Premium
Toward Excellence of Transition Metal‐Based Catalysts for CO 2 Electrochemical Reduction: An Overview of Strategies and Rationales
Author(s) -
Li Mengran,
Garg Sahil,
Chang Xiaoxia,
Ge Lei,
Li Liye,
Konarova Muxina,
Rufford Thomas E.,
Rudolph Victor,
Wang Geoff
Publication year - 2020
Publication title -
small methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.66
H-Index - 46
ISSN - 2366-9608
DOI - 10.1002/smtd.202000033
Subject(s) - catalysis , transition metal , nanotechnology , electrochemistry , oxide , rational design , selective catalytic reduction , materials science , heterogeneous catalysis , chemical physics , chemistry , electrode , organic chemistry , metallurgy
Rational modulations of interactions between the catalyst surface and intermediates are challenging but extremely important to achieve an efficient and selective electrochemical CO 2 reduction (CO 2 R). Current CO 2 R catalyst design remains inefficient because of a gap between existing practical design paradigms and theoretical studies in catalysis. This review attempts to mitigate this gap through a critical discussion of the correlations between recent strategies to develop transition metal‐based catalysts and the underlying rationales and mechanisms. These strategies include surface engineering, the introduction of heterogeneous atoms, and dimension control, and can be implemented by tactics such as controlling catalyst surface facets, surface tethering, alloying, inducing strains, oxide derivation, molecular scaffolding, and nanostructuring. How these tactics are able to tailor the electronic structure, adsorption geometry, density of active sites, and local environment of catalyst to achieve an efficient and selective CO 2 R is described. This review concludes with a discussion of the key research needs in this field such as the surface proton formation and transfer involved in CO 2 R, the roles of mass‐transfer or electrode kinetics in CO 2 R catalysis, development of robust, standardized catalyst testing protocols, and application of machine learning and high‐throughput experiment to accelerate catalyst screening processes.