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Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks

Understanding the evolution of unique structural motifs in bimetallic catalysts under reaction conditions, and linking them to the observed catalytic properties is necessary for the rational design of the next generation of catalytic materials. Extended X-ray absorption fine structure (EXAFS) spectr...

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Autores principales: Timoshenko, Janis, Jeon, Hyo Sang, Sinev, Ilya, Haase, Felix T., Herzog, Antonia, Roldan Cuenya, Beatriz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152410/
https://www.ncbi.nlm.nih.gov/pubmed/34094061
http://dx.doi.org/10.1039/d0sc00382d
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author Timoshenko, Janis
Jeon, Hyo Sang
Sinev, Ilya
Haase, Felix T.
Herzog, Antonia
Roldan Cuenya, Beatriz
author_facet Timoshenko, Janis
Jeon, Hyo Sang
Sinev, Ilya
Haase, Felix T.
Herzog, Antonia
Roldan Cuenya, Beatriz
author_sort Timoshenko, Janis
collection PubMed
description Understanding the evolution of unique structural motifs in bimetallic catalysts under reaction conditions, and linking them to the observed catalytic properties is necessary for the rational design of the next generation of catalytic materials. Extended X-ray absorption fine structure (EXAFS) spectroscopy is a premier experimental method to address this issue, providing the possibility to track the changes in the structure of working catalysts. Unfortunately, the intrinsic heterogeneity and enhanced disorder characteristic of catalytic materials experiencing structural transformations under reaction conditions, as well as the low signal-to-noise ratio that is common for in situ EXAFS spectra hinder the application of conventional data analysis approaches. Here we address this problem by employing machine learning methods (artificial neural networks) to establish the relationship between EXAFS features and structural motifs in metals as well as oxide materials. We apply this approach to time-dependent EXAFS spectra acquired from copper–zinc nanoparticles during the electrochemical reduction of CO(2) to reveal the details of the composition-dependent structural evolution and brass alloy formation, and their correlation with the catalytic selectivity of these materials.
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spelling pubmed-81524102021-06-03 Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks Timoshenko, Janis Jeon, Hyo Sang Sinev, Ilya Haase, Felix T. Herzog, Antonia Roldan Cuenya, Beatriz Chem Sci Chemistry Understanding the evolution of unique structural motifs in bimetallic catalysts under reaction conditions, and linking them to the observed catalytic properties is necessary for the rational design of the next generation of catalytic materials. Extended X-ray absorption fine structure (EXAFS) spectroscopy is a premier experimental method to address this issue, providing the possibility to track the changes in the structure of working catalysts. Unfortunately, the intrinsic heterogeneity and enhanced disorder characteristic of catalytic materials experiencing structural transformations under reaction conditions, as well as the low signal-to-noise ratio that is common for in situ EXAFS spectra hinder the application of conventional data analysis approaches. Here we address this problem by employing machine learning methods (artificial neural networks) to establish the relationship between EXAFS features and structural motifs in metals as well as oxide materials. We apply this approach to time-dependent EXAFS spectra acquired from copper–zinc nanoparticles during the electrochemical reduction of CO(2) to reveal the details of the composition-dependent structural evolution and brass alloy formation, and their correlation with the catalytic selectivity of these materials. The Royal Society of Chemistry 2020-03-11 /pmc/articles/PMC8152410/ /pubmed/34094061 http://dx.doi.org/10.1039/d0sc00382d Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Timoshenko, Janis
Jeon, Hyo Sang
Sinev, Ilya
Haase, Felix T.
Herzog, Antonia
Roldan Cuenya, Beatriz
Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks
title Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks
title_full Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks
title_fullStr Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks
title_full_unstemmed Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks
title_short Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks
title_sort linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando exafs and neural-networks
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152410/
https://www.ncbi.nlm.nih.gov/pubmed/34094061
http://dx.doi.org/10.1039/d0sc00382d
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