Machine Learning-Assisted Design Framework of Carbon Edge-Dominated Dual-Atom Catalysts for Urea Electrosynthesis
-orbitals of early (Zr) and late (Zn) transition metals weakly bind CO, preventing its deep reduction. This work establishes a scalable ML-assisted paradigm for decoupling competitive mechanisms in complex electrocatalysis.
Authors
- Xiangdong Yao (ORCID: https://orcid.org/0000-0002-1235-5090)
- M. T. Nasir (ORCID: https://orcid.org/0000-0001-5752-7119)
- Hanqing Yin (ORCID: https://orcid.org/0009-0009-8774-8348)
- Qingchao Fang (ORCID: https://orcid.org/0000-0002-4992-6045)
- Yun Han (ORCID: https://orcid.org/0000-0002-6159-934X)
- Qin Li (ORCID: https://orcid.org/0000-0002-3934-6004)
- Aijun Du (ORCID: https://orcid.org/0000-0002-3369-3283)
- Qilong Wu
- Xin Mao
Institutions
- Griffith University (AU)
- Sun Yat-sen University (CN)
- Queensland University of Technology (AU)
- University of Wollongong (AU)
- Ningbo University of Finance & Economics (CN)
- ARC Centre of Excellence for Electromaterials Science (AU)
- Technical University of Denmark (DK)
Publication Details
- Journal
- ACS Nano
- Published
- 2026-04-21
- DOI
- https://doi.org/10.1021/acsnano.6c04319
- Primary Topic
- Ammonia Synthesis and Nitrogen Reduction
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and Technology of the People's Republic of China
- Australian Research Council