Advanced nanocomposites for 4D printing: High-performance electroactive shape memory polymers for smart applications
Bibliographic record
Abstract
The potential of 4D printing to enable dynamic, programmable structures has been constrained by the reliance on heat and moisture as activation stimuli, limiting the complexity of shape transformations and hindering localized actuation. This study addresses these limitations by developing electroactive shape memory polymers (SMPs) by incorporating multi-walled carbon nanotubes (MWCNTs) into poly(lactic acid)/polyvinylidene fluoride (PLA/PVDF) blends. Using fused deposition modeling (FDM)-based 4D printing, the MWCNTs were strategically dispersed within the polymer matrix to enhance both electrothermal responsiveness and mechanical properties. The optimized composite, containing 7.5 % MWCNTs, achieved a rapid temperature rise to 80 °C in just 10 s under a low voltage, alongside outstanding shape recovery and fixity ratios of 98.56 % and 99.6 %, respectively. Numerical simulations developed in Abaqus accurately replicated the electrothermal behavior and shape recovery dynamics, with results aligning closely with experimental observations. The advanced SMPs were successfully implemented in bio-inspired origami structures and soft robotic hands, showcasing precise actuation, high flexibility, and robust structural integrity. These findings reveal the transformative potential of electroresponsive nanocomposites for next-generation applications in soft robotics, bio-inspired mechanisms, and adaptive intelligent systems.
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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 | 0.000 |
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".