Cooling rate and dilution affect the nanostructure and microstructure differently in model fats
Why this work is in the frame
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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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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 | 0.001 |
| 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