在TM@NiN2单原子催化剂上的电化学NO-to-NH3转换:一个DFT和机器学习调查
Fuwei Chen1, Yanlong Liu1, Nan Xia1
1Department of Physics, College of Sciences, Shihezi University, Shihezi, Xinjiang, 832000, China. gaoy@shzu.edu.cn.
Physical chemistry chemical physics : PCCP
|February 2, 2026
概括
研究人员将Zn@NiN2确定为电催化方法将一氧化 (NO) 降解为氨 (NH3) 的顶级催化剂. 这种先进的催化剂表现出卓越的性能,为环境清洁和化学合成提供了双重解决方案.
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:
- 通过电催化方法将一氧化 (NO) 降解为氨 (NH3),对于环境整治和化学生产至关重要.
- 开发高效率的催化剂是推动这项技术发展的关键.
研究的目的:
- 通过使用DFT和ML来选单原子催化剂的氧化还原反应 (NORR) 性能.
- 为了确定有效的NO电还原的有前途的候选者.
主要方法:
- 结合密度函数理论 (DFT) 和机器学习 (ML) 进行催化剂选.
- 多步骤协议评估催化剂稳定性,NO吸附,活性和选择性.
- 电子结构分析和巴德尔电荷分析.
主要成果:
- 已确定Zn@NiN2为NORR最有前途的催化剂.
- 在现实的条件下,达到0V的超低限制电位 (UL).
- ML模型将NORR活动与Qs值和εd位置联系起来.
结论:
- Zn@NiN2对NORR显示出异常的电催化活性.
- 催化剂性能归因于Zn-3d/NO-2p轨道杂交和电子介导.
- 为NORR.提供了一个设计先进电催化剂的框架.
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