Continuous‐phase fat crystals strongly influence water‐in‐oil emulsion stability
Why this work is in the frame
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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 ( d 33 ) 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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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 | 0.001 | 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) | 0.000 | 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 it