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过渡金属表面上一氧化碳的光学与结构性质的关联

Correlating Optical and Structural Properties of CO on Transition Metal Surfaces.

作者信息

Ha Mai-Anh, Pashov Dimitar, van Schilfgaarde Mark

机构信息

Computational Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States.

Department of Physics, King's College London, Strand, London WC2R 2LS, U.K.

出版信息

J Phys Chem C Nanomater Interfaces. 2025 Mar 3;129(10):4923-4936. doi: 10.1021/acs.jpcc.4c07418. eCollection 2025 Mar 13.

Abstract

We present an optical study based on the quasiparticle self-consistent (QS ) approximation combining structural information taken from density functional theory (DFT) to elucidate spectral features of CO adsorbed on Pt(111) and Cu(111). Optical information and structural arrangement of the adsorbed CO are correlated by varying both site positions and CO coverage as compared to experimental studies (θ = 1/4 to θ = 1/2). This enables us to resolve key spectral features of both occupied and unoccupied molecular states at various adsorbate coverages, comparing theory to experiment. Using experimental data as benchmarks, we show the theory compares well with available data. Its predictive power provides a new path to infer information about the structure of CO from optical information and can help to predict the presence of other little understood adsorbates such as an OCCO dimer that may be relevant to mechanistic pathways for reduction of CO to high value C products. This new approach complements total energy calculations and also fills a void in DFT-based theory that is known to be an unreliable predictor of the energetics of CO on transition metal surfaces.

摘要

我们提出了一项基于准粒子自洽(QS)近似的光学研究,该研究结合了取自密度泛函理论(DFT)的结构信息,以阐明吸附在Pt(111)和Cu(111)上的CO的光谱特征。与实验研究(θ = 1/4至θ = 1/2)相比,通过改变位点位置和CO覆盖度,将吸附的CO的光学信息与结构排列相关联。这使我们能够在不同的吸附质覆盖度下解析占据和未占据分子态的关键光谱特征,并将理论与实验进行比较。以实验数据为基准,我们表明该理论与现有数据吻合良好。其预测能力为从光学信息推断CO的结构信息提供了一条新途径,并且有助于预测其他了解较少的吸附质的存在,例如可能与将CO还原为高价值C产物的机理途径相关的OCCO二聚体。这种新方法补充了总能计算,也填补了基于DFT的理论中的一个空白,该理论已知是过渡金属表面上CO能量的不可靠预测器。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1cd/11912529/066efdbe5d37/jp4c07418_0001.jpg

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