Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach

Autores
Tavares Duarte de Alencar, Luan Vittor; Rodriguez Reartes, Sabrina Belen; Tavares, Frederico Wanderley; Llovell, Fèlix
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES’s suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.
Fil: Tavares Duarte de Alencar, Luan Vittor. Universidade Federal do Estado do Rio de Janeiro; Brasil. Universitat Rovira I Virgili. Facultad de Quimica.; España
Fil: Rodriguez Reartes, Sabrina Belen. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Universitat Rovira I Virgili. Facultad de Quimica.; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Tavares, Frederico Wanderley. Universidade Federal do Estado do Rio de Janeiro; Brasil
Fil: Llovell, Fèlix. Universitat Rovira I Virgili. Facultad de Quimica.; España
Materia
DEEP EUTECTIC SOLVENTS
VISCOSITY
MACHINE LEARNING
ARTIFICIAL NEURAL NETWORK
COSMO-SAC
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/241500
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular ApproachTavares Duarte de Alencar, Luan VittorRodriguez Reartes, Sabrina BelenTavares, Frederico WanderleyLlovell, FèlixDEEP EUTECTIC SOLVENTSVISCOSITYMACHINE LEARNINGARTIFICIAL NEURAL NETWORKCOSMO-SAChttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES’s suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.Fil: Tavares Duarte de Alencar, Luan Vittor. Universidade Federal do Estado do Rio de Janeiro; Brasil. Universitat Rovira I Virgili. Facultad de Quimica.; EspañaFil: Rodriguez Reartes, Sabrina Belen. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Universitat Rovira I Virgili. Facultad de Quimica.; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Tavares, Frederico Wanderley. Universidade Federal do Estado do Rio de Janeiro; BrasilFil: Llovell, Fèlix. Universitat Rovira I Virgili. Facultad de Quimica.; EspañaAmerican Chemical Society2024-05-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/241500Tavares Duarte de Alencar, Luan Vittor; Rodriguez Reartes, Sabrina Belen; Tavares, Frederico Wanderley; Llovell, Fèlix; Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach; American Chemical Society; ACS Sustainable Chemistry and Engineering; 12; 21; 12-5-2024; 7987-80002168-0485CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1021/acssuschemeng.3c07219info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acssuschemeng.3c07219info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:46:30Zoai:ri.conicet.gov.ar:11336/241500instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 14:46:31.163CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
title Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
spellingShingle Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
Tavares Duarte de Alencar, Luan Vittor
DEEP EUTECTIC SOLVENTS
VISCOSITY
MACHINE LEARNING
ARTIFICIAL NEURAL NETWORK
COSMO-SAC
title_short Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
title_full Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
title_fullStr Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
title_full_unstemmed Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
title_sort Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
dc.creator.none.fl_str_mv Tavares Duarte de Alencar, Luan Vittor
Rodriguez Reartes, Sabrina Belen
Tavares, Frederico Wanderley
Llovell, Fèlix
author Tavares Duarte de Alencar, Luan Vittor
author_facet Tavares Duarte de Alencar, Luan Vittor
Rodriguez Reartes, Sabrina Belen
Tavares, Frederico Wanderley
Llovell, Fèlix
author_role author
author2 Rodriguez Reartes, Sabrina Belen
Tavares, Frederico Wanderley
Llovell, Fèlix
author2_role author
author
author
dc.subject.none.fl_str_mv DEEP EUTECTIC SOLVENTS
VISCOSITY
MACHINE LEARNING
ARTIFICIAL NEURAL NETWORK
COSMO-SAC
topic DEEP EUTECTIC SOLVENTS
VISCOSITY
MACHINE LEARNING
ARTIFICIAL NEURAL NETWORK
COSMO-SAC
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES’s suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.
Fil: Tavares Duarte de Alencar, Luan Vittor. Universidade Federal do Estado do Rio de Janeiro; Brasil. Universitat Rovira I Virgili. Facultad de Quimica.; España
Fil: Rodriguez Reartes, Sabrina Belen. Universidad Nacional del Sur. Departamento de Ingeniería Química; Argentina. Universitat Rovira I Virgili. Facultad de Quimica.; España. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Tavares, Frederico Wanderley. Universidade Federal do Estado do Rio de Janeiro; Brasil
Fil: Llovell, Fèlix. Universitat Rovira I Virgili. Facultad de Quimica.; España
description Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES’s suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.
publishDate 2024
dc.date.none.fl_str_mv 2024-05-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/241500
Tavares Duarte de Alencar, Luan Vittor; Rodriguez Reartes, Sabrina Belen; Tavares, Frederico Wanderley; Llovell, Fèlix; Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach; American Chemical Society; ACS Sustainable Chemistry and Engineering; 12; 21; 12-5-2024; 7987-8000
2168-0485
CONICET Digital
CONICET
url http://hdl.handle.net/11336/241500
identifier_str_mv Tavares Duarte de Alencar, Luan Vittor; Rodriguez Reartes, Sabrina Belen; Tavares, Frederico Wanderley; Llovell, Fèlix; Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach; American Chemical Society; ACS Sustainable Chemistry and Engineering; 12; 21; 12-5-2024; 7987-8000
2168-0485
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1021/acssuschemeng.3c07219
info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acssuschemeng.3c07219
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.265058

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