Enhancing shape memory properties of multi-layered and multi-material polymer composites in 4D printing
Bibliographic record
Abstract
Abstract The shape memory behavior of smart materials is widely used for stimulation or shape-shifting purposes. Shape memory polymers (SMPs) can shape and force recoveries accompanied by attractive attributes such as biocompatibility, biodegradability, and universality. In this paper, a thermoplastic elastomer (TPE) is used as a complementary material for 4D printed polylactic acid (PLA) structures to enhance their shape and force recovery properties and lower the stimulation temperature for more practical implementations. Two approaches are followed to provide SMP composites (SMPCs): multi-layered and multi-material lattices. In multi-layered lattices, specimens are comprised of separate layers and different ratios of SMP and TPE materials. For comparison, PLA-TPE filaments with the same ratios of multi-layered lattices are produced and used to fabricate multi-material lattices. Dynamic mechanical thermal analysis tests showed a reduction in the glass transition temperature of the manufactured PLA-TPE filament. X-ray diffraction test was conducted to prove that the crystallinity of the developed PLA-TPE material increases which explains the better shape memory effect in the multi-material specimens. Phase separation occurred in low ratios of TPE in PLA, discernible in field emission scanning electron microscope (FESEM) images, resultting in low quality in one of the developed PLA-TPE filaments. FESEM images also showed proper miscibility of TPE in PLA in higher ratios. Thermomechanical tests were done on printed specimens to examine and compare the shape and force recovery of the produced SMPCs. While the shape recovery of multi-material samples was not as good as multi-layered samples, both approaches have better shape recovery results than the PLA sample. Due to a lower glass transition temperature in multi-material lattices, their shape recovery process started at lower temperatures widening their potential practical applications. Force recovery of multi-material samples revealed a significant improvement which was due to more oriented crystalline polymer structures.
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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".