Computational studies of electrochemical CO2 reduction on subnanometer transition metal clusters

文献情報

出版日 2014-08-12

DOI 10.1039/C4CP02690J

インパクトファクター 3.676

著者

Cong Liu, Haiying He, Peter Zapol, Larry A. Curtiss

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要旨

Computational studies of electrochemical reduction of CO2 to CO, HCOOH and CH4 were carried out using tetra-atomic transition metal clusters (Fe4, Co4, Ni4, Cu4 and Pt4) at the B3LYP level of theory. Novel catalytic properties were discovered for these subnanometer clusters, suggesting that they may be good candidate materials for CO2 reduction. The calculated overpotentials for producing CH4 are in the order, Co4 < Fe4 < Ni4 < Cu4 < Pt4, with both Co4 and Fe4 having overpotentials less than 1 V. Investigation of the effects of supports found that a Cu4 cluster on a graphene defect site has a limiting potential for producing CH4 comparable to that of a Cu (111) surface. However, due to the strong electronic interaction with the Cu4 cluster, the defective graphene support has the advantage of significantly increasing the limiting potentials for the reactions competing with CH4, such as the hydrogen evolution reaction (HER) and CO production.

掲載誌

Physical Chemistry Chemical Physics

CiteScore: 5.5

自己引用率: 10.3%

年間論文数: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.