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

Abstract

The aim of this work was to develop a simple and easy-to-apply model to predict the pH values of deep eutectic solvents (DESs) over a wide range of pH values that can be used in daily work. For this purpose, the pH values of 38 different DESs were measured (ranging from 0.36 to 9.31) and mathematically interpreted. To develop mathematical models, DESs were first numerically described using σ profiles generated with the COSMOtherm software. After the DESs’ description, the following models were used: (i) multiple linear regression (MLR), (ii) piecewise linear regression (PLR), and (iii) artificial neural networks (ANNs) to link the experimental values with the descriptors. Both PLR and ANN were found to be applicable to predict the pH values of DESs with a very high goodness of fit (R2independent validation > 0.8600). Due to the good mathematical correlation of the experimental and predicted values, the σ profile generated with COSMOtherm could be used as a DES molecular descriptor for the prediction of their pH values.

Deep Eutectic Solvents for Biodiesel Purification in a Microextractor: Solvent Preparation, Selection and Process Optimization

“…Piecewise linear regression is applicable if the data exhibit different linear trends over different domains [ 53 ]; thus, the regression can be made more accurate, as it is the case in this work ( Figure 3 C). By analyzing Figure 3 A,B, it can be noticed that data behave differently below and above the 50% efficiency threshold, which was confirmed by the estimated break point at 52.9 ± 5.1 PLR models were also previously shown to be more efficient that MLR models for the prediction of the DESs pH values based on the σ -descriptors [ 30 ].…”

“…As presented in Table 3 , significant correlations were noticed between S 1 mix , S 3 mix , S 4 mix , S 5 mix , S 6 mix and extraction efficiency and therefore those S i mix descriptors were used for further modeling. The similar approach to model input variables selection was previously described by Benguerba et al [ 27 ] and Panić et al [ 30 ]. By reducing the number of model input variables by excluding the non-significant ones, a low dispersion between observed DES viscosity data and multiple linear region model data was achieved.…”

“…The algorithm searches for numerical solutions in the function parameter space using the least squares method. Calculations were performed in 50 iterations with the convergence parameter of 10 −6 and 95% confidence interval [ 29 , 30 ].…”

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

“…Certain modeling techniques, which have proven effective for pure molecule systems, may yield considerable prediction inaccuracies when confronted with the challenges of characterizing intermolecular interactions in mixtures. More specifically, among prevailing QSPR studies for ES, ,,, their strategies of mixture representation are predominantly merging the specialized molecular descriptors or S σ‑profile descriptors together. However, from an intermolecular interaction standpoint, such a method of ES representation may struggle to yield predictive models with tangible practical applicability.…”

Quantification of alkalinity of deep eutectic solvents based on (H−) and NMR

“…35,36 Furthermore, when studying DESs, if there are proton-donating substances or proton-accepting substances in the solvent system, pH and pKa values are usually used to evaluate its acidity and basicity. 37,38 In this study, we investigated nitrogen-containing compounds that were synthesized as alkaline DESs. Our main method was to characterize the basicity of these solvents using two techniques.…”