A COSMO-RS based first-principles framework for analyzing microemulsion phase diagram trends.

Area of Science:

• Physical Chemistry
• Materials Science
• Computational Modeling

Background:

• Microemulsion phase diagrams are crucial for understanding material properties but lack universal molecular-scale thermodynamic descriptors.
• Existing models often require empirical fitting, limiting their general applicability.
• Predicting phase behavior across temperature and composition is essential for designing advanced materials.

Purpose of the Study:

• To develop a first-principles computational framework for analyzing microemulsion phase behavior.
• To establish universally applicable thermodynamic descriptors for molecular-scale analysis.
• To predict temperature-composition dependent phase behavior without empirical fitting.

Main Methods:

• Combined density functional theory (DFT) with the COSMO-RS model for first-principles calculations.
• Evaluated oil-water interfacial tension (IFT) and surfactant partition coefficient (log(P)) as key indicators.
• Identified minimum-IFT curves and log(P) = 0 conditions to describe phase behavior.

Main Results:

• The model accurately reproduces experimental oil-water IFT and temperature dependencies in binary systems.
• The minimum-IFT curve successfully captures topological features of experimental phase diagrams in ternary systems.
• The log(P) = 0 condition provides an efficient alternative for single-surfactant systems, indicating balanced affinity.

Conclusions:

• The developed framework offers a physically grounded and computationally efficient method for analyzing microemulsion phase behavior.
• This approach provides a route to predict phase diagrams in temperature-composition space without system-specific empirical parameters.
• The identified thermodynamic indicators serve as reliable descriptors for phase inversion and Winsor III region prediction.