Continuous‐phase fat crystals strongly influence water‐in‐oil emulsion stability
Bibliographic record
Abstract
Abstract To elucidate the role of continuous‐phase fat crystals on emulsion destabilization, water‐in‐canola oil emulsions prepared with 0–2% (w/w) added solid fat (hydrogenated canola stearine or hydrogenated cottonseed stearine) were examined using pulsed NMR droplet‐size analysis, sedimentation, and microscopy. Droplet‐size analysis showed that addition of either fat prior to emulsification (precrystallized fat) or fat quench‐crystallized in situ following emulsification (postcrystallized fat) decreased the degree of droplet coalescence, based on volume‐weighted (d33) mean droplet diameters, with postcrystallized emulsions being more stable against coalescence. Sedimentation studies corroborated these results, with greatly enhanced stability against sedimentation in postcrystallized emulsions. Precrystallized fat had very little effect on emulsion sedimentation at levels as high as 2% (w/w). Postcrystallized cottonseed stearine produced slightly less resistant emulsions than did canola stearine, even if both were in the β‐form. Surface energetics revealed that canola stearine had greater affinity for the oil/water interface and hence a greater displacement energy. The presence of micronsized (Pickering) crystals located directly at the droplet interface, resulting from in situ crystallization or generated by the shearing of precrystallized fats, provided enhanced stability vis‐à‐vis preformed crystals. These stabilized emulsions via the formation of crystal networks that partially immobilized droplets.
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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 | 0.000 |
| 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".