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

Area of Science:

• Colloid and Surface Science
• Physical Chemistry
• Materials Science

Background:

• Surfactant-oil-water (SOW) systems are vital for cleaning, detergency, and enhanced oil recovery.
• Limited understanding exists regarding solid-liquid-liquid (SLL) system wettability around the surfactant phase inversion point (PIP).
• Wettability inversion in SLL systems is known to accompany microemulsion (μE) phase inversion, but predictive models are lacking.

Purpose of the Study:

• To develop and test a model predicting SLL wettability in SOW systems near the PIP.
• To evaluate the combined hydrophilic-lipophilic difference (HLD) and net-average-curvature (NAC) framework with an extended equation of state (e-EQS) for wettability prediction.
• To investigate the relationship between interfacial tension (IFT), surface energy, and contact angles around the PIP.

Main Methods:

• Interfacial tension (IFT) predictions for SOW systems near the PIP were obtained using the HLD-NAC framework.
• Contact angles (θO) for a specific microemulsion (sodium dihexyl sulfosuccinate-toluene-saline water) were measured on various surfaces (glass, stainless steel, mica, polytetrafluoroethylene) around the PIP.
• An empirical relationship for surface-microemulsion interfacial energy (γS-μE) was proposed and validated.

Main Results:

• The combined HLD-NAC + e-EQS framework, with an estimated γS-μE = 1/4γo-w@PIP, successfully predicted wettability inversion around the PIP for the studied systems.
• Contact angle predictions showed the largest deviations in the positive HLD range, corresponding to the surfactant being in the light oil phase.
• The model demonstrates the capability to estimate wetting behavior based solely on formulation parameters.

Conclusions:

• A predictive framework combining HLD-NAC and e-EQS can estimate SLL wettability in SOW systems near the PIP.
• The proposed relationship for γS-μE provides a suitable approximation for predicting wettability inversion.
• This approach offers a valuable tool for optimizing SOW formulations for specific wetting behaviors in various applications.