QSPR modeling of selectivity at infinite dilution of ionic liquids

Detalhes bibliográficos
Autor(a) principal: Klimenko, Kyrylo
Data de Publicação: 2021
Outros Autores: Carrera, Gonçalo V.S.M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/128957
Resumo: "
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spelling QSPR modeling of selectivity at infinite dilution of ionic liquidsBig dataKerasLiquid mixturesSeparation technologyComputer Science ApplicationsPhysical and Theoretical ChemistryComputer Graphics and Computer-Aided DesignLibrary and Information Sciences"The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.LAQV@REQUIMTEDQ - Departamento de QuímicaRUNKlimenko, KyryloCarrera, Gonçalo V.S.M.2021-12-09T23:40:22Z2021-122021-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/128957eng1758-2946PURE: 35279538https://doi.org/10.1186/s13321-021-00562-8info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:08:16Zoai:run.unl.pt:10362/128957Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:26.144799Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv QSPR modeling of selectivity at infinite dilution of ionic liquids
title QSPR modeling of selectivity at infinite dilution of ionic liquids
spellingShingle QSPR modeling of selectivity at infinite dilution of ionic liquids
Klimenko, Kyrylo
Big data
Keras
Liquid mixtures
Separation technology
Computer Science Applications
Physical and Theoretical Chemistry
Computer Graphics and Computer-Aided Design
Library and Information Sciences
title_short QSPR modeling of selectivity at infinite dilution of ionic liquids
title_full QSPR modeling of selectivity at infinite dilution of ionic liquids
title_fullStr QSPR modeling of selectivity at infinite dilution of ionic liquids
title_full_unstemmed QSPR modeling of selectivity at infinite dilution of ionic liquids
title_sort QSPR modeling of selectivity at infinite dilution of ionic liquids
author Klimenko, Kyrylo
author_facet Klimenko, Kyrylo
Carrera, Gonçalo V.S.M.
author_role author
author2 Carrera, Gonçalo V.S.M.
author2_role author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
DQ - Departamento de Química
RUN
dc.contributor.author.fl_str_mv Klimenko, Kyrylo
Carrera, Gonçalo V.S.M.
dc.subject.por.fl_str_mv Big data
Keras
Liquid mixtures
Separation technology
Computer Science Applications
Physical and Theoretical Chemistry
Computer Graphics and Computer-Aided Design
Library and Information Sciences
topic Big data
Keras
Liquid mixtures
Separation technology
Computer Science Applications
Physical and Theoretical Chemistry
Computer Graphics and Computer-Aided Design
Library and Information Sciences
description "
publishDate 2021
dc.date.none.fl_str_mv 2021-12-09T23:40:22Z
2021-12
2021-12-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/128957
url http://hdl.handle.net/10362/128957
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1758-2946
PURE: 35279538
https://doi.org/10.1186/s13321-021-00562-8
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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