Literature DB >> 28157336

Orbitalwise Coordination Number for Predicting Adsorption Properties of Metal Nanocatalysts.

Xianfeng Ma1, Hongliang Xin1.   

Abstract

We present the orbitalwise coordination number CN^{α} (α=s or d) as a reactivity descriptor for metal nanocatalysts. With the noble metal Au (5d^{10}6s^{1}) as a specific case, the CN^{s} computed using the two-center s-electron hopping integrals to neighboring atoms provides an accurate and robust description of the trends in CO and O adsorption energies on extended surfaces terminated with different facets and nanoparticles of varying size and shape, outperforming existing bond-counting methods. Importantly, the CN^{s} has a solid physiochemical basis via a direct connection to the moment characteristics of the projected density of states onto the s orbital of a Au adsorption site. Furthermore, the CN^{s} shows promise as a viable descriptor for predicting adsorption properties of Au alloy nanoparticles with size-dependent lattice strains and coinage metal ligands.

Entities:  

Year:  2017        PMID: 28157336     DOI: 10.1103/PhysRevLett.118.036101

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  11 in total

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Authors:  Mingyu Wan; Han Yue; Jaime Notarangelo; Hongfu Liu; Fanglin Che
Journal:  JACS Au       Date:  2022-06-02

2.  Optimum Particle Size for Gold-Catalyzed CO Oxidation.

Authors:  Jin-Xun Liu; Ivo A W Filot; Yaqiong Su; Bart Zijlstra; Emiel J M Hensen
Journal:  J Phys Chem C Nanomater Interfaces       Date:  2018-03-28       Impact factor: 4.126

3.  Covalent triazine framework modified with coordinatively-unsaturated Co or Ni atoms for CO2 electrochemical reduction.

Authors:  Panpan Su; Kazuyuki Iwase; Takashi Harada; Kazuhide Kamiya; Shuji Nakanishi
Journal:  Chem Sci       Date:  2018-03-19       Impact factor: 9.825

4.  Determining the adsorption energies of small molecules with the intrinsic properties of adsorbates and substrates.

Authors:  Wang Gao; Yun Chen; Bo Li; Shan-Ping Liu; Xin Liu; Qing Jiang
Journal:  Nat Commun       Date:  2020-03-05       Impact factor: 14.919

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.  In-situ visualization of solute-driven phase coexistence within individual nanorods.

Authors:  Fariah Hayee; Tarun C Narayan; Neel Nadkarni; Andrea Baldi; Ai Leen Koh; Martin Z Bazant; Robert Sinclair; Jennifer A Dionne
Journal:  Nat Commun       Date:  2018-05-02       Impact factor: 14.919

9.  Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis.

Authors:  James Dean; Michael G Taylor; Giannis Mpourmpakis
Journal:  Sci Adv       Date:  2019-09-13       Impact factor: 14.136

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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