Bibliographic record
Abstract
Clove oil is an essential oil used in food and pharmaceutical applications, with a market value of 300+ million dollars per year. Microemulsions have been used as effective clove oil delivery vehicles and could also be used to develop new extraction processes from clove buds. Eugenol, the main component of clove oil, is a polar oil that behaves as a surfactant and as an oil. This bifunctional behavior makes formulating clove oil microemulsions a challenging task. Here, we used a version of the Hydrophilic–Lipophilic Difference (HLD) + Net-Average Curvature (NAC) model that incorporates the bifunctional polar oil model to predict and fit the phase behavior of lecithin (surfactant) + polyglycerol-10 caprylate (hydrophilic linker) microemulsions using mixtures of heptane and clove oil as the oil phase. Using HLD-NAC parameters from the literature, the predicted HLD-NAC curves reproduced the expected phase transitions and the trends in Eugenol segregation toward the surfactant layer. Using these literature parameters as an initial guess to fit the experimental phase volumes produced accurate calculated phase volumes, and predicted interfacial tensions. This work demonstrates the application of heuristics and databases of HLD-NAC parameters in predicting the complex phase behavior of surfactant–oil–water (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 | 0.001 |
| 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".