机器学习引导的单个原子促进了无贵金属水电解的氧化
Jaehyun Kim1, Ik Seon Kwon2, Jiheon Lim1,3
1Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, Republic of Korea.
Nature communications
|January 29, 2026
概括
在NiFe氧化中原子分散的 (W1-NiFeOOH) 显著降低了氧演化反应的超电位. 这种无贵金属的催化剂在水电解方面表现出高活性和稳定性,提供了可持续的能源解决方案.
科学领域:
- 电化学 电化学 电化学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 有效的水电解需要电催化剂来降低氧演化反应 (OER) 的过量的潜力.
- 贵金属催化剂是有效的,但稀缺,容易出水.
- 非贵金属替代品往往表现出有限的内在活性.
研究的目的:
- 为了确定一个具有成本效益的,无贵金属的OER催化剂.
- 探索NiFe氧化 (W1-NiFeOOH) 中的原子分散作为潜在的OER催化剂.
- 为催化剂设计开发一个集成的计算-实验工作流程.
主要方法:
- 利用基于等价变压器的机器学习原子间潜力来预测吸附能.
- 选了3,976个单原子结合金属氧化氧化物配置,以提名W1-NiFeOOH.
- 通过循环电沉积合成了W1-NiFeOOH,并使用现场光谱和密度函数理论 (DFT) 计算验证了其性能.
主要成果:
- 在2.0V时,W1-NiFeOOH实现了13.1A cm-2的高电流密度.
- 催化剂表现出了显著的稳定性,在性交换膜水电解中运行了500小时.
- 发现表面下W促进剂在Ni-O-Fe位点上诱导协同电子再分配,促进了去质子化步骤.
结论:
- 在NiFe氧化中原子分散的是一种高度活跃和稳定的无贵金属OER催化剂.
- 综合计算实验方法为设计可持续能源催化剂提供了有效的蓝图.
- 通过在活性位点修改电子结构,W促进剂增强了催化活性.
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