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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".