HLD–NAC and the Formation and Stability of Emulsions Near the Phase Inversion Point
Bibliographic record
Abstract
It is well-known that surfactant–oil–water (SOW) emulsions undergo substantial changes in drop size (10-fold or more) and stability (up to 4 orders of magnitude) near the phase inversion point. Predicting these changes is important in numerous applications. However, the complex connection between composition, formulation properties, and hydrodynamic conditions limits the ability to predict the outcome of emulsification and demulsification processes. To address this gap, the hydrophilic–lipophilic deviation (HLD) was used to quantify the proximity to the inversion point, considering the composition of the formulation, temperature, and electrolyte concentration. The net-average-curvature (NAC) equations combined with the HLD predicted the density, interfacial tension, interfacial rigidity, and viscosity for the sodium dihexyl sulfosuccinate (SDHS)–toluene–water system. The predicted properties were incorporated in hydrodynamic models to predict the initial emulsion drop size. The calculated properties and initial drop size were then used in a modified version of the Davies and Rideal coalescence model that incorporates hole nucleation theory to predict emulsion stability. The predictions were consistent with the changes in emulsion drop size and stability around the phase inversion obtained for the SDHS–toluene–water system, and with stability values reported in the literature for ionic and nonionic SOW systems.
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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) | 0.000 | 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) | 0.001 | 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 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".