Dual‐Stimuli Responsive and Sustainable PLA/APHA/TPU Blend for 4D Printing
Bibliographic record
Abstract
4D printing of shape memory polymers (SMPs) offers transformative potential for patient-specific medical devices, yet current SMPs often face a trade-off between mechanical toughness and low-temperature activation. This study presents a novel PLA/APHA/TPU blend filament for 3D printing that overcomes this limitation by combining high strength and flexibility with low-temperature shape memory activation-features not previously achieved in PLA-based SMPs. The uniform dispersion of TPU and APHA in the PLA matrix creates a composite with enhanced tensile strength, modulus, and elongation, addressing the brittleness typical of neat PLA. The optimized 60/20/20 wt.% formulation enables rapid shape recovery at ∼39.5°C, significantly below PLA's glass transition, with near-complete shape fixity (∼100%) and high recovery ratios (>92%) under both thermal and mechanical stimuli. This dual-responsive behavior is driven by the synergistic roles of TPU (providing ductility) and APHA (enhancing flexibility and thermal sensitivity). The composite also retains excellent printability and biocompatibility, making it ideal for next-generation biomedical SMP applications such as 4D-printed orthopedic braces, soft robotic actuators, and adaptive implants. Using bio-based, biodegradable polymers, this work advances eco-friendly, high-performance SMPs for additive manufacturing, setting a new benchmark for PLA-based 4D-printable materials.
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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 | 0.001 |
| 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".