Group contribution and atomic contribution models for the prediction of various physical properties of deep eutectic solvents

• Published in: Scientific reports 2021-03, Vol.11 (1), p.6684-6684, Article 6684
• Main Authors: Haghbakhsh, Reza, Raeissi, Sona, Duarte, Ana Rita C.
• Format: Article
• Language: English
• Subjects: 639/166/898; 639/638/440; 639/638/563; Clean technology; Computers; Feasibility studies; Green chemistry; Humanities and Social Sciences; multidisciplinary; Physical properties; Science; Science (multidisciplinary); Solvents
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-85824-z

The urgency of advancing green chemistry from labs and computers into the industries is well-known. The Deep Eutectic Solvents (DESs) are a promising category of novel green solvents which simultaneously have the best advantages of liquids and solids. Furthermore, they can be designed or engineered to have the characteristics desired for a given application. However, since they are rather new, there are no general models available to predict the properties of DESs without requiring other properties as input. This is particularly a setback when screening is required for feasibility studies, since a vast number of DESs are envisioned. For the first time, this study presents five group contribution (GC) and five atomic contribution (AC) models for densities, refractive indices, heat capacities, speeds of sound, and surface tensions of DESs. The models, developed using the most up-to-date databank of various types of DESs, simply decompose the molecular structure into a number of predefined groups or atoms. The resulting AARD% of densities, refractive indices, heat capacities, speeds of sound and surface tensions were, respectively, 1.44, 0.37, 3.26, 1.62, and 7.59% for the GC models, and 2.49, 1.03, 9.93, 4.52 and 7.80% for the AC models. Perhaps, even more importantly for designer solvents, is the predictive capability of the models, which was also shown to be highly reliable. Accordingly, very simple, yet highly accurate models are provided that are global for DESs and needless of any physical property information, making them useful predictive tools for a category of green solvents, which is only starting to show its potentials in green technology.