Production of Unilamellar Vesicles Using an Inverted Emulsion
Bibliographic record
Abstract
We investigate a method for the controlled assembly of unilamellar vesicles consisting of bilayers assembled one leaflet at a time. We use water-in-oil emulsions stabilized by the material for the inner leaflet and produce vesicles by passing the water droplets through a second oil−water interface, where they become coated with the outer leaflet. We have used this technique to form vesicles from lipids, mixed lipid and surfactant systems, and diblock copolymers. The stability of lipid-stabilized emulsions limits the range of sizes that can be produced and the vesicle yield; nevertheless, there are several advantages with this emulsion-based technique: It is possible to make unilamellar vesicles with sizes ranging from 100 nm to 1 μm. Moreover, the process allows for efficient encapsulation and ensures that the contents of the vesicles remain isolated from the continuous aqueous phase. To illustrate possible applications of this technique, we demonstrate the use of vesicles as microreactors where we polymerize actin through the addition of magnesium and show that the polymerization kinetics are unaffected by the encapsulation.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".