Controlled shape memory effects of magnetic polymer nanocomposites by induction heating
Bibliographic record
Abstract
Interest in stimuli-responsive materials has increased rapidly, leading to a multitude of innovative applications in biomedical design. This study seeks to induce controlled shape memory effects through induction heating of magnetic polymer nanocomposites while retaining thermal consistency within attached hydrogel composites for various biomedical applications. Three commonly used polymer matrices were embedded with varying concentrations of magnetite nanoparticles to determine minimum and maximum loading effects on induction heating response and optimal shape memory effects. Thermal and morphological characterizations were performed to determine transition temperatures, followed by induction heating tests by way of an induction coil at different magnetic field strengths to determine heating rates, activation times and activation rates of shape memory effects for each polymer nanocomposite composition. Simultaneously, mechanically tunable sodium alginate and cellulose nanocrystal hydrogel composites were fabricated and characterized to determine hydrational, mechanical and thermal buffering properties. Induction heating tests revealed that all substrates exhibited a heating response; however, shape memory effects were observed only in poly(vinyl acetate) and Nylon 11. Moreover, all hydrogels displayed promising thermal dissipation, <1°C per 20 s of heating, preventing any potential thermal shock to biological components. These unique properties will allow for successful employment of these multi-composite scaffolds in a multitude of biological applications.
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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".