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

SnO has received great attention as an electrocatalyst for CO reduction reaction (CORR), however; it still suffers from low activity. Moreover, the atomic-level SnO 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, CORR performance is enhanced by supporting SnO nanoparticles on two common supports, vulcan carbon and TiO. Then, electrolysis of CO 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 reduction and establish a correlation between SnO structures and their CORR 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 , 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-based electrocatalysts for CORR.