Modulation of Crystallinity through Radiofrequency Electromagnetic Fields in PLLA/Magnetic Nanoparticles Composites: A Proof of Concept
Bibliographic record
Abstract
To modulate the properties of degradable implants from outside of the human body represents a major challenge in the field of biomaterials. Polylactic acid is one of the most used polymers in biomedical applications, but it tends to lose its mechanical properties too quickly during degradation. In the present study, a way to reinforce poly-L lactic acid (PLLA) with magnetic nanoparticles (MNPs) that have the capacity to heat under radiofrequency electromagnetic fields (EMF) is proposed. As mechanical and degradation properties are related to the crystallinity of PLLA, the aim of the work was to explore the possibility of modifying the structure of the polymer through the heating of the reinforcing MNPs by EMF within the biological limit range f·H < 5·× 109 Am−1·s−1. Composites were prepared by dispersing MNPs under sonication in a solution of PLLA. The heat released by the MNPs was monitored by an infrared camera and changes in the polymer were analyzed with differential scanning calorimetry and nanoindentation techniques. The crystallinity, hardness, and elastic modulus of nanocomposites increase with EMF treatment.
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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".