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dc.contributor.author
Tavares Duarte de Alencar, Luan Vittor
dc.contributor.author
Rodriguez Reartes, Sabrina Belen
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dc.contributor.author
Tavares, Frederico Wanderley
dc.contributor.author
Llovell, Fèlix
dc.date.available
2024-08-01T14:21:00Z
dc.date.issued
2024-05-12
dc.identifier.citation
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
dc.identifier.uri
http://hdl.handle.net/11336/241500
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Chemical Society
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
DEEP EUTECTIC SOLVENTS
dc.subject
VISCOSITY
dc.subject
MACHINE LEARNING
dc.subject
ARTIFICIAL NEURAL NETWORK
dc.subject
COSMO-SAC
dc.subject.classification
Ingeniería Química
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dc.subject.classification
Ingeniería Química
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-07-16T15:30:18Z
dc.identifier.eissn
2168-0485
dc.journal.volume
12
dc.journal.number
21
dc.journal.pagination
7987-8000
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Washington
dc.description.fil
Fil: Tavares Duarte de Alencar, Luan Vittor. Universidade Federal do Estado do Rio de Janeiro; Brasil. Universitat Rovira I Virgili. Facultad de Quimica.; España
dc.description.fil
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
dc.description.fil
Fil: Tavares, Frederico Wanderley. Universidade Federal do Estado do Rio de Janeiro; Brasil
dc.description.fil
Fil: Llovell, Fèlix. Universitat Rovira I Virgili. Facultad de Quimica.; España
dc.journal.title
ACS Sustainable Chemistry and Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/acssuschemeng.3c07219
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://pubs.acs.org/doi/10.1021/acssuschemeng.3c07219
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