Elucidating the oxygen reduction reaction kinetics on defect engineered nanocarbon electrocatalyst: interplay between the N-dopant and defect sites
The active sites of electrocatalysts play a crucial role in the material design and mechanistic exploration of an electrocatalytic reaction. Defect-tailored heteroatom-doped carbon-based electrocatalysts for oxygen reduction reaction (ORR) have been much explored, but there is ambiguity in the predi...
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creator | Bhardwaj, Sakshi Kapse, Samadhan Dan, Soirik Thapa, Ranjit Dey, Ramendra Sundar |
description | The active sites of electrocatalysts play a crucial role in the material design and mechanistic exploration of an electrocatalytic reaction. Defect-tailored heteroatom-doped carbon-based electrocatalysts for oxygen reduction reaction (ORR) have been much explored, but there is ambiguity in the prediction of active sites responsible for the performance of the material. To find the origin of the activity of this class of catalysts towards ORR, in this work, we use the quantum mechanics/machine learning (QM/ML) approach to derive energy-optimized models with both N-atoms and 5-8-5 defect sites which manifest exemplary ORR activity. Following this approach, we synthesized defect-engineered graphene (DG) using the zinc template method at 1050 °C to achieve optimum N-dopants and intrinsic (5-8-5) defects. The obtained electrocatalyst exhibits hierarchical porosity, high surface area, low nitrogen content, good stability and a satisfying ORR performance with a half-wave potential (
E
1/2
) of 0.82 V, comparable to that of commercial Pt/C (
E
1/2
= 0.82 V). Further, the full energy profile was deduced using density functional theory and the charge redistribution in the material cross-verified a reduced overpotential for ORR. This work provides a strategy for the synthesis of noble-metal-free high-performance electrocatalysts for energy conversion.
For oxygen reduction reaction (ORR), the active sites of a defective N-doped graphene are predicted by a quantum mechanics/machine learning approach; the synthesized catalyst shows exemplary ORR activity that was further confirmed by a DFT study. |
doi_str_mv | 10.1039/d3ta00871a |
format | Article |
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E
1/2
) of 0.82 V, comparable to that of commercial Pt/C (
E
1/2
= 0.82 V). Further, the full energy profile was deduced using density functional theory and the charge redistribution in the material cross-verified a reduced overpotential for ORR. This work provides a strategy for the synthesis of noble-metal-free high-performance electrocatalysts for energy conversion.
For oxygen reduction reaction (ORR), the active sites of a defective N-doped graphene are predicted by a quantum mechanics/machine learning approach; the synthesized catalyst shows exemplary ORR activity that was further confirmed by a DFT study.</description><identifier>ISSN: 2050-7488</identifier><identifier>EISSN: 2050-7496</identifier><identifier>DOI: 10.1039/d3ta00871a</identifier><language>eng</language><publisher>Cambridge: Royal Society of Chemistry</publisher><subject>Catalysts ; Charge materials ; Chemical reduction ; Density functional theory ; Design defects ; Dopants ; Electrocatalysts ; Energy conversion ; Graphene ; Machine learning ; Nitrogen ; Noble metals ; Oxygen reduction reactions ; Porosity ; Quantum mechanics ; Reaction kinetics ; Surface stability</subject><ispartof>Journal of materials chemistry. A, Materials for energy and sustainability, 2023-08, Vol.11 (32), p.1745-1755</ispartof><rights>Copyright Royal Society of Chemistry 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c281t-f498a2cb94ce5ff9aaac37bcf55cfcc02316194552167705ab86196801411dfa3</citedby><cites>FETCH-LOGICAL-c281t-f498a2cb94ce5ff9aaac37bcf55cfcc02316194552167705ab86196801411dfa3</cites><orcidid>0000-0002-0790-9730 ; 0000-0003-3297-1437</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>Bhardwaj, Sakshi</creatorcontrib><creatorcontrib>Kapse, Samadhan</creatorcontrib><creatorcontrib>Dan, Soirik</creatorcontrib><creatorcontrib>Thapa, Ranjit</creatorcontrib><creatorcontrib>Dey, Ramendra Sundar</creatorcontrib><title>Elucidating the oxygen reduction reaction kinetics on defect engineered nanocarbon electrocatalyst: interplay between the N-dopant and defect sites</title><title>Journal of materials chemistry. A, Materials for energy and sustainability</title><description>The active sites of electrocatalysts play a crucial role in the material design and mechanistic exploration of an electrocatalytic reaction. Defect-tailored heteroatom-doped carbon-based electrocatalysts for oxygen reduction reaction (ORR) have been much explored, but there is ambiguity in the prediction of active sites responsible for the performance of the material. To find the origin of the activity of this class of catalysts towards ORR, in this work, we use the quantum mechanics/machine learning (QM/ML) approach to derive energy-optimized models with both N-atoms and 5-8-5 defect sites which manifest exemplary ORR activity. Following this approach, we synthesized defect-engineered graphene (DG) using the zinc template method at 1050 °C to achieve optimum N-dopants and intrinsic (5-8-5) defects. The obtained electrocatalyst exhibits hierarchical porosity, high surface area, low nitrogen content, good stability and a satisfying ORR performance with a half-wave potential (
E
1/2
) of 0.82 V, comparable to that of commercial Pt/C (
E
1/2
= 0.82 V). Further, the full energy profile was deduced using density functional theory and the charge redistribution in the material cross-verified a reduced overpotential for ORR. This work provides a strategy for the synthesis of noble-metal-free high-performance electrocatalysts for energy conversion.
For oxygen reduction reaction (ORR), the active sites of a defective N-doped graphene are predicted by a quantum mechanics/machine learning approach; the synthesized catalyst shows exemplary ORR activity that was further confirmed by a DFT study.</description><subject>Catalysts</subject><subject>Charge materials</subject><subject>Chemical reduction</subject><subject>Density functional theory</subject><subject>Design defects</subject><subject>Dopants</subject><subject>Electrocatalysts</subject><subject>Energy conversion</subject><subject>Graphene</subject><subject>Machine learning</subject><subject>Nitrogen</subject><subject>Noble metals</subject><subject>Oxygen reduction reactions</subject><subject>Porosity</subject><subject>Quantum mechanics</subject><subject>Reaction kinetics</subject><subject>Surface stability</subject><issn>2050-7488</issn><issn>2050-7496</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpFkU1PwzAMhisEEtPYhTtSJG5IhaRt2pTbNMaHNMFlnCs3dUZHSUuSCvo7-MNkFIYvfm0_ei3ZQXDK6CWjcX5VxQ4oFRmDg2ASUU7DLMnTw70W4jiYWbulPgSlaZ5Pgq9l08u6AlfrDXEvSNrPYYOaGKx66ep2p2AUr7VGV0tLvK5QoXQE9cY30cNEg24lmNIPsfEz4ysHzWDdNam1Q9M1MJAS3Qd6-92mx7BqO9COgK7-DG3t0J4ERwoai7PfPA2eb5frxX24erp7WMxXoYwEc6FKcgGRLPNEIlcqBwAZZ6VUnEslJY1ilrI84TxiaZZRDqXwdSooSxirFMTT4Hz07Uz73qN1xbbtjfYri0hwlvEkpZmnLkZKmtZag6roTP0GZigYLXZ3L27i9fzn7nMPn42wsXLP_f8l_gbG1IJg</recordid><startdate>20230817</startdate><enddate>20230817</enddate><creator>Bhardwaj, Sakshi</creator><creator>Kapse, Samadhan</creator><creator>Dan, Soirik</creator><creator>Thapa, Ranjit</creator><creator>Dey, Ramendra Sundar</creator><general>Royal Society of Chemistry</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>JG9</scope><scope>L7M</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-0790-9730</orcidid><orcidid>https://orcid.org/0000-0003-3297-1437</orcidid></search><sort><creationdate>20230817</creationdate><title>Elucidating the oxygen reduction reaction kinetics on defect engineered nanocarbon electrocatalyst: interplay between the N-dopant and defect sites</title><author>Bhardwaj, Sakshi ; Kapse, Samadhan ; Dan, Soirik ; Thapa, Ranjit ; Dey, Ramendra Sundar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c281t-f498a2cb94ce5ff9aaac37bcf55cfcc02316194552167705ab86196801411dfa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Catalysts</topic><topic>Charge materials</topic><topic>Chemical reduction</topic><topic>Density functional theory</topic><topic>Design defects</topic><topic>Dopants</topic><topic>Electrocatalysts</topic><topic>Energy conversion</topic><topic>Graphene</topic><topic>Machine learning</topic><topic>Nitrogen</topic><topic>Noble metals</topic><topic>Oxygen reduction reactions</topic><topic>Porosity</topic><topic>Quantum mechanics</topic><topic>Reaction kinetics</topic><topic>Surface stability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bhardwaj, Sakshi</creatorcontrib><creatorcontrib>Kapse, Samadhan</creatorcontrib><creatorcontrib>Dan, Soirik</creatorcontrib><creatorcontrib>Thapa, Ranjit</creatorcontrib><creatorcontrib>Dey, Ramendra Sundar</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><jtitle>Journal of materials chemistry. A, Materials for energy and sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bhardwaj, Sakshi</au><au>Kapse, Samadhan</au><au>Dan, Soirik</au><au>Thapa, Ranjit</au><au>Dey, Ramendra Sundar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Elucidating the oxygen reduction reaction kinetics on defect engineered nanocarbon electrocatalyst: interplay between the N-dopant and defect sites</atitle><jtitle>Journal of materials chemistry. A, Materials for energy and sustainability</jtitle><date>2023-08-17</date><risdate>2023</risdate><volume>11</volume><issue>32</issue><spage>1745</spage><epage>1755</epage><pages>1745-1755</pages><issn>2050-7488</issn><eissn>2050-7496</eissn><abstract>The active sites of electrocatalysts play a crucial role in the material design and mechanistic exploration of an electrocatalytic reaction. Defect-tailored heteroatom-doped carbon-based electrocatalysts for oxygen reduction reaction (ORR) have been much explored, but there is ambiguity in the prediction of active sites responsible for the performance of the material. To find the origin of the activity of this class of catalysts towards ORR, in this work, we use the quantum mechanics/machine learning (QM/ML) approach to derive energy-optimized models with both N-atoms and 5-8-5 defect sites which manifest exemplary ORR activity. Following this approach, we synthesized defect-engineered graphene (DG) using the zinc template method at 1050 °C to achieve optimum N-dopants and intrinsic (5-8-5) defects. The obtained electrocatalyst exhibits hierarchical porosity, high surface area, low nitrogen content, good stability and a satisfying ORR performance with a half-wave potential (
E
1/2
) of 0.82 V, comparable to that of commercial Pt/C (
E
1/2
= 0.82 V). Further, the full energy profile was deduced using density functional theory and the charge redistribution in the material cross-verified a reduced overpotential for ORR. This work provides a strategy for the synthesis of noble-metal-free high-performance electrocatalysts for energy conversion.
For oxygen reduction reaction (ORR), the active sites of a defective N-doped graphene are predicted by a quantum mechanics/machine learning approach; the synthesized catalyst shows exemplary ORR activity that was further confirmed by a DFT study.</abstract><cop>Cambridge</cop><pub>Royal Society of Chemistry</pub><doi>10.1039/d3ta00871a</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-0790-9730</orcidid><orcidid>https://orcid.org/0000-0003-3297-1437</orcidid></addata></record> |
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source | Royal Society Of Chemistry Journals 2008- |
subjects | Catalysts Charge materials Chemical reduction Density functional theory Design defects Dopants Electrocatalysts Energy conversion Graphene Machine learning Nitrogen Noble metals Oxygen reduction reactions Porosity Quantum mechanics Reaction kinetics Surface stability |
title | Elucidating the oxygen reduction reaction kinetics on defect engineered nanocarbon electrocatalyst: interplay between the N-dopant and defect sites |
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