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

Guilhem Morra 1 David Farrusseng 2 Christophe Bouchy 1, * Stéphane Morin 1
* Corresponding author
1 IFPEN - IFP Energies nouvelles
2 IRCELYON - Institut de recherches sur la catalyse et l'environnement de Lyon
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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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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