Synthesis and Characterization of a Bio-Compatible Shape Memory Polymer Blend for Biomedical and Clinical Applications
Bibliographic record
Abstract
Shape memory polymers (SMP) are a class of stimuli-responsive materials that are able to respond to external stimulus such as heat by altering their shape. Bio-compatible SMPs have a number of advantages over existing SMP materials and are being studied extensively for biomedical and clinical applications. Polymer blending has proved to be an effective method to improve the mechanical properties of polymers (such as tensile strength and toughness) as well as shape memory properties. In this study, we investigate the effect of blending two bio compatible polymers, thermoplastic polyurethane (TPU), a polymer with a high toughness and percent elongation, and poly-lactic acid (PLA), a stiff and strong polymer. The thermal, mechanical and thermo-mechanical (shape memory) properties of TPU/PLA blends were characterized in the following weight percent compositions: 80/20, 65/35, and 50/50 TPU/PLA. The TPU/PLA SMP blending was achieved with melt-blending and the tensile samples were fabricated with compression molding. The mechanical properties of each blend were studied at three different temperatures. The following thermo-mechanical (or shape memory) properties were also studied at each temperature: the shape fixity rate (Rf), shape recovery rate (Rr) and the effect of recovery temperature on the shape memory behavior. The microstructure of the polymer blends were investigated with an environmental scanning electron microscope (SEM). The results showed that the glass transition temperatures of the blends were similar to pure PLA. The toughness of the SMP blend increased with increasing TPU concentration and the tensile strength of the blend increased with PLA composition. The shape fixity rate of the TPU/PLA blend increased with increasing TPU content and the shape recovery rate increased with increasing deformation and recovery temperature. The various TPU/PLA SMP blends characterized in this study have the potential to be developed further for specific biomedical and clinical 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 | 0.001 | 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".