当前位置: X-MOL 学术Top. Catal. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Absorption of Hydrocarbons on Palladium Catalysts: From Simple Models Towards Machine Learning Analysis of X-ray Absorption Spectroscopy Data
Topics in Catalysis ( IF 2.8 ) Pub Date : 2020-01-11 , DOI: 10.1007/s11244-020-01221-2
Oleg A. Usoltsev , Aram L. Bugaev , Alexander A. Guda , Sergey A. Guda , Alexander V. Soldatov

Element selectivity and possibilities for in situ and operando applications make X-ray absorption spectroscopy a powerful tool for structural characterization of catalysts. While determination of coordination numbers and interatomic distances from extended spectral region is rather straightforward, analysis of X-ray absorption near-edge structure (XANES) spectra remains a highly debated and topical problem. The latter region of spectra is shaped depending on the local 3D geometry and electronic structure. However, there is no straightforward procedure for the unambiguous extraction of these parameters. This work gives a critical vision on the amount of information that can be practically extracted from Pd K-edge XANES spectra measured under in situ and operando conditions, in which adsorption of reactive molecules at the surface of palladium with further formation of subsurface and bulk palladium carbides are expected. We investigate how particle size, concentration of carbon impurities, and their distribution in the bulk and at the surface of palladium particles affect Pd K-edge XANES features and to which extend they should be implemented in the theoretical model to adequately reproduce experimental data. Then, we show how the step-by-step increasing the complexity of the theoretical model improves the agreement with experiment. Finally, we suggest a set of formal descriptors relevant to possible structural diversity and construct a library of theoretical spectra for machine-learning-based analysis of the experimental data.



中文翻译:

钯催化剂上碳氢化合物的吸收:从简单模型到X射线吸收光谱数据的机器学习分析

元素选择性以及原位和操作应用的可能性使X射线吸收光谱法成为催化剂结构表征的强大工具。尽管确定配位数和距扩展光谱区的原子间距离相当简单,但是X射线吸收近边缘结构(XANES)光谱的分析仍然是一个备受争议和关注的问题。光谱的后一个区域的形状取决于局部3D几何形状和电子结构。但是,没有明确的方法可直接提取这些参数。这项工作对可以从Pd K中实际提取的信息量给出了批判性的见解。边缘XANES光谱是在原位和操作条件下测得的,其中反应性分子在钯表面的吸附会进一步形成地下和块状碳化钯。我们研究了粒径,碳杂质浓度以及它们在钯粒子表面和表面的分布如何影响Pd K先进的XANES功能及其扩展范围应在理论模型中实现,以充分再现实验数据。然后,我们展示了逐步增加理论模型的复杂性如何改善与实验的一致性。最后,我们建议了一组与可能的结构多样性相关的形式化描述符,并构建了一个理论光谱库,用于基于机器学习的实验数据分析。

更新日期:2020-01-11
down
wechat
bug