Characterization and Applications of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are a class of designer solvents formed by mixing a hydrogen bond acceptor (HBA) and a hydrogen bond donor (HBD) to create a eutectic mixture with a melting point lower than that of either component. Since their introduction, DESs have attracted attention for their simplicity of preparation, tunable physicochemical properties and potential to replace conventional organic solvents in various chemical processes. Key characterizations focus on density, viscosity, ionic conductivity, surface tension and phase behaviour, which can be tailored by component selection and ratio. Recent advances have combined experimental thermophysical measurements with cheminformatics and machine learning to predict properties such as density, viscosity and pH, enabling rational design of solvents for extraction, catalysis, materials synthesis and biomass processing. Applications span from bio-inspired materials chemistry, where DESs act as media for controlled mineralisation and the synthesis of hybrid inorganic–organic frameworks, to sustainable catalysis, energy storage and drug delivery. The ability to fine-tune solvent polarity, hydrogen-bonding network and thermal stability has fostered innovations in biomass fractionation, metal extraction, enzymatic biocatalysis and electrochemical systems. Taken together, ongoing efforts in fundamental characterisation and data-driven property prediction are accelerating the translation of DES technology into industrial and environmental applications worldwide.

Research from Nature Portfolio

Recent meta-analyses have scrutinised the influence of water content and temperature on the viscosity of common DESs, revealing consistent excess activation energies for viscous flow across mixtures and identifying gaps in data quality and reporting standards. Parallel developments in predictive modelling introduced group contribution and atomic contribution frameworks capable of estimating densities, refractive indices, heat capacities, speeds of sound and surface tensions directly from molecular structure with average deviations below 5 %. More recently, machine learning algorithms such as least-squares support vector regression have been employed to predict DES densities with sub-percent error, using inputs of temperature and critical properties. These models demonstrate high accuracy and broad applicability domains, offering a pathway to screen and design new DESs without extensive experimentation.

Research from all publishers

Advances in artificial neural network models have enabled precise viscosity predictions for a wide range of DESs and their cosolvent mixtures by integrating molecular descriptors from conductor-like screening models. Such models exhibit strong extrapolation capabilities, accurately forecasting viscosity for compositions and donor–acceptor combinations beyond the training set. In parallel, the unique solvent environment provided by DESs has been leveraged to guide biomimetic synthesis of inorganic–organic hybrid materials, highlighting the capacity of DESs to stabilise transient species and direct mineralisation pathways under mild conditions. A comprehensive review of physicochemical properties has recently catalogued parameters such as density, viscosity, ionic conductivity, vapour pressure and acoustic properties across diverse DES systems, identifying under-explored formulations and urging standardised reporting to facilitate comparative studies and industrial uptake.

Characterization and Applications of Deep Eutectic Solvents publication trend

The graph below shows the total number of articles in characterization and applications of deep eutectic solvents across all publications each year (not limited to Nature Index journals).

Technical terms

Deep eutectic solvent (DES): A mixture of at least two components (a hydrogen bond acceptor and a donor) that exhibits a melting point lower than its individual constituents.

Hydrogen bond acceptor (HBA): A molecule or ion with lone-pair electrons that can accept a hydrogen bond.

Hydrogen bond donor (HBD): A molecule containing a hydrogen atom capable of forming a hydrogen bond to an acceptor.

Viscosity: A measure of a fluid’s resistance to flow or deformation under shear stress.

Density: Mass per unit volume of a substance, reflecting molecular packing and interactions.

References

1. Revisiting the Physicochemical Properties and Applications of Deep Eutectic Solvents. Molecules (2022).
2. Molecular-Based Guide to Predict the pH of Eutectic Solvents: Promoting an Efficient Design Approach for New Green Solvents. ACS Sustainable Chemistry & Engineering (2021).
3. Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures. Molecules (2021).
4. Meta-analysis of viscosity of aqueous deep eutectic solvents and their components. Scientific Reports (2020).
5. Group contribution and atomic contribution models for the prediction of various physical properties of deep eutectic solvents. Scientific Reports (2021).
6. Estimating the density of deep eutectic solvents applying supervised machine learning techniques. Scientific Reports (2022).
7. Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic–Organic Materials. Chemistry of Materials (2023).
8. Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach. ACS Sustainable Chemistry & Engineering (2024).
9. A comprehensive review on the physicochemical properties of deep eutectic solvents. Results in Chemistry (2024).