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绘制原子碳与过渡金属表面的相互作用

Charting the Atomic C Interaction with Transition Metal Surfaces.

作者信息

Piqué Oriol, Koleva Iskra Z, Bruix Albert, Viñes Francesc, Aleksandrov Hristiyan A, Vayssilov Georgi N, Illas Francesc

机构信息

Departament de Ciència de Materials i Química Física & Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, c/ Martí i Franquès 1, 08028 Barcelona, Spain.

Faculty of Chemistry and Pharmacy, University of Sofia, 1126 Sofia, Bulgaria.

出版信息

ACS Catal. 2022 Jul 15;12(15):9256-9269. doi: 10.1021/acscatal.2c01562. eCollection 2022 Aug 5.

Abstract

Carbon interaction with transition metal (TM) surfaces is a relevant topic in heterogeneous catalysis, either for its poisoning capability, for the recently attributed promoter role when incorporated in the subsurface, or for the formation of early TM carbides, which are increasingly used in catalysis. Herein, we present a high-throughput systematic study, adjoining thermodynamic kinetic evidence obtained by extensive density functional calculations on surface models (324 diffusion barriers located on 81 TM surfaces in total), which provides a navigation map of these interactions in a holistic fashion. Correlation between previously proposed electronic descriptors and ad/absorption energies has been tested, with the -band center being found the most suitable one, although machine learning protocols also underscore the importance of the surface energy and the site coordination number. Descriptors have also been tested for diffusion barriers, with ad/absorption energies and the difference in energy between minima being the most appropriate ones. Furthermore, multivariable, polynomial, and random forest regressions show that both thermodynamic and kinetic data are better described when using a combination of different descriptors. Therefore, looking for a single perfect descriptor may not be the best quest, while combining different ones may be a better path to follow.

摘要

碳与过渡金属(TM)表面的相互作用是多相催化中的一个重要课题,这是由于其具有中毒能力、最近被认为当掺入次表面时具有促进作用,或者是由于早期TM碳化物的形成,这些碳化物在催化中越来越多地被使用。在此,我们展示了一项高通量系统研究,结合了通过对表面模型进行广泛的密度泛函计算获得的热力学和动力学证据(总共在81个TM表面上确定了324个扩散势垒),该研究以整体方式提供了这些相互作用的导航图。我们测试了先前提出的电子描述符与吸附/吸收能量之间的相关性,发现-带中心是最合适的描述符,尽管机器学习协议也强调了表面能和位点配位数的重要性。我们还测试了描述符对扩散势垒的适用性,发现吸附/吸收能量以及最小值之间的能量差是最合适的描述符。此外,多变量、多项式和随机森林回归表明,当使用不同描述符的组合时,热力学和动力学数据都能得到更好的描述。因此,寻找单一的完美描述符可能不是最佳选择,而组合不同的描述符可能是更好的途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02e/9880994/03a2185e3856/cs2c01562_0002.jpg

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