Fine Engineering of d-Orbital Vacancies of ZnN<sub>4</sub> via High-Shell Metal and Nonmetal Single-Atoms for Efficient and Poisoning-Resistant ORR
Atomically dispersed metal-nitrogen-carbon (M-N-C) materials are active oxygen reduction reaction (ORR) catalysts. Among M-N-C catalysts, ZnN<sub>4</sub> single-atom catalysts (SACs) due to a nearly full 3d<sup>10</sup> electronic configuration insufficiently activate oxygen and display low ORR activity. To finely engineer d-orbital vacancies of ZnN<sub>4</sub>, we combine high-shell metal and nonmetal SAs as electronic regulators that are ZnN<sub>4</sub>Cl and carbon vacancy-hosted -Cl motifs, which show complementary electron-withdrawing capacities versus the ZnN<sub>4</sub>. Under that, the ZnN<sub>4</sub> exhibits significantly enhanced ORR activity with a half-wave potential (<i>E</i><sub>1/2</sub>) of 0.912 V<sub>RHE</sub> relative to the unmodified ZnN<sub>4</sub> (<i>E</i><sub>1/2</sub> = 0.822 V<sub>RHE</sub>) and simultaneously robust durability (negligible activity loss after 10,000 potential cycles). Particularly, the engineered ZnN<sub>4</sub> possesses high resistance to SCN<sup>-</sup> poisoning, which is rarely achieved among M-N-C SACs. Our works show that combining high-shell metal and nonmetal SAs can finely engineer d-orbital vacancies of metal centers to an optimal state, thereby intrinsically enhancing their catalytic performance.
Authors
- Xiaoxin Zou (ORCID: https://orcid.org/0000-0003-4143-9274)
- Xiao Dan Zhao (ORCID: https://orcid.org/0000-0002-8183-8205)
- Xiaoqiang Cui (ORCID: https://orcid.org/0000-0002-5858-6257)
- Xinyi Li (ORCID: https://orcid.org/0000-0001-5436-9856)
- Lu Li (ORCID: https://orcid.org/0000-0003-4464-8194)
- Hong Huang (ORCID: https://orcid.org/0000-0001-7487-4876)
- Weitao Zheng (ORCID: https://orcid.org/0000-0002-9028-278X)
- Xiaoyuan Sun
- Xiaochun Xu
- Wenting Lu (ORCID: https://orcid.org/0009-0009-9272-5693)
Institutions
- Jilin University (CN)
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry
Publication Details
- Journal
- Nano Letters
- Published
- 2024-11-08
- DOI
- https://doi.org/10.1021/acs.nanolett.4c02830
- Citations
- 21
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.46
Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
- Jilin Scientific and Technological Development Program