Spheroidal Fat Crystals: Structure Modification via Use of Emulsifiers
Bibliographic record
Abstract
Emulsifiers were used to modify the kinetics of formation and morphology of spheroidal fat crystal assemblies generated via the confined gap shear-cooling of model fat systems consisting of fully hydrogenated canola oil (FHCO), canola oil (CO), and glycerol monostearate (GMS), glycerol monopalmitate (GMP), sorbitan monostearate (SMS), or sorbitan tristearate (STS). The presence and morphology of spheroidal fat crystals were dependent on emulsifier type and concentration, with GMS, GMP, and SMS significantly reducing spheroid size at lower concentrations and causing deformation of the crystals into irregular and elongated shapes at higher concentrations. STS significantly hindered the formation of spheroidal crystals and instead promoted globular crystalline assemblies. Thermal analysis and X-ray diffraction revealed that samples crystallized primarily into the β′ polymorph along with a smaller amount of α polymorph. The ratio of α to β′ polymorphs generally increased with emulsifier concentration and imposition of higher laminar shear rates, suggesting the importance of two phenomena: (i) shear may influence the interactions between triacylglycerols at a molecular level, affecting the type of nanoplatelets formed and their interactions to form crystallites and subsequent crystals, and (ii) the extent of shear (rate/duration) may affect the rate and/or amount of emulsifier incorporated into triacylglycerol crystal lattices. Overall, these results demonstrated that fat crystal structures may be tailored with the combined use of emulsifiers and laminar shear.
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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".