通过轨道分析和监督机器学习从分子库的Cr前端XANES解读菲利普斯催化剂
David Trummer1, Keith Searles1, Alexander Algasov2,3
1Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, CH-8093 Zürich, Switzerland.
Journal of the American Chemical Society
|May 11, 2021
概括
这项研究开发了一种使用机器学习和定制的 (Cr) 分子复合物的新方法来分析催化剂的表面位点. 它准确地预测了菲利普斯催化剂中的氧化状态和局部环境.
科学领域:
- 不同质的催化
- 材料科学
- 光谱学
- 计算化学
背景情况:
- 在几十年的研究之后,对异质催化剂,特别是菲利普斯催化剂 (CrO3 / SiO2) 的表面位点的特征仍然是一个重大挑战.
- 现有的CrK边缘X射线吸收近边缘结构 (XANES) 分析方法使用的参考材料并不准确地代表实际表面位置.
研究的目的:
- 为量身定制的分子复合物创建一个全面的CrK边缘XANES光谱库.
- 建立准确的描述器来预测氧化状态,协调环境和连接体类型.
- 应用这些描述符来确定不同过程阶段在菲利普斯催化剂中的位点分布.
主要方法:
- 合成并描述了一系列具有不同氧化状态,协调和连接体强度的分子Cr复合物.
- 收集了实验CrK边缘XANES光谱并分析了前边缘特征以了解电子过渡.
- 创建了潜在活跃站点的理论XANES光谱,并训练了一个ExtraTrees机器学习算法.
主要成果:
- 对XANES前端特征的定量分析揭示了电子交换相互作用的洞察力.
- 机器学习模型使用光谱描述器成功预测了Cr氧化状态,原子间距离和配体类型.
- 菲利普斯催化剂分析显示,在暴露于二氧化碳后,从 (VI) 过渡到 (II) 和 (III),在暴露于乙烯后, (III) 具有残留的 (VI).
结论:
- 一种结合实验和理论XANES光谱与机器学习的新方法可以精确地描述催化剂表面位置.
- 该研究提供了在不同条件下在菲利普斯催化剂中对物种和位点分布的详细了解.
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