holographic Research Strategy (HRS); and (ii) randomly. In order to model real experimentation, data were also generated with error. The relationship between the complexity of different network topologies and their predictive ability was investigated. It was shown that when data used for training have been perturbed with a given level of noise, less complex network architectures give acceptable accuracy. Additionally, estimated experimental spaces were visualized in a 2D layout by means of Holographic Mappings (HMs). Analysis of HMs revealed that ANNs trained by data sets obtained upon an optimization procedure provides better description of the experimental space in the vicinity of the optimum than ANNs trained by randomly selected data sets. This fact indicates again the importance of the optimization in combinatorial catalyst library design.

High-throughput approaches were applied to the discovery of more efficient catalysts for various applications in emissions control. The screening approach was based on a hierarchy of qualitative or semi-quantitative primary screens for discovery of hits and quantitative secondary screens for confirmation and scale-up of leads. In this work, primary screening was carried out by fast scanning mass spectrometry (SMS) for NOx abatement, low temperature CO oxidation, VOC removal, COx methanation and the water gas shift (WGS) reaction.

The Selox is a catalytic benchmark for the selective CO oxidation reaction in the presence of H2, in the form of mathematical equations obtained via modelling of experimental results. The optimisation efficiencies of several Global Optimisation algorithms were studied using the Selox benchmark. Genetic Algorithms, Evolutionary Strategies, Simulated Annealing, Taboo Search and Genetic Algorithms hybridised with Knowledge Discovery procedures were the methods compared. A Design of Experiments search strategy was also exemplified using this benchmark. The main differences regarding the applicability of DoE and Global optimisation techniques are highlighted. Evolutionary strategies, Genetic algorithms, using the sharing procedure, and the Hybrid Genetic algorithms proved to be the most successful in the benchmark optimisation.

Dr. Jozsef L. Margitfalvi received his M.S. degree in Chemical Engineering in Moscow at Mendlejeff Chemical Technological University in 1965 with a strong emphasis in petroleum chemistry and refinery technology. After 4 years of industrial practice in Hungary, he continued his study as a postgraduate student in Moscow. He investigated homogeneous catalytic epoxidation of cyclohexene and received his Ph.D. degree in 1972. Next, Dr. Margitfalvi joined the Institute of Isotopes in Budapest as a research fellow, where he studied the thermal decomposition patterns of different organometallic compounds. Based on these investigations, different supported metal catalyst were prepared. During 1978-1979, he worked at the Worchester Polytechnic Institute in Massachusetts under professor Alvin Weiss. In 1979, Dr. Margitfalvi joined the Central Research Institute for Chemistry and became a senior research associate and group leader. He conducted research in the area of preparation and modification of supported metal catalysts used in hydrocarbon reactions and developed the method of “Controlled Surface Reactions” for the preparation of supported bimetallic catalysts with direct metal-metal interaction. Since 1985, his main interest has been “catalysis in organic reactions” with the focus on catalyst design and modification for selective hydrogenations, reductive amination and carbonyl activation. In 1985, Dr. Margitfalvi received the title of invited “Associate Professor” at the Department of Organic Chemistry at University of Szeged. In the period of 1989-1991, he joined the Swiss Institute ETH in Zurich as a guest scientist. Under Prof. Alfons Baiker he investigated asymmetric hydrogenation of ethyl pyruvate. After returning to Hungary, Dr. Margitfalvi reorganized his research group under the name “Surface Reactions and Catalysis.” In 1995, this group was reorganized further into the “Department of Organic Catalysis,” and he was appointed as the head of this new department. In 1996, Dr. Margitfalvi received the highest academic degree in Hungary, the title “Doctor of the Hungarian Academy of Sciences”. In 1999, he edited a special issue for Applied Catalysis on the topic, “Catalysis by anchored and encapsulated species.” After reorganization of the Central Research Institute for Chemistry into the Chemical Research Center in 1999, he expressed his interest to start research in the area of combinatorial catalysis. By 2002, his group had developed a new catalyst library optimization method, the Holographic Research Strategy. By incorporating an information mining tool, this method became a fast and reliable informatics platform for catalyst library optimization and visualization of multi-dimensional experimental space. In addition to the investigation of chemo- and asymmetric hydrogenation reaction, Dr. Margitfalvi launched a new project aimed at the examination of different oxidation and oxygen transfer reactions, such as CO oxidation, PROX reaction, and oxidative sulfur removal from diesel fuel. His other new research project is the investigation of catalytic reactions for hydrogen production, such as reforming of various alcohols. He has established industrial contacts with different European and US companies in the area of fine chemistry and combinatorial catalysis and is representing Hungary in the European Federation of Catalysis Societies (EFCATS). His hobbies are electronic music (both listening and creating), traveling, sports (soccer, tennis, table tennis, etc.) and reading. SELECTED PUBLICATIONS [1] Vegvari, L.; Tompos, A.; G bolos, S.; Margitfalvi, J.L. Holographic research strategy for catalyst library design. Description of a new powerful optimization method. Catal. Today, 2003, 81, 517-527. [2] Tompos, A.; Margitfalvi, J.L.; Tfirst, E.; Vegvari, L. Information mining using artificial neural networks and “holographic research strategy”. Appl. Catal. A-Gen., 2003, 254, 161-168. [3] Tompos, A.; Margitfalvi, J.L.; Tfirst, E.; Vegvari, L.; Jaloull, M.A.; Khalfalla, H.A.; Elgarni, M.M. Development of catalyst libraries for total oxidation of methane. A case study for combined application of holographic research strategy and artificial neural networks in catalyst library design. Appl. Catal. AGen., 2005, 285, 65-75........