Abstract
The goal of design of catalysts is to obtain a recipe for synthesis of optimum catalyst for a desired reaction. New optimal industrial catalysts should maximise the yield of desired products at commercially acceptable conditions with no or minimal amount of by-products. Industrial heterogeneous catalysts are usually very complex dynamic systems and have the multisite nature. Both selectivity and activity are functions of the chemical makeup of a particular catalytic site [1], i.e., its chemistry is fundamentally a molecular level phenomenon. The most important need in design of the industrial catalysts seems to be a conversion of molecular level description of catalysis into a form useably by engineer-designer of catalytic reactions system at real industrial scale and conditions [2]. It seems to be still a long-term goal. Therefore, the recipes for synthesis of an appropriate population of active sites on a surface of heterogeneous catalysts have been still obtained with experimental studies of known catalysts and designing of improving ones by analogy on the trial and error way. In the last years, new tools based on the combinatorial catalysis approach have improved efficiency of these studies. Design of catalyst libraries in the approach can be stochastic, random or directed (based on scientific knowledge and experience). In order to avoid huge amount of noises, the best design combines scientific knowledge (known rules and correlations, experience and intuition)—at a starting point—with stochastic methods use during the discovery or/and optimisation process.
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Borowiak, M.A. (2002). Impulse Oscillation Model For Accelerated Catalyst Design. In: Derouane, E.G., Parmon, V., Lemos, F., Ribeiro, F.R. (eds) Principles and Methods for Accelerated Catalyst Design and Testing. NATO Science Series, vol 69. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0554-8_23
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DOI: https://doi.org/10.1007/978-94-010-0554-8_23
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