ArticleScientific reports2021
Group contribution and atomic contribution models for the prediction of various physical properties of deep eutectic solvents.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 56 citations in OpenAlex.
- Evaluating the suitability of six hydrophobic natural eutectic solvents as the internal phase in a cosmetic cream formulation.International journal of cosmetic science · 2026Article
- A Leakage-Aware Benchmark Study of Machine Learning Models for Deep Eutectic Solvent Property Prediction.ACS omega · 2026Article
- Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach.ACS sustainable chemistry & engineering · 2024Article
- Editorial for Special Issue: "Recent Advances in Green Solvents".Molecules (Basel, Switzerland) · 2023Article
- Deep Eutectic Solvents: Properties and Applications in COMolecules (Basel, Switzerland) · 2023Review
- Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks.ACS omega · 2022Article
- Viscosity Investigations on the Binary Systems of (1 ChCl:2 Ethylene Glycol) DES and Methanol or Ethanol.Molecules (Basel, Switzerland) · 2021Article
- The Role of Hydrogen Bond Donor on the Extraction of Phenolic Compounds from Natural Matrices Using Deep Eutectic Systems.Molecules (Basel, Switzerland) · 2021Review
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
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