Designing a machine learning-optimized nitrogen and sulfur co-doped CoNi@SiO<sub>2</sub> electrocatalyst for high performance oxygen evolution reaction
Although various heteroatom-doped bimetallic composites have been explored for the oxygen evolution reaction (OER), they often suffer from aggregation and low electrical conductivity, which hinder their electrocatalytic efficacy.
Authors
- Naznin Islam (ORCID: https://orcid.org/0000-0002-9224-2175)
- Farhan Zafar (ORCID: https://orcid.org/0009-0009-9527-3733)
- Muhammad Sohail (ORCID: https://orcid.org/0000-0001-5035-5019)
- Muhammad Ali Khan (ORCID: https://orcid.org/0000-0002-0991-9772)
- Naeem Akhtar (ORCID: https://orcid.org/0000-0001-9382-5869)
- Mujahid Abbas (ORCID: https://orcid.org/0000-0001-5528-1207)
- Salah M. El‐Bahy (ORCID: https://orcid.org/0000-0002-1652-3522)
- Mohammad Waseem (ORCID: https://orcid.org/0000-0002-1708-0137)
- Adel Alkhedaide (ORCID: https://orcid.org/0000-0002-5008-8421)
- Yu Cong (ORCID: https://orcid.org/0000-0001-5544-1303)
- M. M. Maqbool
Institutions
- Bahauddin Zakariya University (PK)
- University of Bradford (GB)
- COMSATS University Islamabad (PK)
- Taif University (SA)
- Chinese Academy of Sciences (CN)
- Changchun Institute of Applied Chemistry (CN)
Publication Details
- Journal
- Journal of Materials Chemistry C
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4tc05271d
- Citations
- 3
- Primary Topic
- Electrocatalysts for Energy Conversion
- Type
- article
- Field-Weighted Citation Impact
- 1.09