Biodegradable toughened nanohybrid shape memory polymer for smart biomedical applications
Bibliographic record
Abstract
A polyurethane nanohybrid has been prepared through the in situ polymerization of an aliphatic diisocyanate, ester polyol and a chain extender in the presence of two-dimensional platelets. Polymerization within the platelet galleries helps to intercalate, generate diverse nanostructure and improve the nano to macro scale self-assembly, which leads to a significant enhancement in the toughness and thermal stability of the nanohybrid in comparison to pure polyurethane. The extensive interactions, the reason for property enhancement, between nanoplatelets and polymer chains are revealed through spectroscopic measurements and thermal studies. The nanohybrid exhibits significant improvement in the shape memory phenomena (91% recovery) at the physiological temperature, which makes it suitable for many biomedical applications. The structural alteration, studied through temperature dependent small angle neutron scattering and X-ray diffraction, along with unique crystallization behavior have extensively revealed the special shape memory behavior of this nanohybrid and facilitated the understanding of the molecular flipping in the presence of nanoplatelets. Cell line studies and subsequent imaging testify that this nanohybrid is a superior biomaterial that is suitable for use in the biomedical arena. In vivo studies on albino rats exhibit the potential of the shape memory effect of the nanohybrid as a self-tightening suture in keyhole surgery by appropriately closing the lips of the wound through the recovery of the programmed shape at physiological temperature with faster healing of the wound and without the formation of any scar. Further, the improved biodegradable nature along with the rapid self-expanding ability of the nanohybrid at 37 °C make it appropriate for many biomedical applications including a self-expanding stent for occlusion recovery due to its tough and flexible nature.
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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".