Contact-line bending energy controls phospholipid vesicle adhesion
Bibliographic record
Abstract
Phospholipid bilayer bending energy is often discarded in the analysis of vesicle adhesion on the basis of a dimensionless parameter w = − Δ U R 0 2 / κ b ≫ 1 (interaction energy Δ U , spherical radius R 0 , bending rigidity κ b ), considered a regime of strong adhesion. In this study, we propose a model by which bending energy in a singular proximity of the contact line balances the adhesion energy. This is developed for a regime in which the membrane correlation length ξ is small compared with the vesicle radius R 0 , so a spherical cap with circular footprint presents an effective contact angle θ . Experiments are conducted in which the adhesion of 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC) vesicles to hyaluronic acid hydrogel substrates is controlled by tuning the van der Waals attraction with systematic change in the hydrogel concentration. Theoretical interpretation of the data furnishes a dimensionless model parameter α ≈ 2 – 10 for contact angles θ ≈ 20 – 80 ∘ , beyond which vesicles collapse into discs. We show that the van der Waals interaction energy varies in the range − Δ U ≈ 0.14 – 0.68 μ J m − 2 in response to varying the hydrogel concentration in a range c ha ≈ 2 – 10 %. The analysis provides a foundation for exploring vesicle–hydrogel interactions with electro-steric influences; these are poorly understood but pertinent in a wide variety of biological and technological applications.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".