Triacylglycerol Interfacial Crystallization and Shear Structuring in Water-in-Oil Emulsions
Bibliographic record
Abstract
We demonstrate the collective roles of surfactant–triacylglycerol interactions and confined gap shear crystallization on the creation of novel microstructured water-in-oil emulsions containing solid lipid-encapsulated water droplets. The emulsions studied consisted of 20 wt % water dispersed in a mixture of canola oil, a stabilizing fat [hydrogenated canola oil (HCO)] and either glycerol monoleate (GMO) or polyglycerol polyricinoleate (PGPR) as surfactants. Following valve homogenization, emulsions were cooled from 70 to 25 °C either in a stirred beaker (bulk-cooling) or on a rheometer stage (confined gap shear-cooling). Irrespective of the cooling protocol, GMO promoted HCO nucleation at the oil–water interface and later in the continuous phase, providing combined Pickering and network stabilization. With PGPR, HCO nucleated in the continuous phase with little evidence of interfacial nucleation. Bulk-cooling resulted in spherulitic HCO crystalline aggregates, whereas in a confined gap, ellipsoidal crystalline masses (crystal cocoons) were created. The presence of GMO led to the inclusion of the dispersed aqueous phase within these cocoons, whereas with PGPR, no such droplet encapsulation was observed. It is proposed that molecular compatibility between the oleic acid in GMO and the stearic acids in HCO permitted their liquid-state association and thus HCO nucleation and crystallization on the droplet surface, whereas PGPR’s lack of complementarity did not promote such nucleation. The formation of such crystal cocoons enclosing water droplets represents the first instance of this new class of Pickering-type emulsion stabilization.
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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".