Literature DB >> 32078938

Evaluating the catalytic activity of transition metal dimers for the oxygen reduction reaction.

Zhao Liang1, Mingming Luo1, Mingwei Chen1, Chao Liu2, S Gouse Peera3, Xiaopeng Qi1, Juan Liu4, U Pramod Kumar1, Tongxiang Liang Tongxiang Liang5.   

Abstract

Various experimental investigation had proved that metal dimers possess excellent oxygen reduction reaction (ORR) activity compared to single metal atom catalysts, due to the synergistic effect exerted by two metal atoms. However, it is still unclear how the electrocatalytic activity is enhanced in a fundamental aspect. In this study, we systematically investigated five 3d transition metals (Fe, Co, Ni, Cu and Zn) by density functional theory (DFT) to explore the ability of metal dimers to catalyze the ORR. It is found that different combinations of different metal atoms have different adsorption strengths to oxygenated intermediates, which helps to screen suitable catalyst materials. The scaling relationship of the free energy of adsorption of oxygen-containing species was calculated for various metal-dimer systems. The classical volcanic diagram is derived, and it is found that the CoZnOH embedded nitrogen-doped graphene (the overpotential is 0.61 V) shows the best catalytic properties, and it is predicted that when the adsorption free energy of OH is equal to 0.95 eV, the optimal overpotential is 0.29 V. Electronic structure calculations show that the pairing of different metal atoms alters the d-band center which in turn change the adsorption properties and hence ORR catalytic performance.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Density functional theory; Oxygen reduction reaction; Transition metal dimer; Volcano plot

Year:  2020        PMID: 32078938     DOI: 10.1016/j.jcis.2020.02.034

Source DB:  PubMed          Journal:  J Colloid Interface Sci        ISSN: 0021-9797            Impact factor:   8.128


  1 in total

1.  Accelerated discovery of superoxide-dismutase nanozymes via high-throughput computational screening.

Authors:  Zhenzhen Wang; Jiangjiexing Wu; Jia-Jia Zheng; Xiaomei Shen; Liang Yan; Hui Wei; Xingfa Gao; Yuliang Zhao
Journal:  Nat Commun       Date:  2021-11-25       Impact factor: 14.919

  1 in total

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