Preparation and lower critical solution temperature behavior investigation of new thermoresponsive poly(<i>N</i>-isopropylacrylamide-co-phthalocyanine) magnetic nanocomposites containing phthalocyanine-coated Fe<sub>3</sub>O<sub>4</sub> hybrid
Bibliographic record
Abstract
New thermoresponsive poly(N-isopropyl acrylamide-co-phthalocyanine) magnetic nanocomposites were prepared by in situ dispersion polymerization. 4-Nitrophthalic acid and CoCl2 were employed to synthesize tetranitrophthalocyanine and then it was converted to tetraaminophthalocyanine by sodium sulfide. The cobalt tetra(N-carbonylacrylic)aminophthalocyanine monomer was obtained by reaction of tetraaminophthalocyanine with maleic anhydrid. N-isopropylacrylamide as the main monomers, N,N′-methylenebisacrylamide as the cross-linker, poly(N-vinylpyrrolidone) as the steric stabilizer, potassium persulfate as the initiator, and new Fe-phthalocyanine oligomer/Fe3O4 nanohybrid particles (FePc/Fe3O4) as nanoparticles were used. The magnetite nanocomposites were characterized by Fourier-transform infrared spectrum, X-ray diffraction spectroscopy, scanning electron microscopy, thermogravimetric analysis, vibrating sample magnetometer, and differential scanning calorimetry. The results showed that the lower critical solution temperatures of the hydrogel nanocomposits were influenced by the content of FePc/Fe3O4 hybrid nanoparticles. The lower critical solution temperatures of the magnetic hydrogel nanocomposites F3 and F4 were at about 34 and 40 °C. The results show that the increase of FePc/Fe3O4 nanoparticle content caused the LCSTs of the hydrogels to increase. FePc/Fe3O4 nanoparticles were prepared from 4,4′-isopropyliden-bis-dioxydiphthalonitrile and FeCl3·6H2O via the solvothermal route. The sizes of nanoparticles were determined by scanning electron microscopy. They are spherical in shape and the average size of them is between 30 and 70 nm.
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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".