A generalized model for estimating adsorption energies of single atoms on doped carbon materials

Single metal atoms on doped carbons constitute a new class of extremely appealing materials, as they present the best metal utilization for catalysis. However, their stability can be compromised by metal aggregation and the formation of nanoparticles, which often results in reduced activity or even...

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Autores: Minotaki, Maria G., Geiger, Julian, Ruiz-Ferrando, Andrea, Sabadell-Rendón, Albert, López, Núria
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2072/537622
Acceso en línea:http://hdl.handle.net/2072/537622
https://doi.org/10.1039/D3TA05898K
Access Level:acceso abierto
Palabra clave:Química
54 - Química
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spelling A generalized model for estimating adsorption energies of single atoms on doped carbon materialsMinotaki, Maria G.Geiger, JulianRuiz-Ferrando, AndreaSabadell-Rendón, AlbertLópez, NúriaQuímica54 - QuímicaSingle metal atoms on doped carbons constitute a new class of extremely appealing materials, as they present the best metal utilization for catalysis. However, their stability can be compromised by metal aggregation and the formation of nanoparticles, which often results in reduced activity or even catalyst deactivation. In many cases, the carbon hosts are generated via thermal processes, leading to poorly controlled materials. This causes a structural and compositional diversity that is modeled via indirect procedures and by comparison to a collection of structural models with different compositions. Our aim in this work is to develop a general framework based on machine learning techniques to determine the stability of the different structures against aggregation as nanoparticles. Here, we built machine learning models for the cavities and identified the robust features characterizing the metal–support interaction, considering different heteroatoms in the decorative cavity and single metal atoms. The descriptors presented here are accessible and cost-effective, such as the cavity size, electronegativity of the metal and heteroatoms, different covalent radii, and the metal electronic density. These can then be employed in the search for a mathematical equation that describes the adsorption energy via the Bayesian machine scientist. The algorithm is able to separate coordination, and covalent and ionic contributions expressed by the descriptors. This approach paves the way towards general modeling of single atoms in modified carbons particularly addressing one of the crucial features, stability.Royal Society of Chemistry2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion17 p.application/pdfhttp://hdl.handle.net/2072/537622https://doi.org/10.1039/D3TA05898KRECERCAT (Dipòsit de la Recerca de Catalunya)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésNCCR Catalysis (grant number 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation.Generalitat de Catalunya and the European Union under Grant 2020_FI_B 00266, 2023-FI-3-00027TotalEnergies (contract reference CT00001052)Ministry of Science and Innovation (Ref. No. PID2021-122516OB-I00)CC-BYinfo:eu-repo/semantics/openAccessoai:recercat.cat:2072/5376222026-05-29T05:05:01Z
dc.title.none.fl_str_mv A generalized model for estimating adsorption energies of single atoms on doped carbon materials
title A generalized model for estimating adsorption energies of single atoms on doped carbon materials
spellingShingle A generalized model for estimating adsorption energies of single atoms on doped carbon materials
Minotaki, Maria G.
Química
54 - Química
title_short A generalized model for estimating adsorption energies of single atoms on doped carbon materials
title_full A generalized model for estimating adsorption energies of single atoms on doped carbon materials
title_fullStr A generalized model for estimating adsorption energies of single atoms on doped carbon materials
title_full_unstemmed A generalized model for estimating adsorption energies of single atoms on doped carbon materials
title_sort A generalized model for estimating adsorption energies of single atoms on doped carbon materials
dc.creator.none.fl_str_mv Minotaki, Maria G.
Geiger, Julian
Ruiz-Ferrando, Andrea
Sabadell-Rendón, Albert
López, Núria
author Minotaki, Maria G.
author_facet Minotaki, Maria G.
Geiger, Julian
Ruiz-Ferrando, Andrea
Sabadell-Rendón, Albert
López, Núria
author_role author
author2 Geiger, Julian
Ruiz-Ferrando, Andrea
Sabadell-Rendón, Albert
López, Núria
author2_role author
author
author
author
dc.subject.none.fl_str_mv Química
54 - Química
topic Química
54 - Química
description Single metal atoms on doped carbons constitute a new class of extremely appealing materials, as they present the best metal utilization for catalysis. However, their stability can be compromised by metal aggregation and the formation of nanoparticles, which often results in reduced activity or even catalyst deactivation. In many cases, the carbon hosts are generated via thermal processes, leading to poorly controlled materials. This causes a structural and compositional diversity that is modeled via indirect procedures and by comparison to a collection of structural models with different compositions. Our aim in this work is to develop a general framework based on machine learning techniques to determine the stability of the different structures against aggregation as nanoparticles. Here, we built machine learning models for the cavities and identified the robust features characterizing the metal–support interaction, considering different heteroatoms in the decorative cavity and single metal atoms. The descriptors presented here are accessible and cost-effective, such as the cavity size, electronegativity of the metal and heteroatoms, different covalent radii, and the metal electronic density. These can then be employed in the search for a mathematical equation that describes the adsorption energy via the Bayesian machine scientist. The algorithm is able to separate coordination, and covalent and ionic contributions expressed by the descriptors. This approach paves the way towards general modeling of single atoms in modified carbons particularly addressing one of the crucial features, stability.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/2072/537622
https://doi.org/10.1039/D3TA05898K
url http://hdl.handle.net/2072/537622
https://doi.org/10.1039/D3TA05898K
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv NCCR Catalysis (grant number 180544), a National Centre of Competence in Research funded by the Swiss National Science Foundation.
Generalitat de Catalunya and the European Union under Grant 2020_FI_B 00266, 2023-FI-3-00027
TotalEnergies (contract reference CT00001052)
Ministry of Science and Innovation (Ref. No. PID2021-122516OB-I00)
dc.rights.none.fl_str_mv CC-BY
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC-BY
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 17 p.
application/pdf
dc.publisher.none.fl_str_mv Royal Society of Chemistry
publisher.none.fl_str_mv Royal Society of Chemistry
dc.source.none.fl_str_mv RECERCAT (Dipòsit de la Recerca de Catalunya)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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