Pre‐Nucleation Structuring of TAG Melts Revealed by Fluorescence Polarization Spectroscopy and Molecular Mechanics Simulations
Bibliographic record
Abstract
Abstract We have studied the pre‐nucleation behavior of tripalmitin (TP) and tristearin (TS) in blends with triolein (TO), high oleic safflower oil (HOSfO) and soybean oil (SBO) by means of fluorescence polarization spectroscopy (FPS) and molecular mechanics simulations (MM). The FPS measurements at different temperatures showed that there is an increase in the anisotropy of the TP:HOSfO and TP:SBO blends as opposed to the TP:TO sample. This increase is directly related to an increase in the microviscosity of the blend which is interpreted as a structuring step prior to the nucleation and growth of the crystals. A similar but less pronounced effect was also observed in the TS:SBO blends. We performed MM simulations in an attempt to understand the molecular interactions responsible for this behavior. The simulation results have shown that short range van der Waals (vdW) interactions are the ones responsible for the increase in the microviscosity of the blends prior to crystallization. Our results also indicate that the presence of molecules that contain at least one chain of palmitic acid in their triglyceride (TAG) composition will induce a pre‐nucleation increase in the microviscosity of the blend in both TP and TS containing systems. Lastly, we studied the applicability of these conclusions to longer chain TAG analogues. Our MM results show that hypothetical blends of TS and TAGs containing stearic acid in their structure, will not have a low enough vdW energy to account for an increase in the microviscosity. Hence, there seems to be a specific interaction particularly favorable when the oil contains TAGs with at least one palmitic acid chain.
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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 | 0.001 |
| 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".