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Double‐Dependence Correlations in Graphdiyne‐Supported Atomic Catalysts to Promote CO2RR toward the Generation of C2 Products

Developing efficient and stable atomic catalysts (ACs) to achieve high faradaic efficiency and selectivity of C2 products is a significant challenge for research on the CO2 reduction reaction (CO2RR). Although significant efforts have been devoted to this endeavor, the understanding of C2 pathways a...

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Published in:Advanced energy materials 2023-02, Vol.13 (7), p.n/a
Main Authors: Sun, Mingzi, Wong, Hon Ho, Wu, Tong, Lu, Qiuyang, Lu, Lu, Chan, Cheuk Hei, Chen, Baian, Dougherty, Alan William, Huang, Bolong
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container_title Advanced energy materials
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Wong, Hon Ho
Wu, Tong
Lu, Qiuyang
Lu, Lu
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Chen, Baian
Dougherty, Alan William
Huang, Bolong
description Developing efficient and stable atomic catalysts (ACs) to achieve high faradaic efficiency and selectivity of C2 products is a significant challenge for research on the CO2 reduction reaction (CO2RR). Although significant efforts have been devoted to this endeavor, the understanding of C2 pathways and the influences of metal selection and active sites on the CO2RR still remain unclear. Herein, this work presents a comprehensive theoretical exploration of full C2 reaction pathway mapping based on graphdiyne (GDY)‐supported ACs with considerations of different metals and active sites for the first time. This work demonstrates the integrated large‐small cycle mechanism to explain the challenges for C2 product generation, where the double‐dependence correlation with metal and active sites is identified. A series of novel transition metal based GDY‐SACs, GDY‐Pr, and GDY‐Pm SACs are demonstrated as promising electrocatalysts to generate CH3CH2OH, CH3COOH, CH3CHO, and CH2OHCH2OH while the formation of C2H4 is very difficult for all GDY‐ACs. First‐principle machine learning predicts the reaction energy for the first time, where the adsorptions of the intermediates are critical to achieving accurate predictions of multi‐carbon products. This work supplies an advanced understanding of the complicated CO2RR mechanisms, which is expected to aid the development of novel atomic catalysts for efficient C2 product generation. Understanding the CO2 reduction reaction's (CO2RR) mechanism is highly significant for developing efficient atomic catalysts with high selectivity. Through comprehensive explorations of the C2 product formation on a graphdiyne‐supported atomic catalyst, a double‐correlation dependence is revealed to demonstrate reaction trends of CO2RRs regarding elements and active sites. This work aids the design of novel atomic catalysts for the CO2RR.
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First‐principle machine learning predicts the reaction energy for the first time, where the adsorptions of the intermediates are critical to achieving accurate predictions of multi‐carbon products. This work supplies an advanced understanding of the complicated CO2RR mechanisms, which is expected to aid the development of novel atomic catalysts for efficient C2 product generation. Understanding the CO2 reduction reaction's (CO2RR) mechanism is highly significant for developing efficient atomic catalysts with high selectivity. Through comprehensive explorations of the C2 product formation on a graphdiyne‐supported atomic catalyst, a double‐correlation dependence is revealed to demonstrate reaction trends of CO2RRs regarding elements and active sites. 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subjects atomic catalysts
C 2 products
carbon dioxide reduction
Catalysts
Chemical reduction
double‐dependence correlation
Electrocatalysts
graphdiyne
Machine learning
Selectivity
Transition metals
title Double‐Dependence Correlations in Graphdiyne‐Supported Atomic Catalysts to Promote CO2RR toward the Generation of C2 Products
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