Atomic and molecular adsorption on Fe(110)

SURFACE SCIENCE

卷:667

Article

Xu, Lang¹  Kirvassilis, Demetrios¹  Bai, Yunhai¹  Mavrikakis, Manos¹ 

[1] Univ Wisconsin, Dept Chem & Biol Engn, Madison, WI 53706 USA

关键词: Density functional theory;    Iron;    Adsorption;    Catalysis;    Diffusion;   

DOI  :  10.1016/j.susc.2017.09.002

来源: Elsevier

【 摘 要 】

Iron is the principal catalyst for the ammonia synthesis process and the Fischer-Tropsch process, as well as many other heterogeneously catalyzed reactions. It is thus of fundamental importance to understand the interactions between the iron surface and various reaction intermediates. Here, we present a systematic study of atomic and molecular adsorption behavior over Fe(110) using periodic, self-consistent density functional theory (DFT-GGA) calculations. The preferred binding sites, binding energies, and the corresponding surface deformation energies of five atomic species (H, C, N, O, and S), six molecular species (NH3, CH4, N-2, CO, HCN, and NO), and eleven molecular fragments (CH, CH2, CH3, NH, NH2, OH, CN, COH, HCO, NOH, and HNO) were determined on the Fe(llO) surface at a coverage of 0.25 monolayer. The binding strengths calculated using the PW91 functional decreased in the following order: C>CH>N>O>S>NH>COH>CN>CH2HTC/SUBTAG > FORTITLEHTC_RETAINNOH>OH>HNO>HCO>NH2HTC/SUBTAG > FORTITLEHTC_RETAINH>NO>HCN>CH3HTC/SUBTAG > FORTITLEHTC_RETAINCO>N2HTC/SUBTAG > FORTITLEHTC_RETAINNH(3). No stable binding structures were observed for CH4. The estimated diffusion barriers and pathways, as well as the adsorbate-surface and intramolecular vibrational modes of all the adsorbates at their preferred binding sites, were identified. Using the calculated adsorption energetics, we constructed the potential energy surfaces for a few surface reactions including the decomposition of methane, ammonia, dinitrogen, carbon monoxide, and nitric oxide. These potential energy surfaces provide valuable insight into the ability of Fe(110) to catalyze common elementary steps. (c) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

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