Application of a Ternary Phase Diagram to the Phase Separation of Oil‐in‐Water Emulsions Using Isopropyl Alcohol
Bibliographic record
Abstract
Abstract The oil‐in‐water emulsion formed during an aqueous extraction of yellow mustard seed flour was destabilized using isopropyl alcohol (IPA) in a four stage extraction process, with concurrent recovery of oil and water in separate phases. The emulsion was extracted using two different approaches: phase separation extraction (PSE) that used fresh IPA as the extraction solvent at each stage, and phase separation extraction with recycle (PSER) that reused the extracted water‐rich phase, containing IPA, as the extraction solvent. Extraction processes by both approaches were modeled by the ternary liquid phase diagram of IPA, canola oil and water to characterize the extraction progress. PSER resulted in improved oil–water separation and IPA usage efficiency than PSE, but achieved only 84.0 % oil recovery, compared to 92.3 % by PSE. The ternary diagram of IPA, canola oil and water offered good approximation of the oil and water separation behavior of PSE and PSER by closely predicting the compositions of the separated phases; however, the weight ratio of the separated phases were not as closely predicted.
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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".