On the clustering of triacylglycerols in the molten state
Bibliographic record
Abstract
The liquid–solid phase transition of triacylglycerols (TAGs), the main components of edible fats and oils is central to the production and sensory properties of many processed foods. While there has been extensive research on the nucleation and growth of fats, there remains a dearth of knowledge regarding the structural organization of TAGs in the liquid state. From a molecular perspective, TAGs consist of three alkyl chains esterified to a glycerol backbone. Several models based on experiment and simulation have helped to unveil TAG organization in the molten state. However, more evidence for their structural organization is necessary. Here, we provide simulation and experimental insights on the structural organization of molten tripalmitin using small-angle neutron and x-ray scattering, and wide-angle x-ray scattering. In agreement with recent work, we also propose a model in which TAGs associate as clusters via glycerol-glycerol interactions, with their alkyl chains extending outwards in a loose shell. Our model, however, highlights and demonstrates the dynamic nature of clusters, where TAGs can transfer from one cluster to another via diffusion. The average number of TAG molecules per cluster varies from 5 to 9 and decreases with increasing temperature, which results in a smaller average distance between clusters. Overall, this study strongly suggests that prior to the onset of nucleation, TAGs are associated as dynamic clusters formed via intermolecular interactions between neighboring glycerol cores.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".