Experimental and Computational Study Toward Identifying Active Sites of Supported SnO x Nanoparticles for Electrochemical CO 2 Reduction Using Machine-Learned Interatomic Potentials.

SnO x has received great attention as an electrocatalyst for CO 2 reduction reaction (CO 2 RR), however; it still suffers from low activity. Moreover, the atomic-level SnO x 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, CO 2 RR performance is enhanced by supporting SnO 2 nanoparticles on two common supports, vulcan carbon and TiO 2 . Then, electrolysis of CO 2 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 SnO 2 reduction and establish a correlation between SnO x structures and their CO 2 RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of * H and * CO 2 - , 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 SnO x -based electrocatalysts for CO 2 RR.