The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Nitrogen electroreduction performance of transition metal dimers embedded into N-doped graphene: a theoretical prediction
Abstract
By means of comprehensive density functional theory computations, the FeRh dimer embedded in N-doped graphene was screened as a quite promising electrocatalyst with dual-atom centered sites for ammonia synthesis from N 2 fixation.
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Cites
How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels
Peterson, A. A., Abild-Pedersen, F., Studt, F., Rossmeisl, J., & Nørskov, J. K. (2010) · Energy & Environmental Science · doi:10.1039/c0ee00071j · Cited by 3,784
Montoya, J. H., Tsai, C., Vojvodic, A., & Nørskov, J. K. (2015) · ChemSusChem · doi:10.1002/cssc.201500322 · Cited by 1,384
Cited by
Ren, Y., Yu, C., Tan, X., Huang, H., Wei, Q., & Qiu, J. (2021) · Energy & Environmental Science · doi:10.1039/d0ee03596c · Cited by 565
Lv, X., Wei, W., Huang, B., Dai, Y., & Frauenheim, T. (2021) · Nano Letters · doi:10.1021/acs.nanolett.0c05080 · Cited by 369
Metadata
- Authors
- Li, H., Zhao, Z., Cai, Q., Yin, L., & Zhao, J.
- Venue
- Journal of Materials Chemistry A
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2020
- Volume
- 8
- Issue
- 8
- Pages
- 4533-4543
- ISSN
- 2050-7488 (ISSN-L)
- 2050-7496
- OpenAlex
- W3003612706
- Cited by
- 160
- References
- 2
- FWCI
- 6.02
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Nitrogen electroreduction performance of transition metal dimers embedded into N-doped graphene: a theoretical prediction. https://app.cochat.ai/papers/10.1039/c9ta13599e
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- Li, H., Zhao, Z., Cai, Q., Yin, L., & Zhao, J. (2020). Nitrogen electroreduction performance of transition metal dimers embedded into N-doped graphene: a theoretical prediction. Journal of Materials Chemistry A. https://doi.org/10.1039/c9ta13599e
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
