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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 |
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creator | Sun, Mingzi Wong, Hon Ho Wu, Tong Lu, Qiuyang Lu, Lu Chan, Cheuk Hei 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. |
doi_str_mv | 10.1002/aenm.202203858 |
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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.</description><identifier>ISSN: 1614-6832</identifier><identifier>EISSN: 1614-6840</identifier><identifier>DOI: 10.1002/aenm.202203858</identifier><language>eng</language><publisher>Weinheim: Wiley Subscription Services, Inc</publisher><subject>atomic catalysts ; C 2 products ; carbon dioxide reduction ; Catalysts ; Chemical reduction ; double‐dependence correlation ; Electrocatalysts ; graphdiyne ; Machine learning ; Selectivity ; Transition metals</subject><ispartof>Advanced energy materials, 2023-02, Vol.13 (7), p.n/a</ispartof><rights>2022 Wiley‐VCH GmbH</rights><rights>2023 Wiley‐VCH GmbH</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-2526-2002</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Sun, Mingzi</creatorcontrib><creatorcontrib>Wong, Hon Ho</creatorcontrib><creatorcontrib>Wu, Tong</creatorcontrib><creatorcontrib>Lu, Qiuyang</creatorcontrib><creatorcontrib>Lu, Lu</creatorcontrib><creatorcontrib>Chan, Cheuk Hei</creatorcontrib><creatorcontrib>Chen, Baian</creatorcontrib><creatorcontrib>Dougherty, Alan William</creatorcontrib><creatorcontrib>Huang, Bolong</creatorcontrib><title>Double‐Dependence Correlations in Graphdiyne‐Supported Atomic Catalysts to Promote CO2RR toward the Generation of C2 Products</title><title>Advanced energy materials</title><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.</description><subject>atomic catalysts</subject><subject>C 2 products</subject><subject>carbon dioxide reduction</subject><subject>Catalysts</subject><subject>Chemical reduction</subject><subject>double‐dependence correlation</subject><subject>Electrocatalysts</subject><subject>graphdiyne</subject><subject>Machine learning</subject><subject>Selectivity</subject><subject>Transition metals</subject><issn>1614-6832</issn><issn>1614-6840</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNo9kMtKw0AYhQdRsNRuXQ-4Tp1LrsuS1ipUK1XXw2TmD01JMnEyQbLTN_AZfRITKz2b_8LHOXAQuqZkTglhtxLqas4IY4THQXyGJjSkvhfGPjk_7ZxdolnbHsggP6GE8wn6WpouK-Hn83sJDdQaagU4NdZCKV1h6hYXNV5b2ex10dcj99I1jbEONF44UxUKp9LJsm9di53Bz9ZUxg0WW7bbDY8PaTV2e8BrqMH-WWKT45SNpO6Ua6_QRS7LFmb_c4re7lav6b232a4f0sXGaxjnsad5nDBQmYIwHyUJ0TSHPIuCXGoWJhkhsZ_pRCWKRoHv5yQmOsggC6XKk4RP0c3Rt7HmvYPWiYPpbD1EChZFEffHgIFKjtRHUUIvGltU0vaCEjHWLMaaxalmsVg9PZ4u_gvn7Xf_</recordid><startdate>20230217</startdate><enddate>20230217</enddate><creator>Sun, Mingzi</creator><creator>Wong, Hon Ho</creator><creator>Wu, Tong</creator><creator>Lu, Qiuyang</creator><creator>Lu, Lu</creator><creator>Chan, Cheuk Hei</creator><creator>Chen, Baian</creator><creator>Dougherty, Alan William</creator><creator>Huang, Bolong</creator><general>Wiley Subscription Services, Inc</general><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-2526-2002</orcidid></search><sort><creationdate>20230217</creationdate><title>Double‐Dependence Correlations in Graphdiyne‐Supported Atomic Catalysts to Promote CO2RR toward the Generation of C2 Products</title><author>Sun, Mingzi ; Wong, Hon Ho ; Wu, Tong ; Lu, Qiuyang ; Lu, Lu ; Chan, Cheuk Hei ; Chen, Baian ; Dougherty, Alan William ; Huang, Bolong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p2338-d3892ecbce6fffffa00d1fefb75fad269b0084bd9c9c17544f080d5beb6acf993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>atomic catalysts</topic><topic>C 2 products</topic><topic>carbon dioxide reduction</topic><topic>Catalysts</topic><topic>Chemical reduction</topic><topic>double‐dependence correlation</topic><topic>Electrocatalysts</topic><topic>graphdiyne</topic><topic>Machine learning</topic><topic>Selectivity</topic><topic>Transition metals</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Mingzi</creatorcontrib><creatorcontrib>Wong, Hon Ho</creatorcontrib><creatorcontrib>Wu, Tong</creatorcontrib><creatorcontrib>Lu, Qiuyang</creatorcontrib><creatorcontrib>Lu, Lu</creatorcontrib><creatorcontrib>Chan, Cheuk Hei</creatorcontrib><creatorcontrib>Chen, Baian</creatorcontrib><creatorcontrib>Dougherty, Alan William</creatorcontrib><creatorcontrib>Huang, Bolong</creatorcontrib><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Advanced energy materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Mingzi</au><au>Wong, Hon Ho</au><au>Wu, Tong</au><au>Lu, Qiuyang</au><au>Lu, Lu</au><au>Chan, Cheuk Hei</au><au>Chen, Baian</au><au>Dougherty, Alan William</au><au>Huang, Bolong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Double‐Dependence Correlations in Graphdiyne‐Supported Atomic Catalysts to Promote CO2RR toward the Generation of C2 Products</atitle><jtitle>Advanced energy materials</jtitle><date>2023-02-17</date><risdate>2023</risdate><volume>13</volume><issue>7</issue><epage>n/a</epage><issn>1614-6832</issn><eissn>1614-6840</eissn><abstract>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.</abstract><cop>Weinheim</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/aenm.202203858</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-2526-2002</orcidid></addata></record> |
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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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