Thermal and Mechanical Properties of Cocoa Butter Crystallized under an External Laminar Shear Field
Bibliographic record
Abstract
In this work, the effects of laminar shear on nanoscale structure and physical properties of triglyceride crystal networks were quantified. Cocoa butter (CB) was crystallized in the presence and absence of an external shear field. Samples were crystallized under a shear rate of approximately 340 s –1 in two different ways, by using a continuous Couette-type laminar shear crystallizer and by using a standard paddle mixer. Using different analytical methods and physical models, the mechanical strength and thermal properties of the crystallized CB were characterized. Cocoa butter crystallized under an external shear field had a stronger network with higher breaking force, Young’s, and storage modulus than statically crystallized samples. The sheared samples also had a smaller crystallite size (300 nm length, 130 nm width). Here we also show that the arrangement of the crystalline component influences the material’s strength. The Young’s modulus was increased by about 30% in samples that exhibited crystalline alignment. Moreover, higher average peak melting temperature (∼0.8 °C) and full width at half maximum of the thermogram curves, fwhm, (∼0.9 °C) were observed for the statically crystallized samples. In the presence of crystalline alignment, the melting point of cocoa butter was decreased slightly, about 0.5 °C. This work demonstrates the substantial effects of laminar shear applied during crystallization of cocoa butter on its functional properties including melting, hardness, and rigidity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".