Issue 14, 2020

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

Abstract

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 CO2 to reveal the details of the composition-dependent structural evolution and brass alloy formation, and their correlation with the catalytic selectivity of these materials.

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

Supplementary files

Article information

Article type
Edge Article
Submitted
20 Janv. 2020
Accepted
05 Marts 2020
First published
11 Marts 2020
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2020,11, 3727-3736

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

J. Timoshenko, H. S. Jeon, I. Sinev, F. T. Haase, A. Herzog and B. Roldan Cuenya, Chem. Sci., 2020, 11, 3727 DOI: 10.1039/D0SC00382D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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