Modeling the Physicochemical Properties of Natural Deep Eutectic Solvents

Area of Science:

• Green Chemistry
• Materials Science
• Computational Chemistry

Background:

• Natural deep eutectic solvents (NADES) are mixtures of natural compounds with lowered melting points.
• They possess advantageous properties like low toxicity and volatility, making them sustainable alternatives to organic solvents.
• Understanding and predicting NADES behavior requires modeling their composition-property relationships.

Purpose of the Study:

• To review and structure current knowledge on NADES properties and their relationship to molecular structure.
• To survey available modeling methods for predicting NADES behavior.
• To outline future research directions for developing tailorable NADES.

Main Methods:

• Review of experimental data correlating NADES structure and properties.
• Analysis of molecular modeling techniques (DFT, molecular dynamics).
• Evaluation of continuum solvation models (COSMO-RS) and equation of state models (PC-SAFT).
• Discussion of data-driven approaches (group-contribution, machine learning).

Main Results:

• Molecular modeling aids in interpreting density, spectra, and interaction energies.
• Continuum and equation of state models predict bulk properties.
• Data-driven methods, especially machine learning, show promise for quantitative predictions.
• Combined multiscale modeling enhances predictability of phase equilibria and physical properties.

Conclusions:

• A comprehensive understanding of NADES requires integrating molecular and bulk-medium modeling approaches.
• Multiscale modeling is crucial for accurately predicting NADES properties and solute interactions.
• This approach will enable the rational design of truly tailorable NADES for specific (bio)chemical applications.