Superparamagnetic Amine-Functionalized Maghemite Nanoparticles as a Thixotropy Promoter for Hydrogels and Magnetic Field-Driven Diffusion-Controlled Drug Release
Bibliographic record
Abstract
Superparamagnetic nanoparticle-arrested hydrogel matrices have immense significance in smart soft biomaterials. Herein, we report the synthesis of superparamagnetic nanoparticle-loaded magneto-responsive tough elastomeric hydrogels for dual-responsive drug delivery. In the first phase of work, we carried out room-temperature synthesis of amine-functionalized superparamagnetic iron oxide nanoparticles (IONPs), and in the second phase of work, we demonstrated that IONPs could act as a toughening agent as well as a viscosity modifier for poly(acrylic acid- co -hydroxyethyl methacrylate) copolymer hydrogels. The hydrogel was tested by Fourier transformed infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and continuous-wave-electron paramagnetic resonance (CW-EPR). Moreover, the IONPs affect its gelation time and elasticity significantly, which was also evaluated from its rheological behavior. The compressive mechanical strength (∼120 kPa), elasticity, recovery to original shape (zero permanent set), water uptake, and thixotropic behavior under dynamic stress of the hybrid hydrogels have supported its robustness in the swelled state. The drug release behavior of the hydrogel showed dual parameter dependency (IONPs and cross-linker) and dual responsiveness against both pH and static magnetic field. The delayed network rupturing, dual-responsive drug delivery nature, and noncytotoxic behavior against human live cells could promote this hybrid hydrogel as an ideal alternative for the remotely controlled drug delivery vehicle.
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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".