Tong Yang, Keda Ding, Jun Zhou, Xiaoyang Ma, Kay Chen Tan, Ge Wang, Haitao Huang, Ming Yang
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Unravelling Species-Specific Loading Effects on Oxygen Reduction Activity of Heteronuclear Single Atom Catalysts.
Toward high-density single atom catalysts (SACs), the interaction between neighboring SACs and the induced non-linear loading effect become crucial for their intrinsic catalytic performance. Despite recent investigations on homonuclear SACs, understanding such effect in heteronuclear SACs remains limited. Using Fe and Co SACs co-supported on the nitrogen-doped graphene as a model system, the loading effect on the site-specific activity of heteronuclear SACs toward oxygen reduction reaction (ORR) is here reported by density functional theory calculations. The Fe site exhibits an oscillatory decrease in activity with the loading. In contrast, the Co site has a volcano-like activity with the optimum performance achieved at ≈16.8 wt.% (average inter-site distance: ≈7 Å). At the ultra-high loading of 38.4 wt.% (inter-site distance: ≈4 Å), the Co site is the only ORR active site, whereas Fe sites turn into spectators. This distinct loading-dependent activity between the Fe and Co sites can be ascribed to their difference in the binding capability with the substrate and the dxz and dyz orbitals' occupation. These findings highlight the importance of the loading effect in heteronuclear SACs, which could be useful for the development of high-performance heteronuclear and high-entropy SACs toward various catalytic reactions in the high-loading regime.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.