Experimental and Computational Study Toward Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2024-10-03
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en
Pages
11
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Small, Volume 20, issue 40
Abstract
SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however; it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, CO2RR performance is enhanced by supporting SnO2 nanoparticles on two common supports, vulcan carbon and TiO2. Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C. Furthermore, this study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO2 reduction and establish a correlation between SnOx structures and their CO2RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2−, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx-based electrocatalysts for CO2RR.Description
Publisher Copyright: © 2024 The Author(s). Small published by Wiley-VCH GmbH.
Keywords
CO reduction reaction, machine learned interatomic potential, supported SnO nanoparticles, temperature effect
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Citation
Shi, J, Pršlja, P, Jin, B, Suominen, M, Sainio, J, Jiang, H, Han, N, Robertson, D, Košir, J, Caro, M & Kallio, T 2024, ' Experimental and Computational Study Toward Identifying Active Sites of Supported SnO x Nanoparticles for Electrochemical CO 2 Reduction Using Machine-Learned Interatomic Potentials ', Small, vol. 20, no. 40, 2402190 . https://doi.org/10.1002/smll.202402190