Magnetic Nanoparticles in Biopolymer Fibers: Fabrication Techniques and Characterization Methods
Bibliographic record
Abstract
Hybrid nanocomposites combining biopolymer fibers incorporated with nanoparticles (NPs) have received increasing attention due to their remarkable characteristics. Inorganic NPs are typically chosen for their properties, such as magnetism and thermal or electrical conductivity, for example. Meanwhile, the biopolymer fiber component is a backbone, and could act as a support structure for the NPs. This shift towards biopolymers over traditional synthetic polymers is motivated by their sustainability, compatibility with biological systems, non-toxic nature, and natural decomposition. This study employed the solution blow spinning (SBS) method to obtain a nanocomposite comprising poly(vinyl pyrrolidone), PVA, and gelatin biodegradable polymer fibers incorporated with magnetic iron oxide nanoparticles coated with poly(acrylic acid), PAA2k, coded as γ-Fe2O3-NPs-PAA2k. The fiber production process entailed a preliminary investigation to determine suitable solvents, polymer concentrations, and spinning parameters. γ-Fe2O3-NPs were synthesized via chemical co-precipitation as maghemite and coated with PAA2k through the precipitation–redispersion protocol in order to prepare γ-Fe2O3-NPs-PAA2k. Biopolymeric fibers containing coated NPs with sub-micrometer diameters were obtained, with NP concentrations ranging from 1.0 to 1.7% wt. The synthesized NPs underwent characterization via dynamic light scattering, zeta potential analysis, and infrared spectroscopy, while the biopolymer fibers were characterized through scanning electron microscopy, infrared spectroscopy, and thermogravimetric analysis. Overall, this study demonstrates the successful implementation of SBS for producing biopolymeric fibers incorporating iron oxide NPs, where the amalgamation of materials demonstrated superior thermal behavior to the plain polymers. The thorough characterization of the NPs and fibers provided valuable insights into their properties, paving the way for their potential applications in various fields such as biomedical engineering, environmental remediation, and functional materials.
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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 | 0.001 |
| 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 teacher head, 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".