Computational Design of Natural Deep Eutectic Systems Using COSMO-RS for Ice Control Applications

Natural Deep Eutectic Systems (NADES) have shown great promise in sustainable and green chemistry applications due to their low toxicity and ease of preparation. Given the empirical approach to the selection of components and the mixing ratios for NADES, we aimed to create a methodology for in silico prescreening and eutectic composition prediction to optimize NADES for ice control applications. The activity coefficients of water for commonly used sugars (SUs), sugar alcohols (SAs), and amino acids (AAs) were predicted using the conductor-like screening model for real solvents (COSMO-RS) and used as a prescreening tool to identify potential NADES-forming compounds. Then, binary phase diagrams were generated for mixtures of these compounds using COSMO-RS to find the eutectic composition. Experimental characterization of the melting temperature, enthalpy of fusion, and glass transition temperature of the predicted eutectic compositions for select diluted AA-SA and SU-SA NADES was performed using differential scanning calorimetry (DSC), and their ice-forming characteristics were quantified using low-temperature Raman spectroscopy. The NADES exhibited significantly reduced enthalpies of fusion and ice crystal formation by over 70% and 50%, respectively, compared to pure water, highlighting their anti-icing potential. Comparisons with pure precursors showed similar or lower enthalpies of fusion for the predicted eutectic NADES combinations, underscoring their effectiveness while reducing environmental impact. Lastly, quantum chemical descriptors generated by COSMO-RS were analyzed for the different NADES-forming components to identify new prescreening parameters, such as the hydrogen bond donating moments, and propose potential new quantitative structure–property relationship (QSPR) model descriptors.