Electrically Driven Artificial Muscles Using Novel Polysiloxane Elastomers Modified with Nitroaniline Push–Pull Moieties
Bibliographic record
Abstract
The synthesis of novel dielectric elastomers that show a muscle-like actuation when exposed to a low electric field represents a major challenge in materials science. Silicone elastomers modified with polar side groups are among the most attractive dielectrics for such a purpose because of their high polarizability over a wide temperature and frequency range. Nitroaniline (NA) has a strong dipole moment, and therefore, its incorporation into silicone networks should allow the formation of elastomers with increased dielectric permittivity. However, incorporation of a large amount of NA into silicone needed to increase the dielectric permittivity is still challenging. In this work, we present the synthesis of polysiloxane elastomers modified with a large fraction of the nitroaniline (NA) polar group, following two different synthetic strategies. Both approaches allowed the formation of homogenous elastomers at the molecular level. These yellowish materials have a dielectric permittivity three times higher as compared to the reported NA-modified silicones. Additionally, they have excellent mechanical properties with low viscoelastic losses and a strain at break reaching 300%. Furthermore, the mechanical properties of these elastomers can be easily tuned by the content of cross-linkers used. The developed elastomers are highly stable in electromechanical tests and show an actuation strain of 8% at unprecedentedly low electric fields of 7.5 V/μm. The combination of properties such as high dielectric permittivity, large strain at break, low viscoelastic losses, fast and reversible actuation, and actuation at low electric fields is crucial for the new generation of dielectric elastomer materials that will find their way in applications ranging from artificial muscles, soft robots, sensors, and haptic displays to electronic skin.
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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) | 0.001 | 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".