Song Dandan, Cui Leyuan, Qin Ruixuan, Fu Gang
State Key Laboratory for Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, China.
JACS Au. 2025 Jun 11;5(7):3156-3162. doi: 10.1021/jacsau.5c00304. eCollection 2025 Jul 28.
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.
金属氧化物表面的氧空位（OVs）被广泛认为是催化活性位点；然而，它们的分布对催化性能的影响仍未得到充分研究。在本研究中，我们使用密度泛函理论（DFT）计算结合机器学习势来研究OVs在ZnO-(10 0)表面的分布及其在CO加氢中的作用。我们通过使用自行开发的DefectMaker程序将超过700,000种潜在的OV构型简化为独特的、不可约的结构，从而有效地对其进行了分析。我们的结果表明，较高的OV浓度导致线性OV结构的形成，尽管这些结构具有能量稳定性，但与孤立的OVs相比，由于线性OVs的表面极化降低，其CO加氢效率较低。此外，对InO表面的对比研究揭示了OVs的分散分布，保持了材料在CO加氢中的催化活性。这项工作为更有效地进行CO转化的金属氧化物缺陷工程提供了更深入的理解。
