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

The phase diagram of the microemulsion system as a function of composition and temperature serves as the fundamental thermodynamic basis for understanding the overall material properties of the system. However, a generally applicable thermodynamic descriptor at the molecular scale that captures these phase behavior trends without empirical fitting has not yet been established. In this work, a first-principles computational framework combining density functional theory (DFT) and the COSMO-RS model was developed to analyze temperature-composition dependent phase behavior in nonionic microemulsion systems. Two key thermodynamic indicators were systematically evaluated: the oil-water interfacial tension (IFT) and the surfactant partition coefficient between oil and water phases, log(P). For each surfactant concentration, the temperature corresponding to the minimum in interfacial tension was identified, defining a minimum-IFT curve associated with the most favorable interfacial free-energy balance. In parallel, the log(P) = 0 condition was used to indicate balanced surfactant affinity between the two phases. These two indicators were examined as thermodynamically grounded descriptors of phase inversion behavior and the Winsor III region. The model quantitatively reproduces experimental oil-water interfacial tensions and their temperature dependence in binary systems. For ternary systems, the minimum-IFT curve captures the key topological features of experimental fish-type phase diagrams across diverse formulations, providing a physically motivated indicator for the Winsor III region. The log(P) = 0 condition offers a computationally efficient alternative for single-surfactant systems, albeit with increased sensitivity to surfactant composition. Overall, this framework provides a physically grounded and computationally efficient route for analyzing microemulsion phase behavior in temperature-composition space, without relying on system-specific empirical fitting parameters.