Predicting the density and viscosity of hydrophobic eutectic solvents: towards the development of sustainable solvents

Abstract

The interest in green and sustainable solvents has been dramatically increasing in recent years because of the growing awareness of the impact of classical organic solvents on environmental pollution and human health. As a solution to these issues, several greener and more sustainable solvents have been proposed in recent years such as the novel Hydrophobic Eutectic Solvents (HESs). HESs have many advantageous characteristics and could be considered as a potential replacement for both ionic liquids and classical solvents. However, choosing the right HES with the required physiochemical properties for a certain application is an extremely difficult task, especially since large-scale experimental measurements are expensive and time-consuming. Thus, the development of predictive models capable of estimating the properties of these solvents could be considered as a powerful tool in screening new green and sustainable HESs. This work presents two novel Quantitative Structure–Property Relationship (QSPR) models for predicting the density and viscosity of HESs using Conductor-like Screening Model for Real Solvents (COSMO-RS) based descriptors. The data set used includes all the experimental measurements reported in the literature up to the date of writing this work to ensure that the developed models are highly reliable and robust. The results show that the proposed models were excellent at predicting the properties of HES not included in the training set as R² values of 0.9956 and 0.9871 were obtained for density and viscosity, respectively. This work presents an initiative towards the development of reliable models for predicting the properties of HESs as a means to promote an efficient solvent design approach that can aid in designing and simulating new processes utilizing these novel HESs.