Superparamagnetic-Oil-Filled Nanocapsules of a Ternary Graft Copolymer
Bibliographic record
Abstract
Stearic and oleic acid-coated Fe3O4 nanoparticles were dispersed in decahydronaphthalene (DN). This oil phase was dispersed in water using ternary graft copolymer poly(glycidyl methacrylate)-graft-[polystyrene-ran-(methoxy polyethylene glycol)-ran-poly(2-cinnamoyloxyethyl methacrylate)] or PGMA-g-(PS-r-MPEG-r-PCEMA) to yield capsules. The walls of these capsules were composed of PCEMA chains that were soluble in neither water nor DN, and the DN-soluble PS chains stretched into the droplet phase and the water-soluble MPEG chains extended into the aqueous phase. Structurally stable capsules were prepared by photolyzing the capsules with UV light to cross-link the PCEMA layer. Both the magnetite particles and the magnetite-containing capsules were superparamagnetic. The sizes of the capsules increased as they were loaded with more magnetite nanoparticles, reaching a maximal loading of ~0.5 mg of ligated magnetite nanoparticles per mg of copolymer. But the radii of the capsules were always <100 nm. Thus, a novel nanomaterial--superparamagnetic-oil-filled polymer nanocapsules--was prepared. The more heavily loaded capsules were readily captured by a magnet and could be redispersed via shaking. Although the cross-linked capsules survived this capturing and redispersing treatment many times, the un-cross-linked capsules ruptured after four cycles. These results suggest the potential to tailor-make capsules with tunable wall stability for magnetically controlled release 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 | 0.000 |
| 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) | 0.000 | 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".