Knowledge Based Catalyst Design by High Throughput Screening of Model Reactions and Statistical Modelling.

Abstract : Material design and synthesis are key steps in the development of catalysts. They are usually based on an empiric and/or theoretical approach. The recently developed high-throughput experimentation can accelerate optimisation of new catalytic formulations by systematic screening in a predefined study domain. This work aims at developing a QSAR (Quantitative Structure Activity Relationship) method based on kinetic and mechanistic descriptors for metal and acid catalysis. Physico-chemical features of approximately sixty bimetallic catalysts have been measured according to their performance in two model reactions: xylene hydrogenation for catalysis on metallic sites and isomerisation of 3,3-dimethyl-1-butene for catalysis on acid sites. These descriptors were finally used to model the performances of around twenty catalysts for a more complex reaction: n-decane dehydrogenation.
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Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, Institut Français du Pétrole, 2013, 68 (3), pp.487-504. 〈10.2516/ogst/2012082〉
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Guilhem Morra, David Farrusseng, Christophe Bouchy, Stéphane Morin. Knowledge Based Catalyst Design by High Throughput Screening of Model Reactions and Statistical Modelling.. Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles, Institut Français du Pétrole, 2013, 68 (3), pp.487-504. 〈10.2516/ogst/2012082〉. 〈hal-00864211〉

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