Self‐Healing Polyester Urethane Supramolecular Elastomers Reinforced with Cellulose Nanocrystals for Biomedical Applications
Bibliographic record
Abstract
Stretchable self-healing urethane-based biomaterials have always been crucial for biomedical applications; however, the strength is the main constraint of utilization of these healable materials. Here, a series of novel, healable, elastomeric, supramolecular polyester urethane nanocomposites of poly(1,8-octanediol citrate) and hexamethylene diisocyanate reinforced with cellulose nanocrystals (CNCs) are introduced. Nanocomposites with various amounts of CNCs from 10 to 50 wt% are prepared using solvent casting technique followed by the evaluation of their microstructural features, mechanical properties, healability, and biocompatibility. The synthesized nanocomposites indicate significantly higher tensile modulus (approximately 36-500-fold) in comparison to the supramolecular polymer alone. Upon exposure to heat, the materials can reheal, but nevertheless when the amount of CNC is greater than 10 wt%, the self-healing ability of nanocomposites is deteriorated. These materials are capable of rebonding ruptured parts and fully restoring their mechanical properties. In vitro cytotoxicity test of the nanocomposites using human dermal fibroblasts confirms their good cytocompatibility. The optimized structure, self-healing attributes, and noncytotoxicity make these nanocomposites highly promising for tissue engineering and other biomedical 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".