Deciphering pH Behavior in Deep Eutectic Solvents: Synergizing Experimental Data and Computational Modeling

The expanding application of deep eutectic solvents (DESs) in both upstream and downstream processes has brought increasing attention to the role of pH in these systems. Despite its importance, accurate pH determination in DESs remains a significant challenge, mainly due to the lack of standardized measurement protocols. This has resulted in limited and sometimes contradictory data in literature. In this study, we investigate the acid–base properties of representative DESs using two complementary techniques: pH electrode measurements and UV–vis spectrophotometry with pH‐sensitive indicators. The DESs examined include hydrophilic systems based on choline chloride, betaine, and tetrabutylammonium chloride combined with glycerol or ethylene glycol, as well as a hydrophobic lidocaine‐oleic acid system. Indicator solutions were titrated with hydrochloric acid, while absorbance and electrode pH values were recorded across each dye's transition range. The resulting absorbance–pH curves were fitted using an inverse Boltzmann model, showing excellent correlation ( R 2 > 0.99) and enabling reliable interpolation. Additionally, Gaussian process (GP)‐based machine learning (ML) confirmed strong agreement between methods within a 95% confidence interval. This integrated approach provides a robust framework for accurate pH assessment in DESs.