Influence of Oxygen Vacancy Distribution on CO Hydrogenation: A Case Study of ZnO and InO.

Oxygen vacancies (OVs) on metal oxide surfaces are widely recognized as catalytically active sites; however, the impact of their distribution on the catalytic performance remains underexplored. In this study, we used density functional theory (DFT) calculations combined with a machine learning potential to investigate the distribution of OVs on the ZnO-(10 0) surface and their role in CO hydrogenation. We efficiently analyzed over 700,000 potential OV configurations by reducing them to unique, irreducible structures using the self-developed DefectMaker program. Our results revealed that higher OV concentrations led to the formation of linear OV structures, which, despite their energetic stability, exhibited lower CO hydrogenation efficiency compared to isolated OVs, due to the reduced surface polarization with linear OVs. Additionally, a comparative investigation on InO surfaces revealed a scattered distribution of OVs, maintaining the material's catalytic activity in CO hydrogenation. This work provides a deeper understanding of defect engineering in metal oxides for a more efficient CO conversion.