Experimental and Computational Study Toward Identifying Active Sites of Supported SnO_x Nanoparticles for Electrochemical CO₂ Reduction Using Machine-Learned Interatomic Potentials

SnO_x has received great attention as an electrocatalyst for CO₂ reduction reaction (CO₂RR), 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, 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 SnO_x 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 SnO_x-based electrocatalysts for CO₂RR.