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 (R²_{independent 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.

Two deep eutectic solvents (DES) based on betaine (B) and propylene glycol (PG) in molar ratios of 1:4 and 1:5 were prepared and their physico-chemical properties were determined. Liquid-liquid equilibria at 298.15 K and atmospheric pressure were determined for 13 quasi-three-component systems with a hydrocarbon (n-hexane, n-heptane, i-octane or toluene), solute (pyridine or thiophene) and DES (B-PG 1:4 or B-PG 1:5). Experimental phase diagrams and calculated distribution ratios showed that DESs are more suitable for denitrification than for desulfurization. The equilibria in the investigated systems were described by the NRTL and UNIQUAC models, and good agreement with experiments was obtained.