Simple Engineering of Polymer–Nanoparticle Hybrid Nanocapsules
Bibliographic record
Abstract
Abstract A general method to generate hybrid hollow capsules is reported. The process is based on the stabilization of solvent droplets by nanoparticles, in macroscopically miscible mixtures of tetrahydrofuran (THF) and water. After addition of a crosslinking polymer and removal of the solvent core, capsules of diameter ca 100 nm are obtained. This novel strategy does not require the use of block copolymers. In contrast, most methods reporting the formation of hybrid nanocapsules incorporate nanoparticles into block‐copolymer polymersomes or use nanoparticles tethered with block‐copolymers. The nanocapsules were characterized using a full set of techniques including nanoparticle tracking analysis, electron microscopy and liquid phase atomic force microscopy. Our results show that the vesicular shape of the nanocapsules is templated by the liquid droplets. Nanocapsules were prepared from quantum dots, gold nanoparticles, superparamagnetic iron oxide nanoparticles and mixtures of particles. The entrapment of a fluorescent dye was also demonstrated. Thus, nanocapsules with dual properties (e.g., magnetic and fluorescent) are easily obtained. Interestingly, the magnetic nanocapsules enable magnetic resonance imaging contrast enhancement of tumors in vivo.
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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 | 0.001 |
| 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".