Biocompound-Based Multiple Shape Memory Polymers Reinforced by Photo-Cross-Linking
Bibliographic record
Abstract
For the design of shape memory polymers with potential biomedical applications, we synthesized methacrylate-based monomers bearing biological compounds such as cholic acid and cinnamic acid in addition to oligo(ethylene glycol) as pendant groups and opted to use a simple radical polymerization method for the preparation of the copolymers. The glass transition temperatures of these polymers are broad and tunable and can thus accommodate dual and triple shape memory behaviors. The fixity ratios of dual and triple shape memory are all above 91%. The recovery ratio of dual shape memory is 89.5%, whereas the two recovery ratios of triple shape memory are 53.2 and 81.5%, respectively. To further improve the shape memory properties, a cinnamic acid-based methacrylate monomer was incorporated into the copolymers to enable a photo-cross-linking of the terpolymer. After irradiation, the fixity ratios remain high, whereas the recovery ratios of dual and triple shape memories are much improved. The terpolymers after light irradiation even show quadruple shape memory property. Different irradiation times were tested to optimize the shape memory effects. The recovery ratio of dual shape memory of such terpolymers can reach as high as 98.6%, whereas the recovery ratios of triple and quadruple shape memories are improved to the range of 75.3-99.1%.
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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 | 0.001 |
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".