Machine-learning enables nitrogen reduction reaction on transition metal doped C<sub>3</sub>B by controlling the charge states
The charge transfer and d-band center are key factors affecting the NRR performance of TM@C 3 B, both of which can be modulated by charge states. Thus, use of charge states in modeling electrochemical reactions represents a new design method.
Authors
- Guixian Ge (ORCID: https://orcid.org/0000-0003-1193-2025)
- Pengfei Ou (ORCID: https://orcid.org/0000-0002-3630-0385)
- Aodi Zhang
- Chao Yang (ORCID: https://orcid.org/0000-0003-1202-6892)
- Hongxia Yan (ORCID: https://orcid.org/0000-0001-7432-2385)
- Wentao Wang (ORCID: https://orcid.org/0000-0003-4308-3515)
- Yunxia Liang (ORCID: https://orcid.org/0000-0002-3107-4163)
- Chengwei Yang
Institutions
- Shihezi University (CN)
- National University of Singapore (SG)
- Henan University (CN)
- Guizhou Education University (CN)
Publication Details
- Journal
- Materials Chemistry Frontiers
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5qm00140d
- Citations
- 5
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.59