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Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents

Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties t...

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Autores principales: Halder, Amit Kumar, Haghbakhsh, Reza, Voroshylova, Iuliia V., Duarte, Ana Rita C., Cordeiro, Maria Natalia D. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370217/
https://www.ncbi.nlm.nih.gov/pubmed/35956845
http://dx.doi.org/10.3390/molecules27154896
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author Halder, Amit Kumar
Haghbakhsh, Reza
Voroshylova, Iuliia V.
Duarte, Ana Rita C.
Cordeiro, Maria Natalia D. S.
author_facet Halder, Amit Kumar
Haghbakhsh, Reza
Voroshylova, Iuliia V.
Duarte, Ana Rita C.
Cordeiro, Maria Natalia D. S.
author_sort Halder, Amit Kumar
collection PubMed
description Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including ‘mixtures-out’- and ‘compounds-out’-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures.
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spelling pubmed-93702172022-08-12 Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents Halder, Amit Kumar Haghbakhsh, Reza Voroshylova, Iuliia V. Duarte, Ana Rita C. Cordeiro, Maria Natalia D. S. Molecules Article Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including ‘mixtures-out’- and ‘compounds-out’-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures. MDPI 2022-07-31 /pmc/articles/PMC9370217/ /pubmed/35956845 http://dx.doi.org/10.3390/molecules27154896 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Halder, Amit Kumar
Haghbakhsh, Reza
Voroshylova, Iuliia V.
Duarte, Ana Rita C.
Cordeiro, Maria Natalia D. S.
Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents
title Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents
title_full Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents
title_fullStr Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents
title_full_unstemmed Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents
title_short Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents
title_sort predicting the surface tension of deep eutectic solvents: a step forward in the use of greener solvents
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370217/
https://www.ncbi.nlm.nih.gov/pubmed/35956845
http://dx.doi.org/10.3390/molecules27154896
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