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

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Halder, AK (Author) Other; Haghbakhsh, R (Author) Other; Voroshylova, IV (Author) FCUP; Duarte, ARC (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: Molecules

Serial No. 3 Vol. 27 No. 969

Final page: 4896

ISSN: 1420-3049

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00X-2JK

DOI: 10.3390/molecules27154896

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 18