Development and Characterization of Shape Memory Thermoplastic Polyurethane (TPU)/Polylactic Acid (PLA) Polymer Blend
Bibliographic record
Abstract
Shape memory polymers (SMPs) are a type of smart material that are gaining increasing attention nowadays. SMPs are enabled to realize dual shape transitions by applying a desired stimulus. The first shape is the permanent shape obtained during sample preparation, while the second shape represents the programmed shape imposed by the external force. And the most commonly used triggering stimuli include heat, electric, light, etc. Among the various SMPs, the thermoplastic polyurethane (TPU)/polylactic acid (PLA) polymer blend is one of the favored picks for research. Some of the drawbacks of PLA, such as brittleness and low elongation, can be effectively improved by blending with TPUs. And the resulting TPU/PLA SMP present both enhanced mechanical properties and biocompatibility. Consequently, it shows a continuously growing market potential, especially in the biomedical field. This study provides a thorough understanding of the working mechanism of the TPU/PLA polymer blend and the interacting effects upon some key microstructures. A total of four parts are covered in this study. The first part focuses on a fundamental study of the ratio of the two components. The role of the regions present in the polymer blend is analyzed and their respective functions in the realization of the shape memory effect are described. The second part of the study would be devoted to the examination of a factor that is easily overlooked by researchers, the stretching time in the SM test. This leads to a number of microstructural changes, especially in the crystallization behaviors. And all corresponding alterations in shape memory properties could be observed and characterized. When comes to the third part, another parameter, the cooling method used in the fabrication process, is chosen to be investigated. As this factor also primarily contributes to the crystallization, a distinction is made between the role of crystals in this case and in the second part. Lastly, h-BN, a relatively innovative filler in the field of shape memory, has been added to prepare the TPU/PLA/BN polymer composite. The addition of h-BN extends the interpretation of the shape memory effect in the polymer composite with fillers.
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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 | 0.000 |
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".