Interactions between citrate-capped gold nanoparticles and polymersomes
Bibliographic record
Abstract
Abstract Polymersomes are vesicles formed by self-assembled amphiphilic block copolymers. Polymersomes generally have better stability than liposomes and they have been widely used in making drug delivery vehicles. In this work, the interaction between two types of polymersomes and citrate-capped gold nanoparticles (AuNPs) was studied. The following two polymers: poly(2-methyloxazoline-b-dimethylsiloxane-b-2-methyloxazoline) (called P1) and poly(butadiene-b-ethylene oxide) (called P2) were respectively used to form polymersomes. While P1 only formed spherical vesicle structures, worm-like structures were also observed with P2 as indicated by cryo-TEM. Both polymersomes adsorbed AuNPs leading to their subsequent aggregation. A lower polymersome concentration produced more obvious aggregation of AuNPs as judged from the color change. Capping AuNPs with glutathione inhibited adsorption of AuNPs. Considering the surface property of the polymers, the interaction with AuNPs was likely due to van der Waals forces. P1 polymersomes encapsulated calcein stably and AuNPs did not induce leakage. The P1/AuNP complex was more efficiently internalized by HeLa cells compared to free P1 polymersomes, further indicating a stable adsorption under cell culture conditions. In summary, this work indicates citrate-capped AuNPs form stable adsorption complexes with these polymersomes and their interactions have been explored.
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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) | 0.001 | 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".