Quantification of alkalinity of deep eutectic solvents based on (H_-) and NMR

Alkaline deep eutectic solvents (DESs) have been widely employed across diverse fields. A comprehensive understanding of the alkalinity data is imperative for the comprehension of their performance. However, the current range of techniques for quantifying alkalinity is constrained. In this investigation, we formulated a series of alkaline DESs and assessed their basicity properties through a comprehensive methodology of Hammett functions alongside ¹H NMR analysis. A correlation was established between the composition, structure and alkalinity of solvents. Furthermore, a strong linear correlation was observed between the Hammett basicity (H_-) of solvents and initial CO₂ adsorption rate. Machine learning techniques were employed to predict the significant impact of alkaline functional components on alkalinity levels and CO₂ capture capacity. This study offers valuable insights into the design, synthesis and structure-function relationship of alkaline DESs.