Solid-Liquid-Liquid Wettability of Surfactant-Oil-Water Systems and Its Prediction around the Phase Inversion Point

Abstract

Surfactant-oil-water (SOW) systems are important for numerous applications, including hard surface cleaning, detergency, and enhanced oil-recovery applications. There is limited literature on the wettability of solid-liquid-liquid (SLL) systems around the surfactant phase inversion point (PIP), and the few references that exist point to wettability inversion accompanying the microemulsion (μE) phase inversion. Despite the significance of this phenomenon and the extreme changes in contact angles, there are no models to predict SLL wettability as a function of proximity to the PIP. Recent works on SLL wettability in surfactant-free systems suggest that SLL contact angles can be predicted with an extension of Neumann's equation of state (e-EQS) if the interfacial tension (IFT or γ_o-w) is known and if there is a good estimate for the interfacial energy between the wetting phase and the surface (γ_{S-wetting liquid}). In this work, IFT predictions for SOW systems around the PIP were obtained via the combined hydrophilic-lipophilic difference (HLD) and net-average-curvature (NAC) framework. To test the hypothesis that the combined HLD-NAC + e-EQS can predict wettability inversion around the PIP, with a given γ_S-μE, the contact angles (measured through the light oil phase, θ_O) for the μE of sodium dihexyl sulfosuccinate-toluene-saline water system were measured on high surface free energy (SFE) materials (glass, stainless steel, and mica) and on polytetrafluoroethylene (low SFE) around the PIP. Considering that at the PIP, most systems have a contact angle of 90°, an estimated γ_S-μE = 1/4γ_o-w@PIP was found to be suitable for the systems considered in this work and for systems presented in the literature. The largest deviations between the predictions and the experimental values were found in the positive HLD range (surfactant in the light oil phase). Although there is room for improvement, this framework can estimate the wetting behavior of SOW systems starting solely from formulation parameters.