Coupled Interfacial Tension and Phase Behavior Model Based on Micellar Curvatures
Bibliographic record
Abstract
This article introduces a consistent and robust model that predicts interfacial tensions for all microemulsion Winsor types and overall compositions. The model incorporates film bending arguments and Huh's equation and is coupled to phase behavior so that simultaneous tuning of both interfacial tension (IFT) and phase behavior is possible. The oil-water interfacial tension and characteristic length are shown to be related to each other through the hydrophilic-lipophilic deviation (HLD). The phase behavior is tied to the micelle curvatures, without the need for using the net average curvature (NAC). The interfacial tension model is related to solubilization ratios in order to introduce a coupled interfacial tension-phase behavior model for all phase environments. The approach predicts two- and three-phase interfacial tensions and phase behavior (i.e., tie lines and tie triangles) for changes in composition and HLD input parameters, such as temperature, pressure, surfactant structure, and oil equivalent alkane carbon number. Comparisons to experimental data show excellent fits and predictive capability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".