The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
High-Throughput Screening of Synergistic Transition Metal Dual-Atom Catalysts for Efficient Nitrogen Fixation
Abstract
Great enthusiasm in single-atom catalysts (SACs) for the nitrogen reduction reaction (NRR) has been aroused by the discovery of metal–N x as a promising catalytic center. However, the poor activity and low selectivity of available SACs are far away from the industrial requirement. Through the first-principles high-throughput screening, we find that Fe–Fe distributed on graphite carbon nitride (Fe 2 / g -CN) can manipulate the binding strength of the target reaction species (compromises the ability to adsorb N 2 H and NH 2 ), therefore achieving the best NRR performance among 23 transition metal (TM) centers. Our results show that Fe 2 / g -CN achieves a high theoretical Faradaic efficiency of 100% and, impressively, the lowest limiting potential of −0.13 V. Particularly, multiple-level descriptors shed light on the origin of NRR activity, achieving a fast prescreening among various candidates. Our predictions not only accelerate discovery of catalysts for ammonia synthesis but also contribute to further elucidate the structure–performance correlations.
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1 retracted
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Cited by
Review of Carbon Support Coordination Environments for Single Metal Atom Electrocatalysts (SACS)
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Metadata
- Authors
- Lv, X., Wei, W., Huang, B., Dai, Y., & Frauenheim, T.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-02-15
- Volume
- 21
- Issue
- 4
- Pages
- 1871-1878
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W3132688281
- Cited by
- 369
- References
- 65
- FWCI
- 13.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lv, X., Wei, W., Huang, B., Dai, Y., & Frauenheim, T. (2021). High-Throughput Screening of Synergistic Transition Metal Dual-Atom Catalysts for Efficient Nitrogen Fixation. Nano Letters. https://doi.org/10.1021/acs.nanolett.0c05080
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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