Cooling rate and dilution affect the nanostructure and microstructure differently in model fats
Bibliographic record
Abstract
Abstract The effects of cooling rate and solid mass fraction on the polymorphism, nano and microstructure, thermal and rheological properties of binary mixtures of fully hydrogenated canola oil and canola oil at 20°C have been studied. The β‐polymorph was observed in fully hydrogenated canola oil (FHCO) when crystallized at slow cooling rates (0.1C°/min), however crystallization at higher cooling rates (0.7 and 10°C/min) resulted in the formation of the α form. The β‐polymorph was detected in all the binary mixtures of FHCO/canola oil and was not affected by crystallization at different cooling rates. Melting thermograms obtained from 100% FHCO displayed three melting peaks, associated with the development of the β‐polymorph via α→ β′→ β‐polymorphic transition in the DSC pan. Some solubilization of solid FHCO into canola oil was observed and the solubility was proportionally higher with increasing liquid oil fraction. The strong influence of the matrix concentration on micro/nanoscale structure was demonstrated by characterization of crystal size using cryogenic transmission electron (Cryo‐TEM) and polarized light microscopy (PLM). Crystallization under higher cooling rates lead to formation of smaller nano and meso‐structural elements. Furthermore, oscillatory rheology showed the influence of structural elements' size and polymorphism on material strength. The shear storage modulus (G′) of the mixtures was higher when crystallized at fast cooling rates (10°C/min). In contrast, for pure FHCO, G′ increased by lowering the cooling rate and the highest storage modulus was observed after crystallization at 0.1°C/min.
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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".