Oil binding capacities of triacylglycerol crystalline nanoplatelets: nanoscale models of tristearin solids in liquid triolein
Bibliographic record
Abstract
Polycrystalline particles composed of triacylglycerol (TAG) molecules, and their networks, in anhydrous TAG oils find extensive use as edible oils in the food industry. Although modelling studies of TAG systems, have been carried out, none have attempted to address a problem of central concern to food science and technology: the "oil binding capacity" of a system of such edible oils. Crystalline nanoparticles (CNPs) have recently been identified as the fundamental components of solid fats in oils. Oil binding capacity is an important concept regarding the ability of fats particles to retain oil, and the ability of these CNPs to bind oil is important in designing healthy foods. We have carried out atomic scale molecular dynamics computer simulations to understand the behavior of a triacylglycerol oil (triolein) in nanoscale confinements between tristearin CNPs. We define a nanoscale oil binding capacity function by utilizing the average oil number density, 〈Φ(d)〉, between two CNPs as a function of their separation, d. We modelled pure tristearin CNPs as well as tristearin CNPs in which the surfaces are covered with an interface comprising soft permanent coatings. Their surfaces are "hard" and "soft" respectively. We found that for a pair of hard-surface tristearin CNPs a distance d apart, (i) triolein exhibits number density, and therefore density, oscillations as a function of d, and (ii) the average number density between two such CNPs decreases as d decreases, viz. the oil binding capacity is lowered. When a soft layer of oil covers the CNP surfaces, we found that the oscillations are smeared out and that the average number density between the two CNPs remained approximately constant as d decreased indicating a high oil binding capacity. Our results might have identified important nanoscale aspects to aid in healthy food design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".