Cellulose nanocrystals (CNC) reinforced shape memory polyurethane ribbons for future biomedical applications and design
Bibliographic record
Abstract
Shape memory materials are an innovative type of materials that reversibly store a temporary shape and recover back to the original dimensions with the application of an external mechanism such as heat. Shape memory polymers (SMP), specifically thermoplastic SMP (e.g. shape memory polyurethane (SMPU)) have received much attention during the past decade because of the promising future applications and advantages such as ease of processability for thermoplastic SMP (e.g. by 3-D printing), cost, and biocompatibility. In the biomedical field, applications such as stents, surgical sutures, and orthodontic devices, amongst others have been proposed. The addition of fillers to the material can modify the material to improve their load bearing capabilities. Bio-based fillers such as cellulose nanocrystals (CNC) have been proposed in a variety of reinforcing applications. The present work focuses on the experimental description of the addition of nonmodified CNC to SMPU. The work studied the effect on melt-extruded ribbons, for 0, 0.5, 1, 2, and 4 wt%. An increase of yield point, toughness, flexural modulus, recovery rate, and decrease of total time showed that SMPU/CNC nanocomposites are a potential candidate to use in future 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".