Nanoscale characteristics of triacylglycerol oils: phase separation and binding energies of two-component oils to crystalline nanoplatelets
Bibliographic record
Abstract
Fats are elastoplastic materials with a defined yield stress and flow behavior and the plasticity of a fat is central to its functionality. This plasticity is given by a complex tribological interplay between a crystalline phase structured as crystalline nanoplatelets (CNPs) and nanoplatelet aggregates and the liquid oil phase. Oil can be trapped within microscopic pores within the fat crystal network by capillary action, but it is believed that a significant amount of oil can be trapped by adsorption onto crystalline surfaces. This, however, remains to be proven. Further, the structural basis for the solid-liquid interaction remains a mystery. In this work, we demonstrate that the triglyceride liquid structure plays a key role in oil binding and that this binding could potentially be modulated by judicious engineering of liquid triglyceride structure. The enhancement of oil binding is central to many current developments in this area since an improvement in the health characteristics of fat and fat-structured food products entails a reduction in the amount of crystalline triacylglycerols (TAGs) and a relative increase in the amount of liquid TAGs. Excessive amounts of unbound, free oil, will lead to losses in functionality of this important food component. Engineering fats for enhanced oil binding capacity is thus central to the design of more healthy food products. To begin to address this, we modelled the interaction of triacylglycerol oils, triolein (OOO), 1,2-olein elaidin (OOE) and 1,2-elaidin olein (EEO) with a model crystalline nanoplatelet composed of tristearin in an undefined polymorphic form. The surface of the CNP in contact with the oil was assumed to be planar. We considered pure OOO and mixtures of OOO + OOE and OOO + EEO with 80% OOO. The last two cases were taken as approximations to high oleic sunflower oil (HOSO). The intent was to investigate whether phase separation on a nanoscale took place. We defined an "oil binding capacity" parameter, B(Q,Q'), relating a state Q to a reference state Q'. We used atomic scale molecular dynamics in the NVT ensemble and computed averages over 1-5 ns. We found that the probability of the OOE phase separating into a layer on the surface of the CNP compared to being retained randomly in an OOO + OOE mix were approximately equal. However, we found that it was probable that the EEO component of an OOO + EEO mix would phase separate and coat the surface of the CNP. These results suggest a mechanism whereby many-component oils undergo phase separation on a nanoscale so as to create a transition oil region between the surface of the CNP and the bulk major oil component (OOO in the case considered here) so as to create the appropriate oil binding capacity for the use to which it is put.
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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 | 0.000 | 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) | 0.003 | 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".