Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents

Abstract

The case of sustainable solvents is of great interest both academically and industrially. With research communities becoming more aware of the negative impacts of conventional organic solvents, a range of greener and more sustainable solvents have been developed to counter the harmful drawbacks associated with conventional solvents. Among these, eutectic solvents (ESs) attracted considerable attention for their "green" properties and have proven their usefulness as environmentally benign alternatives to classical solvents. Among the various desirable characteristics of ESs, pH is a key property with significant implications for the design and control of industrial-scale applications. However, selecting an ES with the required pH for a particular application is a challenging task, especially with extensive experimentally determined data being time consuming and expensive. Therefore, in this work, the pH of various ESs have been predicted via novel quantitative structure−property relationships (QSPR) models using two machine learning algorithms, a multiple linear regression (MLR) and an artificial neural network (ANN), with a set of molecular descriptors generated by COSMO-RS. A total of 648 experimental points for 41 chemically unique ESs prepared from 9 HBAs and 21 HBDs at different temperatures were utilized for sufficient data set representation. On the basis of the statistical analysis of the models, it can be concluded that both approaches can be utilized as powerful predictive tools in estimating the pH of new ESs with the ANN model having better predictive capabilities and the MLR model being more interpretable. These models inspire and stimulate the development of robust models to predict the properties of designer solvents from the drawn molecular structures, which will save time and resources.

Prediction of pH Value of Aqueous Acidic and Basic Deep Eutectic Solvent Using COSMO-RS σ Profiles’ Molecular Descriptors

“…Therefore, any change in the molecular structure can be quantified. By coupling the σ profile of DES-forming compounds with experimental data using model-generating methods such as multiple linear regression (MLR), piecewise linear regression (PLR), or artificial neural networks (ANNs), models for the description of DESs’ physicochemical properties can be developed [ 11 , 12 , 13 , 14 ]. In most studies, good model fitting of the literature viscosity, density, and pH values of the DESs was obtained [ 12 , 13 ].…”

A Review of the electrochemical corrosion of metals in choline chloride based deep eutectic solvents

“…For all choline chloride based DESs, a linear decrease of pH value was observed with the increase in temperature, and as concerning the water influence, the increase in water content decreases the pH for the majority of DESs. However, there are DESs where the opposite was observed, for instance in ChCl-citric acid mixture [41]. The fast metal corrosion due to the rapid hydrogen evolution reaction in acidic DESs was demonstrated by Abbott et al [48], where it was shown that the corrosion rate (expressed in μm per year) for mild steel in oxaline was two orders of magnitude higher in comparison to reline and ethaline, whereas it was one order of magnitude higher for Ni.…”

“…The pH value of a DES depends on the ability of the DES's cation, anion, and HBD to act as proton acceptors and proton donors [40,41]. If HA is a protic hydrogen bond donor, then the protonation reaction in DES is HA + Y ⇌ HY + +A − (1) where Y represents the DES constituent [40,41].…”

“…The pH value of ChCl based DESs is significantly influenced by the type of HBD. The acidity decreases in the following order: DESs with carboxylic acids (citric, glycolic, lactic, malic, malonic, oxalic acid)>polyols (ethylene glycol, glycerol) > sugars(fructose, glucose) > amines and amides (urea, ethanolamine, diethanolamine) [41]. For all choline chloride based DESs, a linear decrease of pH value was observed with the increase in temperature, and as concerning the water influence, the increase in water content decreases the pH for the majority of DESs.…”

Green Extraction of Volatile Fatty Acids from Fermented Wastewater Using Hydrophobic Deep Eutectic Solvents

“…DESs are commonly described as a mixture of two or more compounds that form upon mixing a liquid phase with a melting point far below that of its constituents [23][24][25]. It is anticipated that the formation of the DES occurs via a combination of entropy of mixing, van der Waals interactions, and hydrogen bonding, where one compound is considered a hydrogen bond donor (HBD) and the other is a hydrogen bond acceptor (HBA).…”