Knowledge Based Catalyst Design by High Throughput Screening of Model Reactions and Statistical Modelling. - IFPEN - IFP Energies nouvelles Access content directly
Journal Articles Oil & Gas Science and Technology - Revue d'IFP Energies nouvelles Year : 2013

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.

Domains

Catalysis
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Dates and versions

hal-00864211 , version 1 (26-09-2013)

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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, 2013, 68 (3), pp.487-504. ⟨10.2516/ogst/2012082⟩. ⟨hal-00864211⟩
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