Smart Magnetically Responsive Hydrogel Nanoparticles Prepared by a Novel Aerosol-Assisted Method for Biomedical and Drug Delivery Applications
Bibliographic record
Abstract
We have developed a novel spray gelation-based method to synthesize a new series of magnetically responsive hydrogel nanoparticles for biomedical and drug delivery applications. The method is based on the production of hydrogel nanoparticles from sprayed polymeric microdroplets obtained by an air-jet nebulization process that is immediately followed by gelation in a crosslinking fluid. Oligoguluronate (G-blocks) was prepared through the partial acid hydrolysis of sodium alginate. PEG-grafted chitosan was also synthesized and characterized (FTIR, EA, and DSC). Then, magnetically responsive hydrogel nanoparticles based on alginate and alginate/G-blocks were synthesized via aerosolization followed by either ionotropic gelation or both ionotropic and polyelectrolyte complexation using CaCl(2) or PEG-g-chitosan/CaCl(2) as crosslinking agents, respectively. Particle size and dynamic swelling were determined using dynamic light scattering (DLS) and microscopy. Surface morphology of the nanoparticles was examined using SEM. The distribution of magnetic cores within the hydrogels nanoparticles was also examined using TEM. In addition, the iron and calcium contents of the particles were estimated using EDS. Spherical magnetic hydrogel nanoparticles with average particle size of 811 ± 162 to 941 ± 2 nm were obtained. This study showed that the developed method is promising for the manufacture of hydrogel nanoparticles, and it represents a relatively simple and potential low-cost system.
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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".