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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.00609 |
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| _version_ | 1866911638671589376 |
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| author | Li, Jiahang Li, Suhang Yan, Chong Yu, Jiajun Liu, Qinzhuang Wang, Ruo-Ya Ma, Dongwei |
| author_facet | Li, Jiahang Li, Suhang Yan, Chong Yu, Jiajun Liu, Qinzhuang Wang, Ruo-Ya Ma, Dongwei |
| contents | The rational design of bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is essential for achieving efficient and cost-effective overall water splitting. Atomically dispersed transition-metal catalysts, including single-atom catalysts and dual-atom catalysts (DACs), have emerged as a prominent class of heterogeneous catalysts, in which coordination engineering plays a decisive role in tuning catalytic performance. Herein, we explore coordination-engineered bifunctional overall water splitting electrocatalysts using graphene-supported DACs (TM1TM2-C6-xNx) as model systems. By tuning C/N coordination and dual-metal combinations (Fe, Co, Ni, and Cu), a library of 228 structures was constructed. A three-step screening strategy, combining constant-charge and constant-potential density functional theory with kinetic analysis of proton-coupled electron transfer (PCET), identifies 24 highly active candidates (TM1TM2 = CoNi, CoCu and Co2) with mixed C/N coordination for OER. These catalysts exhibit overpotentials comparable to that of IrO2 and low PCET barriers (lower than 0.40 eV), among which 22 also show high HER activity. Machine learning reveals clear coordination-dependent structure-performance relationships. Such bifunctionality arises from coordination engineering that enables the simultaneous optimization of OER intermediate adsorption and the hydrogen binding strength for HER. This work establishes coordination engineering as an effective strategy for designing high-performance bifunctional dual-atom electrocatalysts for overall water splitting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00609 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Coordination Engineering of Dual-Atom Catalysts for Overall Water Splitting: Mechanistic Insights from Constant-Potential First-Principles and Machine Learning Li, Jiahang Li, Suhang Yan, Chong Yu, Jiajun Liu, Qinzhuang Wang, Ruo-Ya Ma, Dongwei Materials Science The rational design of bifunctional electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is essential for achieving efficient and cost-effective overall water splitting. Atomically dispersed transition-metal catalysts, including single-atom catalysts and dual-atom catalysts (DACs), have emerged as a prominent class of heterogeneous catalysts, in which coordination engineering plays a decisive role in tuning catalytic performance. Herein, we explore coordination-engineered bifunctional overall water splitting electrocatalysts using graphene-supported DACs (TM1TM2-C6-xNx) as model systems. By tuning C/N coordination and dual-metal combinations (Fe, Co, Ni, and Cu), a library of 228 structures was constructed. A three-step screening strategy, combining constant-charge and constant-potential density functional theory with kinetic analysis of proton-coupled electron transfer (PCET), identifies 24 highly active candidates (TM1TM2 = CoNi, CoCu and Co2) with mixed C/N coordination for OER. These catalysts exhibit overpotentials comparable to that of IrO2 and low PCET barriers (lower than 0.40 eV), among which 22 also show high HER activity. Machine learning reveals clear coordination-dependent structure-performance relationships. Such bifunctionality arises from coordination engineering that enables the simultaneous optimization of OER intermediate adsorption and the hydrogen binding strength for HER. This work establishes coordination engineering as an effective strategy for designing high-performance bifunctional dual-atom electrocatalysts for overall water splitting. |
| title | Coordination Engineering of Dual-Atom Catalysts for Overall Water Splitting: Mechanistic Insights from Constant-Potential First-Principles and Machine Learning |
| topic | Materials Science |
| url | https://arxiv.org/abs/2605.00609 |