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Wang, Shuo, Gao, Haixing, Li, Lei, San Hui, Kwan 
ORCID: https://orcid.org/0000-0001-7089-7587, Dinh, Duc Anh, Wu, Shuxing, Kumar, Sachin, Chen, Fuming, Shao, Zongping and Nam Hui, Kwun
(2022)
High-throughput identification of highly active and selective single-atom catalysts for electrochemical ammonia synthesis through nitrate reduction.
Nano Energy, 100.
ISSN 2211-2855
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Abstract
The highly selective and active nitrate-to-ammonia electrochemical conversion (NO3 reduction reaction [NO3RR]) can be an appealing and supplementary alternative to the Haber-Bosch process. It also opens up a new idea for addressing nitrate pollution. Previous study demonstrated that FeN4 single-atom catalyst (SAC) indicates excellent NO3RR performance. Nonetheless, the mechanism that triggers the electrocatalytic NO3RR remains unclear. The feasibility of NO3RR over various SACs is verified in this study via high-throughput density functional theory calculations with the single transition metal (TM) atom coordinated with four nitrogen atoms supported on graphene as the example. We conducted a comprehensive screening of TM SAC candidates for stability, NO3− adsorption strength, catalytic activity, and selectivity. Results reveal that the most promising candidate among the 23 TM SACs is Os SAC with a low limiting potential of −0.42 V. Os SAC is better than Fe SAC with a limiting potential of −0.53 V because of the strong interaction between the oxygen of NO3− species and Os atom. The origin of high NO3RR activity of Os SAC is explained by its inner electronic structure of the strong hybridization of the Os atom and NO3− caused by the increasing charge transfer from TM atom to NO3−, leading to the suitable NO3− adsorption. This research provides a fundamental insight of discovering novel NO3RR catalysts and may provide a motivating drive for the creation of effective ammonia electrocatalysts for further experimental investigation.
| Item Type: | Article |
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| Additional Information: | Data availability: The authors declare that the data supporting the findings of this study are available in the paper and Supplementary Information. Source data are provided with this paper. Acknowledgements: This work was funded by the Science and Technology Development Fund, Macau SAR (File no. 0041/2019/A1, 0046/2019/AFJ, 0021/2019/AIR, 0007/2021/AGJ), University of Macau (File no. MYRG2017-00216-FST, MYRG2018-00192-IAPME, MYRG2020-00187-IAPME), the UEA funding, Science and Technology Program of Guangzhou (2019050001), and National Key Research and Development Program of China (2019YFE0198000). F. Chen acknowledges the Pearl River Talent Program (2019QN01L951). The DFT calculations are performed at High Performance Computing Cluster (HPCC) of Information and Communication Technology Office (ICTO) at University of Macau. |
| Faculty \ School: | Faculty of Science > School of Engineering |
| UEA Research Groups: | Faculty of Science > Research Groups > Emerging Technologies for Electric Vehicles (EV) Faculty of Science > Research Groups > Energy Materials Laboratory |
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| Depositing User: | LivePure Connector |
| Date Deposited: | 21 Jun 2022 10:30 |
| Last Modified: | 18 Jun 2023 01:38 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/85724 |
| DOI: | 10.1016/j.nanoen.2022.107517 |
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