作为NORR电催化剂在G/BP异构结构上固定单个过渡金属原子的设计和选:一个DFT和机器学习研究
Xiang Zhong1, Xinyi Han1, Xiaowen Sun1
1School of Science, Jiangsu Ocean University, Lianyungang 222005, China.
Journal of hazardous materials
|February 14, 2026
概括
氧化 (NOx) 的电催化降解将有害气体转化为氨 (NH3). 这项研究确定铁 (Fe) 和 (Rh) 定在石墨烯和蓝 (G/BP) 异构结构上,是该过程的高度活跃和选择性的催化剂.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 计算化学计算化学
背景情况:
- 在环境条件下,电催化氧化还原反应 (NORR) 为将危险的NO气体转化为有价值的氨 (NH3) 提供了一个可持续的途径.
- 然而,缓慢的反应动力学和低的法拉第效率阻碍了NORR的实际应用,需要开发先进的催化剂.
研究的目的:
- 系统地研究在石墨烯和蓝色 (TM-G/BP) 异构结构上的过渡金属原子作为高效的NORR催化剂.
- 阐明NORR活动的潜在机制,并开发催化剂性能的预测模型.
主要方法:
- 用密度函数理论 (DFT) 的计算来检查一系列TM-G/BP异构结构.
- 进行了电子结构分析,以了解催化剂活性部位和NO分子之间的相互作用.
- 使用机器学习方法 (SISSO) 来构建用于预测NORR活动 (UL) 的描述符.
主要成果:
- Fe-G/BP和Rh-G/BP异构表现出高活性和对NORR的选择性,限制电位 (UL) 分别为0.31V和0.46V.
- 电子分析显示TM-3d/4d轨道和NO-2p轨道之间存在强烈的杂交,这对于激活吸附的NO至关重要.
- 开发的机器学习描述器实现了高相关系数 (0.90) 和低平均平方误差 (0.06) 预测UL.
结论:
- Fe-G/BP和Rh-G/BP异构结构是高效NORR的有希望的电催化剂.
- 这项研究为NO-to-NH3转化提供了宝贵的机械洞察力,突出了电子结构和轨道杂交的作用.
- 机器学习有效地预测NORR催化剂活动,加速新材料的发现.
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