Solid–Liquid–Liquid Wettability of Surfactant–Oil–Water Systems and Its Prediction around the Phase Inversion Point
Bibliographic record
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 γo–w) is known and if there is a good estimate for the interfacial energy between the wetting phase and the surface (γS–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 γS−μE, the contact angles (measured through the light oil phase, θO) 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 γS−μE = 1/4γo–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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".