Tailoring conductive nanofiller alignment for high actuation strain and output force in electroactive polymers
Bibliographic record
Abstract
An intrinsic conflict between high deformability and rigidity hinders the development of electroactive polymer (EAP)-based soft robots. Here, we employ an external electric field to align Al2O3-coated carbon nanotubes (Al2O3@CNTs) in a poly(vinylidene fluoride-trifluoroethylene-chlorotrifluoroethylene) (P(VDF-TrFE-CTFE)) matrix. Compared with pure P(VDF-TrFE-CTFE), the thickness strain of nanocomposites with horizontally and vertically aligned Al2O3@CNTs increases by 473% and 814%, respectively. It results in a high bending angle up to 215° for their actuator beams. Importantly, the horizontally aligned Al2O3@CNTs enhance the local stiffness via ‘face-enhanced effect’, yielding a high output force per unit volume (1.25 mN/mm3 at 30 V/μm). It is not only ~346% higher than pure P(VDF-TrFE-CTFE) but also higher than the reported ceramic actuators. Accordingly, the soft robots made by the designed nanocomposite actuators could climb slopes up to 52° and carry loads equivalent to eight times their body mass. Consequently, this modulating strategy develops a high-performance actuation for soft robots. Electroactive polymers can be used for soft robotics, though it is challenging to balance rigidity and deformability. Here the authors designed a polymer composite using an electric-field assisted tape-casting method to orient the Al2O3-coated carbon nanotubes to tailor the dielectric and mechanical properties.
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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".