Relationship between crystallization behavior, microstructure, and mechanical properties in a palm oil‐based shortening
Bibliographic record
Abstract
Abstract In this study, the effects of cooling rate, degree of supercooling, and storage time on the microstructure and rheological properties of a vegetable shortening composed of soybean and palm oils were examined. The solid fat content vs. temperature profile displayed two distinct regions: from 5 to 25°C, and from 25°C to the end of melt at 45–50°C. A peak melting temperature of 42.7°C was determined by DSC. Discontinuity in the crystallization induction time (determined by pulsed NMR) vs. temperature plot at 27°C also suggested the existence of two separate groups of crystallizing material. Isothermal crystallization kinetics were characterized using the Avrami and Fisher‐Turnbull models. In using DSC and powder X‐ray diffraction, the α polymorph formed upon fast cooling (>5°C/min), and the β′ form predominated at lower cooling rates (<1°C/min). An α to β′ transition took place upon storage. Fractal dimensions (Df) obtained by microscopy and image analysis showed no dependence on the degree of supercooling since Df remained constant (∼1.89) at crystallization temperatures of 5, 22, and 27°C. Crystallization at 22°C at 1°C/min and 15°C/min yielded Df values of 1.98 and 1.93, respectively. Differences in microstructure were observed, and changes in particle properties increased the parameter λ at higher degrees of supercooling.
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) | 0.000 | 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".