Experimental and Theoretical Study into Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials

SnO2 has received great attention for electrochemical CO2 reduction reaction (CO2RR), however, it still suffers from low activity. Moreover, SnO2 structure at the atomic level and the nature of active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, two common supports i.e. Vulcan carbon (C) and TiO2 were used to enhance the performance of SnO2. SnO2/C demonstrates over 90% selectivity for C1 products (HCOO− and CO) over a wide potential range while SnO2/TiO2 shows lower activities. Furthermore, our study outlines an explicit method for multiscale simulation to investigate SnO2 reduction and establish a correlation between SnOx structures and their CO2RR performance. For that reason, we used the Machine Learning interatomic potential method to generate SnOx nanoparticles to assess the possible synthesized active sites. Further, computational selectivity is analyzed with Density Functional Theory simulations to identify the key differences between the binding energies of *H and *CO2−, which are correlated with a surface oxygen amount. In addition, electrolysis of CO2 at various temperatures in a neutral electrolyte revealed that the application window for this catalyst is between 12 and 30 °C. This study offers an in-depth understanding and insight into the rational design and application of Sn-based electrocatalysts for CO2RR.