DFT insights into electrocatalytic CO2 reduction to methanol on α-Fe2O3(0001) surfaces

Literature Information

Publication Date 2020-04-20
DOI 10.1039/C9CP06453B
Impact Factor 3.676
Authors

Nandha Kumar, Nicola Seriani, Ralph Gebauer


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Abstract

Electrocatalytic reduction of CO2 to manufacture fuels and other useful chemicals is one of the appealing methods to reuse CO2. Herein, electrocatalytic CO2 reduction on a model α-Fe2O3(0001) surface catalyst has been investigated by means of density functional theory. This systematic study, involving 20 reaction intermediates and 63 distinct elementary reaction steps, has allowed the identification of a novel mechanism for the decomposition of the key intermediate *COOH. Methanol is the preferred product, with an overpotential of 0.8 V, over carbon monoxide (CO), formic acid (HCOOH), and formaldehyde (CH2O). Formaldehyde formed on the surface will be converted into methanol. This work demonstrates the need for a complete investigation of possible pathways to find the most favourable one, beyond chemical intuition. Moreover, it suggests that hematite could be an interesting material for CO2 reduction.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
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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.

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