Extraction of Clove Oil with Lecithin-based Microemulsions Guided by the Hydrophilic-Lipophilic-Difference (HLD) and Net-Average-Curvature (NAC) Model
Bibliographic record
Abstract
Current methods for extracting herbal oils, including steam and solvent extraction, suffer from low yields, using large volumes of solvents, and for being time and energy intensive. Capillary displacement, as an emerging extraction method, uses dilute surfactant solutions to promote low interfacial tensions to reduce the capillary pressure that retains triglycerides on the seed matrix. This work adapts this strategy to extract polar oils, specifically eugenol. Polar oils are compounds with dual, surfactant- and oil-like behaviour and their high affinity for the surfactant alters the phase behavior and make separating them from the surfactant difficult. The Hydrophilic-Lipophilic-Difference and Net-Average-Curvature + polar oil models are applied to predict the influence of eugenol in lecithin-based microemulsions and as a guide to develop an extraction process called Solvent-Addition and Self-Emulsification, that including a recycling step for the aqueous phase, produced 18% yield at room temperature and pressure with 5 minutes of extraction time.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".