Development of PVDF nanocomposite with single-walled carbon nanotubes (SWCNT) and boron nitride nanotubes (BNNT) for soft morphing actuator
Bibliographic record
Abstract
Abstract Soft morphing actuators can deliver a range of displacements whilst being flexible and lightweight, making them advantageous over traditional mechanical actuators. Piezoelectric polymer polyvinylidene fluoride (PVDF) is combined with nanofillers to achieve superior soft actuator with the nanocomposite than with solely the polymer. This paper investigates and compares the distinctive effects of 1D nanofillers: single-walled carbon nanotubes (SWCNTs) and boron nitride nanotubes (BNNTs), through the promotion of crystal structures and polar β crystals of PVDF, and consequently its actuation ability. Results showed that 80 µ m thick 2 wt.% SWCNT/PVDF clamped at both ends with a 10 mm span achieved a high deflection per applied electric field of 414 µ m (V mm −1 ) −1 and deflection of 570 µ m. This was due to a combination of fabrication method, physical geometry, and large surface area of SWCNTs leading to enhanced degree of crystallinity, β crystals, dielectric constant, and conductivity. The increase in both overall crystal formation and targeted β crystals lead to a high total β crystal content of 35%, and the conductivity lead to a low applied electric field of 1.3 V mm −1 . BNNT/PVDF was able to undergo electric poling due to its insulating nature. BNNT/PVDF achieved a deflection magnitude per applied electric field of 2.9 µ m (V mm −1 ) −1 , due to a much higher electric field (90–150 V mm −1 ). This corresponded to a deflection magnitude of 260 µ m, which was a 520% increase from only stretched BNNT/PVDF samples. Both nanocomposites displayed large scale actuation that is greater than the 70 µ m deflection (0.9 µ m (V mm −1 ) −1 ) observed for pure PVDF of same geometry and setup.
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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) | 0.000 | 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".