Molecular View of Phase Coexistence in Lipid Monolayers
Bibliographic record
Abstract
We used computer simulations to study the effect of phase separation on the properties of lipid monolayers. This is important for understanding the lipid-lipid interactions underlying lateral heterogeneity (rafts) in biological membranes and the role of domains in the regulation of surface tension by lung surfactant. Molecular dynamics simulations with the coarse-grained MARTINI force field were employed to model large length (~80 nm in lateral dimension) and time (tens of microseconds) scales. Lipid mixtures containing saturated and unsaturated lipids and cholesterol were investigated under varying surface tension and temperature. We reproduced compositional lipid demixing and the coexistence of liquid-expanded and liquid-condensed phases as well as liquid-ordered and liquid-disordered phases. Formation of the more ordered phase was induced by lowering the surface tension or temperature. Phase transformations occurred via either nucleation or spinodal decomposition. In nucleation, multiple domains formed initially and subsequently merged. Using cluster analysis combined with Voronoi tessellation, we characterized the partial areas of the lipids in each phase, the phase composition, the boundary length, and the line tension under varying surface tension. We calculated the growth exponents for nucleation and spinodal decomposition using a dynamical scaling hypothesis. At low surface tensions, liquid-ordered domains manifest spontaneous curvature. Lateral diffusion of lipids is significantly slower in the more ordered phase, as expected. The presence of domains increased the monolayer surface viscosity, in particular as a result of domain reorganization under shear.
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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) | 0.002 | 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".