A Revised High‐Throughput Screening Model on Oxygen Reduction Reaction Over Dual Atom Catalysts Based on the Axial Pre‐Adsorption and O<sub>2</sub> Adsorption

Abstract Dual‐atom catalysts (DACs) often exhibit superior electrocatalytic activity, due to their versatile combinations and synergistic effects. However, the neglect of both dynamic axial adsorption of the active site upon working potential and the reactant adsorption as a rate‐determining step hinders the establishment of an accurate high‐throughput screening strategy. Here, the oxygen reduction reaction (ORR) of 42 kinds of 3d–3d metal DACs by density functional theory (DFT) calculations are systematiclly investigated and demonstrated that the ORR kinetics can be limited by O 2 * adsorption besides the proton–electron transfer step and the active center of DACs may be reconstructed by axial pre‐adsorption of intermediates under working potential. Therefore, the ORR volcano plot is proposed by using both the O 2 * and OH* adsorption as activity descriptors. Then, a high‐throughput screening method is constructed and 38 promising ORR DACs are screened out from 267 DACs containing 3d, 4d, or 5d metals. Importantly, the previously unexplored MnCoN 6 DAC is also experimentally synthesized, and exhibits ultrahigh ORR activity outperforming Pt/C, perfectly matching with theoretical prediction. In short, this work not only proposes a volcano plot‐based high‐throughput screening method but also provides a proof‐of‐concept of experimental verification of theoretical prediction to heuristically design electrocatalysts for other reactions.

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Publication Details

Journal
Advanced Energy Materials
Published
2024-10-16
DOI
https://doi.org/10.1002/aenm.202403524
Citations
12
Primary Topic
Electrocatalysts for Energy Conversion
Type
article
Field-Weighted Citation Impact
1.23

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article

A Revised High‐Throughput Screening Model on Oxygen Reduction Reaction Over Dual Atom Catalysts Based on the Axial Pre‐Adsorption and O2 Adsorption

Danyang Li, Panpan Sun, Dapeng Cao, Jimmy Yun et al.
12 citations
Advanced Energy Materials
Electrocatalysts for Energy Conversion
1.23
article

A Revised High‐Throughput Screening Model on Oxygen Reduction Reaction Over Dual Atom Catalysts Based on the Axial Pre‐Adsorption and O2 Adsorption

Danyang Li, Panpan Sun, Dapeng Cao, Jimmy Yun, Haoxiang Xu
article en
12 citations

Abstract

Abstract Dual‐atom catalysts (DACs) often exhibit superior electrocatalytic activity, due to their versatile combinations and synergistic effects. However, the neglect of both dynamic axial adsorption of the active site upon working potential and the reactant adsorption as a rate‐determining step hinders the establishment of an accurate high‐throughput screening strategy. Here, the oxygen reduction reaction (ORR) of 42 kinds of 3d–3d metal DACs by density functional theory (DFT) calculations are systematiclly investigated and demonstrated that the ORR kinetics can be limited by O 2 * adsorption besides the proton–electron transfer step and the active center of DACs may be reconstructed by axial pre‐adsorption of intermediates under working potential. Therefore, the ORR volcano plot is proposed by using both the O 2 * and OH* adsorption as activity descriptors. Then, a high‐throughput screening method is constructed and 38 promising ORR DACs are screened out from 267 DACs containing 3d, 4d, or 5d metals. Importantly, the previously unexplored MnCoN 6 DAC is also experimentally synthesized, and exhibits ultrahigh ORR activity outperforming Pt/C, perfectly matching with theoretical prediction. In short, this work not only proposes a volcano plot‐based high‐throughput screening method but also provides a proof‐of‐concept of experimental verification of theoretical prediction to heuristically design electrocatalysts for other reactions.

Advanced Energy MaterialsVol. 15(9)
UNSW Sydney (AU), Beijing University of Chemical Technology (CN)
National Natural Science Foundation of China, Key Technologies Research and Development Program
Openalex Percentile: Top 23%
Electrocatalysts for Energy Conversion
1.23
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