DOI: https://doi.org/10.29363/nanoge.interect.2022.028
Publication date: 11th October 2022
The improvement of spectroscopic techniques has enabled the in situ characterization of catalysts under operating conditions, often revealing a highly dynamic behavior of the active phase. For example, metals and metal-oxides usually employed as catalysts frequently undergo significant chemical and structural transformations during operation. In contrast, the conceptual framework and structural models used to rationalize the catalytic properties of such materials has traditionally relied on a rather static picture of the catalyst substrate. In addition to this so-called environmental complexity, the structural complexity of such these nanostructured materials further hinders the characterization of their response to reaction conditions [1].
Establishing reliable structural models of working catalysts is particularly relevant in computational modeling studies relying on quantum mechanical calculations. To overcome these challenges, novel computational approaches have been developed to determine the structure and composition of targeted materials and conditions, combining quantum mechanics, structure prediction (i.e. global optimization) algorithms, ab initio thermodynamics, and, more recently, also machine-learning methods [2].
During my talk, I will introduce some of these approaches and showcase their capacity by presenting different case studies involving the characterization of the structure and oxidation states of technologically relevant catalytic materials [3, 4].
We gratefully acknowledge support by the Spanish/FEDER Ministerio de Ciencia, Innovación y Universidades (grants PGC2018-093863-B-C22, PID2021-128217NB-I00 and MDM-2017-0767), the Generalitat de Catalunya (AGAUR grant 2018BP00190), and "La Caixa" Foundation Junior Leader fellowship 2021.
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