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Wang, Shuo, Li, Lei, Li, Jing, Yuan, Chengzong, Kang, Yao, Hui, Kwan San 
ORCID: https://orcid.org/0000-0001-7089-7587, Zhang, Jintao, Bin, Feng, Fan, Xi, Chen, Fuming and Hui, Kwun Nam
(2021)
High-throughput screening of nitrogen-coordinated bimetal catalysts for multielectron reduction of CO2 to CH4 with high selectivity and low limiting potential.
The Journal of Physical Chemistry C, 125 (13).
pp. 7155-7165.
ISSN 1932-7447
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Abstract
Significant challenges remain for developing efficient catalysts in an electrochemical multielectron CO2 reduction reaction (CO2RR), which usually suffers from poor activity and selectivity. Motivated by the recent experimental progress in fabricating dual-metal atom catalysts (DMACs) in N-doped graphene materials (graphene-N6V4; N: nitrogen and V: vacancy), we sampled eight types of homonuclear (N6V4-M2, M = Cr, Mn, Fe, Co, Ni, Cu, Pd, and Ag) catalysts and 28 types of heteronuclear (N6V4-M1M2) catalysts to study CO2RR activity via first-principles high-throughput screening. Using stability, activity, and selectivity as indicators along with the broken conventional scaling relationship, N6V4-AgCr was selected as a promising candidate for deep CO2 reduction to methane with a low overpotential of 0.55 V after two screening rounds. Further analysis showed that a frustrated Lewis pair, formed between metal and the para-N, owing to the difference in the electronic arrangement of the d orbitals of various transition metals, caused a difference in the spin polarization of the systems and affected the catalytic performance of each DMAC. Our work not only provides a solid strategy for screening potential catalysts but also demonstrates that their CO2 reduction activities originate from the various atomic and electronic structures of DMACs.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: This work was funded by the Science and Technology Development Fund, Macau SAR (File no. 0191/2017/A3, 0041/2019/A1, 0046/2019/AFJ, and 0021/2019/AIR), the University of Macau (File no. MYRG2017-00216-FST and MYRG2018-00192-IAPME), the UEA funding, the Science and Technology Program of Guangzhou (2019050001), and the National Key Research and Development Program of China (2019YFE0198000). F.C. acknowledges the Pearl River Talent Program (2019QN01L951). The DFT calculations were performed at the High Performance Computing Cluster (HPCC) of the Information and Communication Technology Office (ICTO) at the University of Macau. |
| Faculty \ School: | Faculty of Science > School of Engineering (former - to 2024) |
| UEA Research Groups: | Faculty of Science > Research Groups > Emerging Technologies for Electric Vehicles (EV) Faculty of Science > Research Groups > Energy Materials Laboratory |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 08 May 2021 00:03 |
| Last Modified: | 23 Oct 2024 23:56 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/79951 |
| DOI: | 10.1021/acs.jpcc.0c10802 |
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