Experimental and Theoretical Study into IdentifyingIdentifying 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.二氧化锡（SnO₂）因应用于电化学二氧化碳还原反应（electrochemical CO₂ reduction reaction, CO₂RR）而受到广泛关注，但其催化活性仍偏低。此外，由于表面结构的动态性以及电化学条件下结构表征存在难度，SnO₂的原子级结构与活性位点本质仍不明确。 本研究采用两种常见载体，即瓦尔肯碳（Vulcan carbon, C）与二氧化钛（TiO₂），以提升SnO₂的催化性能。实验结果显示，SnO₂/C催化剂在宽电位区间内对C1产物（甲酸根HCOO⁻与一氧化碳CO）的选择性超过90%，而SnO₂/TiO₂的催化活性相对较低。 此外，本研究提出了一种明确的多尺度模拟方法，用于探究SnO₂的还原过程，并建立SnOₓ结构与其CO₂RR性能之间的关联。为此，我们采用机器学习原子间势（Machine Learning interatomic potential）方法生成SnOₓ纳米颗粒，以评估潜在可合成的活性位点。进一步通过密度泛函理论（Density Functional Theory, DFT）模拟分析计算层面的选择性，以明确*H与*CO₂⁻的结合能差异——该差异与表面氧含量密切相关。 另外，在中性电解质中于不同温度下开展CO₂电解实验的结果表明，该催化剂的适用温度区间为12 ℃至30 ℃。 本研究为面向CO₂RR的锡基电催化剂的合理设计与应用提供了深入的认知与见解。