Oleic Acid Phase Behavior from Molecular Dynamics Simulations
Bibliographic record
Abstract
Fatty acid aggregation is important for a number of diverse applications: from origins of life research to industrial applications to health and disease. Experiments have characterized the phase behavior of oleic acid mixtures, but the molecular details are complex and hard to probe with many experiments. Coarse-grained molecular dynamics computer simulations and free energy calculations are used to model oleic acid aggregation. From random dispersions, we observe the aggregation of oleic acid monomers into micelles, vesicles, and oil phases, depending on the protonation state of the oleic acid head groups. Worm-like micelles are observed when all the oleic acid molecules are deprotonated and negatively charged. Vesicles form spontaneously if significant amounts of both neutral and negative oleic acid are present. Oil phases form when all the fatty acids are protonated and neutral. This behavior qualitatively matches experimental observations of oleic acid aggregation. To explain the observed phase behavior, we use umbrella sampling free energy calculations to determine the stability of individual monomers in aggregates compared to water. We find that both neutral and negative oleic acid molecules prefer larger aggregates, but neutral monomers prefer negatively charged aggregates and negative monomers prefer neutral aggregates. Both neutral and negative monomers are most stable in a DOPC bilayer, with implications on fatty acid adsorption and cellular membrane evolution. Although the CG model qualitatively reproduces oleic acid phase behavior, we show that an updated polarizable water model is needed to more accurately predict the shift in pKa for oleic acid in model bilayers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".