Literature DB >> 24919964

Fast prediction of adsorption properties for platinum nanocatalysts with generalized coordination numbers.

Federico Calle-Vallejo1, José I Martínez, Juan M García-Lastra, Philippe Sautet, David Loffreda.   

Abstract

Platinum is a prominent catalyst for a multiplicity of reactions because of its high activity and stability. As Pt nanoparticles are normally used to maximize catalyst utilization and to minimize catalyst loading, it is important to rationalize and predict catalytic activity trends in nanoparticles in simple terms, while being able to compare these trends with those of extended surfaces. The trends in the adsorption energies of small oxygen- and hydrogen-containing adsorbates on Pt nanoparticles of various sizes and on extended surfaces were analyzed through DFT calculations by making use of the generalized coordination numbers of the surface sites. This simple and predictive descriptor links the geometric arrangement of a surface to its adsorption properties. It generates linear adsorption-energy trends, captures finite-size effects, and provides more accurate descriptions than d-band centers and usual coordination numbers. Unlike electronic-structure descriptors, which require knowledge of the densities of states, it is calculated manually. Finally, it was shown that an approximate equivalence exists between generalized coordination numbers and d-band centers.
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  adsorption energy; coordination numbers; d-band center; nanoparticles; platinum

Year:  2014        PMID: 24919964     DOI: 10.1002/anie.201402958

Source DB:  PubMed          Journal:  Angew Chem Int Ed Engl        ISSN: 1433-7851            Impact factor:   15.336


  25 in total

1.  Introducing structural sensitivity into adsorption-energy scaling relations by means of coordination numbers.

Authors:  Federico Calle-Vallejo; David Loffreda; Marc T M Koper; Philippe Sautet
Journal:  Nat Chem       Date:  2015-04-06       Impact factor: 24.427

2.  Hydrogen quenches the size effects in carbon clusters.

Authors:  José I Martínez; Julio A Alonso
Journal:  Phys Chem Chem Phys       Date:  2019-05-22       Impact factor: 3.676

3.  How Au Outperforms Pt in the Catalytic Reduction of Methane towards Ethane and Molecular Hydrogen.

Authors:  José I Martínez; Federico Calle-Vallejo; Pedro L de Andrés
Journal:  Top Catal       Date:  2018-05-15       Impact factor: 2.910

Review 4.  Ab Initio Machine Learning in Chemical Compound Space.

Authors:  Bing Huang; O Anatole von Lilienfeld
Journal:  Chem Rev       Date:  2021-08-13       Impact factor: 60.622

5.  Making the hydrogen evolution reaction in polymer electrolyte membrane electrolysers even faster.

Authors:  Jakub Tymoczko; Federico Calle-Vallejo; Wolfgang Schuhmann; Aliaksandr S Bandarenka
Journal:  Nat Commun       Date:  2016-03-10       Impact factor: 14.919

6.  Platinum clusters with precise numbers of atoms for preparative-scale catalysis.

Authors:  Takane Imaoka; Yuki Akanuma; Naoki Haruta; Shogo Tsuchiya; Kentaro Ishihara; Takeshi Okayasu; Wang-Jae Chun; Masaki Takahashi; Kimihisa Yamamoto
Journal:  Nat Commun       Date:  2017-09-25       Impact factor: 14.919

7.  Why conclusions from platinum model surfaces do not necessarily lead to enhanced nanoparticle catalysts for the oxygen reduction reaction.

Authors:  Federico Calle-Vallejo; Marcus D Pohl; David Reinisch; David Loffreda; Philippe Sautet; Aliaksandr S Bandarenka
Journal:  Chem Sci       Date:  2016-12-06       Impact factor: 9.825

8.  Density Functional Theory and Machine Learning Description and Prediction of Oxygen Atom Chemisorption on Platinum Surfaces and Nanoparticles.

Authors:  David S Rivera Rocabado; Yusuke Nanba; Michihisa Koyama
Journal:  ACS Omega       Date:  2021-07-01

9.  The stability and catalytic activity of W13@Pt42 core-shell structure.

Authors:  Jin-Rong Huo; Xiao-Xu Wang; Lu Li; Hai-Xia Cheng; Yan-Jing Su; Ping Qian
Journal:  Sci Rep       Date:  2016-10-19       Impact factor: 4.379

10.  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

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