Effects of Shear and Cooling Rate on the Crystallization Behavior and Structure of Cocoa Butter: Shear Applied During the Early Stages of Nucleation
Bibliographic record
Abstract
Here we investigated the effects of applied shear and temperature during the early stages of nucleation on the isothermal crystallization behavior and microstructure of cocoa butter (CB). Results showed that the composition of nucleating triacylglycerols (TAGs) as well as crystalline microstructure and polymorphism of CB were affected by mixing and temperature gradients while still in the molten state. The initial crystalline material isolated from CB after it had been subjected to shear had a similar TAG composition as native CB. However, in the absence of shear, high melting TAGs such as trisaturates (SSS) along with lower amounts of monounsaturated TAGs (SUS) were present, possibly due to fractionation. After subjecting CB to shear in its molten state, crystallization rates were faster due to the cocrystallization of different TAGs into a mixed crystal; however, the polymorphic transition into the more stable β-V form was found to be slower due to inherent complexity in TAG composition. Under static conditions, the presence of high amounts of homogeneous TAGs (SSS) was correlated to faster polymorphic transformations possibly due to a templating effect.
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 | 0.001 |
| 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 | 0.001 |
| 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".