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了解使用密度函数理论在Mo-Te-Nb-O M1催化剂上选择性氧化到烯酸的选择性氧化

Karnajit Sen¹, Ansgar Schäfer², Frank Rosowski^3,4

¹BASF SE, RGS/AD - B007, Carl-Bosch-Straße 38, 67056 Ludwigshafen am Rhein, GermanyBASF SE, RGS/AD - B007, Carl-Bosch-Straße 38, 67056 Ludwigshafen am Rhein, Germany.

|March 2, 2026

概括

这项研究研究了使用混合金属氧化物 (MMO) 催化剂将氧化成烯酸的过程. 研究人员确定了形成异醇的竞争途径,阻碍了烯酸生产,并开发了催化剂性能的预测模型.

科学领域:

不同质的催化剂.
量子化学是一种量子化学.
表面科学是一门科学.

背景情况:

混合金属氧化物 (MMO) 催化剂,特别是M1阶段 (Mo-Te-Nb-O),对于选择性氧化反应至关重要.
M1催化剂的复杂表面结构,具有各种金属位点和氧化状态,使理解它们的催化活性和选择性变得复杂.
氧化成烯酸是工业上重要的反应,在实现高选择性方面存在挑战.

研究的目的:

阐明在M1 MMO催化剂上选择性氧化成酸的反应机制.
识别和描述相互竞争的反应路径,特别是那些导致不需要的副产品的反应路径.
开发一个预测模型来估计M1催化剂C-H激活化学中的激活障碍.

主要方法:

使用混合密度函数理论 (DFT) 方法的量子化学计算.
采用多层集群模型来表示M1阶段的催化活性位点.
建立关键中间体的最低能量路径:烯,醇和烯蛋白.
分析异醇的形成作为一种非选择性的途径.

主要成果:

来自基的异醇的形成具有较低的激活屏障,在的C-H激活后与烯形成竞争.
烯形成之后,易于激活基位置到阿克罗莱因,后者进一步氧化为烯酸.
开发了一个一般的线性缩放关系,以使用四个能量描述符 (E_H,E_BDE,E_int^TS,E_MO) 来估计M1催化剂上的CH激活障碍.

结论:

该研究澄清了M1 MMO催化剂上氧化到酸的反应网络,突出了异醇形成在减少选择性的作用.
开发的线性缩放关系为预测和设计M1催化剂提供了有价值的工具,用于增强C-H激活反应的活性和选择性.
了解这些机制细节和开发预测模型对于推进选择性催化氧化过程至关重要.

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