Origins of Microstructural Transformations in Charged Vesicle Suspensions: The Crowding Hypothesis
Bibliographic record
Abstract
It is observed that charged unilamellar vesicles in a suspension can spontaneously deflate and subsequently transition to form bilamellar vesicles, even in the absence of externally applied triggers such as salt or temperature gradients. We provide strong evidence that the driving force for this deflation-induced transition is the repulsive electrostatic pressure between charged vesicles in concentrated suspensions, above a critical effective volume fraction. We use volume fraction measurements and cryogenic transmission electron microscopy imaging to quantitatively follow both the macroscopic and microstructural time-evolution of cationic diC18:1 DEEDMAC vesicle suspensions at different surfactant and salt concentrations. A simple model is developed to estimate the extent of deflation of unilamellar vesicles caused by electrostatic interactions with neighboring vesicles. It is determined that when the effective volume fraction of the suspension exceeds a critical value, charged vesicles in a suspension can experience "crowding" due to overlap of their electrical double layers, which can result in deflation and subsequent microstructural transformations to reduce the effective volume fraction of the suspension. Ordinarily in polydisperse colloidal suspensions, particles interacting via a repulsive potential transform into a glassy state above a critical volume fraction. The behavior of charged vesicle suspensions reported in this paper thus represents a new mechanism for the relaxation of repulsive interactions in crowded situations.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".