Literature DB >> 20550380

Communications: Exceptions to the d-band model of chemisorption on metal surfaces: The dominant role of repulsion between adsorbate states and metal d-states.

Hongliang Xin1, Suljo Linic.   

Abstract

We show that there is a family of adsorbate-substrate systems that do not follow the trends in adsorption energies predicted by the d-band model. A physically transparent model is used to analyze this phenomenon. We found that these adsorbate-substrate pairs are characterized by the repulsive interaction of the substrate d-band with the renormalized adsorbate states. The exceptions to the d-band model are mainly associated with the adsorbates having almost completely filled valence shell, and the substrates with nearly fully occupied d-band, e.g., OH, F, or Cl adsorption on metals and alloys characterized by d(9) or d(10) substrate surface atoms.

Entities:  

Year:  2010        PMID: 20550380     DOI: 10.1063/1.3437609

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  12 in total

1.  Plasmonic-metal nanostructures for efficient conversion of solar to chemical energy.

Authors:  Suljo Linic; Phillip Christopher; David B Ingram
Journal:  Nat Mater       Date:  2011-11-23       Impact factor: 43.841

2.  High-performance Ag-Co alloy catalysts for electrochemical oxygen reduction.

Authors:  Adam Holewinski; Juan-Carlos Idrobo; Suljo Linic
Journal:  Nat Chem       Date:  2014-08-11       Impact factor: 24.427

3.  Towards the rational design of Pt-based alloy catalysts for the low-temperature water-gas shift reaction: from extended surfaces to single atom alloys.

Authors:  Yuqi Yang; Tonghao Shen; Xin Xu
Journal:  Chem Sci       Date:  2022-05-05       Impact factor: 9.969

4.  An improved d-band model of the catalytic activity of magnetic transition metal surfaces.

Authors:  Satadeep Bhattacharjee; Umesh V Waghmare; Seung-Cheol Lee
Journal:  Sci Rep       Date:  2016-11-03       Impact factor: 4.379

5.  Bayesian learning of chemisorption for bridging the complexity of electronic descriptors.

Authors:  Siwen Wang; Hemanth Somarajan Pillai; Hongliang Xin
Journal:  Nat Commun       Date:  2020-11-30       Impact factor: 14.919

6.  Machine learned features from density of states for accurate adsorption energy prediction.

Authors:  Victor Fung; Guoxiang Hu; P Ganesh; Bobby G Sumpter
Journal:  Nat Commun       Date:  2021-01-04       Impact factor: 14.919

7.  Active learning with non-ab initio input features toward efficient CO2 reduction catalysts.

Authors:  Juhwan Noh; Seoin Back; Jaehoon Kim; Yousung Jung
Journal:  Chem Sci       Date:  2018-04-17       Impact factor: 9.825

8.  Statistical learning goes beyond the d-band model providing the thermochemistry of adsorbates on transition metals.

Authors:  Rodrigo García-Muelas; Núria López
Journal:  Nat Commun       Date:  2019-10-15       Impact factor: 14.919

9.  Interaction of H2O with the Platinum Pt (001), (011), and (111) Surfaces: A Density Functional Theory Study with Long-Range Dispersion Corrections.

Authors:  Marietjie J Ungerer; David Santos-Carballal; Abdelaziz Cadi-Essadek; Cornelia G C E van Sittert; Nora H de Leeuw
Journal:  J Phys Chem C Nanomater Interfaces       Date:  2019-09-25       Impact factor: 4.126

10.  Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.

Authors:  Marc O J Jäger; Yashasvi S Ranawat; Filippo Federici Canova; Eiaki V Morooka; Adam S Foster
Journal:  ACS Comb Sci       Date:  2020-11-04       Impact factor: 3.784

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