Real time high voltage capacitance for rapid evaluation of dielectric elastomer actuators
Bibliographic record
Abstract
Dielectric elastomer actuators (DEAs) are soft electromechanical transducers that have enabled robotic, haptic, and optical applications. Despite their advantages in high specific energy, large bandwidth, and simple fabrication, their widespread adoption is limited by poor long-term performance. While the mechanical work output has been studied extensively, the electrical energy input has rarely been characterized. Here we report a method to continuously monitor high voltage capacitance during DEA actuation to directly measure the electrical energy consumption. Our approach can track energy conversion efficiency, but also show changes in the device's properties in real-time. This unprecedented insight enables a novel way to study DEAs, evaluate degradation mechanisms, and correlate material structure to device performance. Moreover, it provides a data acquisition platform for data-driven optimization and prediction of long-term actuator performance. This work is a necessary step towards developing ultra-resilient DEAs and enabling a wide range of applications, from wearable devices to soft machines across different scales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".