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Learning Design Rules for Selective Oxidation Catalysts from High-Throughput Experimentation and Artificial Intelligence

Lucas Foppa, Christopher Sutton, Luca M. Ghiringhelli, Sandip De, Patricia Löser, Stephan A. Schunk, Ansgar Schäfer, Matthias Scheffler

2022ACS Catalysis53 citationsDOIOpen Access PDF

Abstract

-supported catalysts containing ruthenium, tungsten, and phosphorus were synthesized and tested in the catalytic oxidation of propylene. As candidate descriptive parameters, the temperature and 10 parameters related to the composition and chemical nature of the catalyst materials, derived from calculated free-atom properties, were offered. The temperature, the phosphorus content, and the composition-weighted electronegativity are identified as key parameters describing high yields toward the value-added oxygenate products acrolein and acrylic acid. The SG rules not only reflect the underlying processes particularly associated with high performance but also guide the design of more complex catalysts containing up to five elements in their composition.

Topics & Concepts

CatalysisElectronegativityReactivity (psychology)ChemistryRutheniumAcroleinChemical engineeringOrganic chemistryMedicinePathologyAlternative medicineEngineeringCatalysis and Oxidation ReactionsMachine Learning in Materials ScienceCatalytic Processes in Materials Science
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