On the optimisation of water droplet encapsulation within fat crystal shells
Bibliographic record
Abstract
Novel means of water droplet encapsulation within solid matrices are sought for applications ranging from bioactive delivery to flavour and texture control. Model water-in-oil emulsions containing a 10 wt% dispersed aqueous phase were formulated using a continuous phase composed of soybean oil and hydrogenated soybean oil. Polyglycerol polyricinoleate and glycerol monooleate were incorporated in order to stabilize the emulsion. Encapsulation was achieved by applying controlled shear during cooling from 80 to 20 °C on a rheometer stage. Cooling rates of 0.5, 1.0, or 2.0 °C/min and shear rates of 500, 1000, or 2000 s -1 were investigated. Polarized light microscopy confirmed the formation of fat crystal shells encapsulating aqueous droplets under most conditions, with the exception of the highest shear (2000 s -1 ) combined with the slowest cooling rate (0.5 °C/min). Higher shear rates generally produced smaller water droplets and more spherical fat crystal shells whereas slower cooling rates promoted larger spheroidal shells and the occurrence of multi-droplet encapsulation. The fat phase exhibited varying β′/ β ratios depending on the shear-cooling conditions used. Overall, this study demonstrated that the morphology of water droplets encapsulated within crystalline fat shells can be tailored by controlling shear and cooling rates.
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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) | 0.000 | 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 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".