Liquid Metal-Based Capacitive Strain Sensor with Self-Shielding and Low-Hysteresis
Bibliographic record
Abstract
Soft capacitive strain sensors with soft conductive electrodes are advantageous for their ability to decouple resistance from the flexible electrodes, offering excellent repeatability, low hysteresis, low energy consumption, and good temperature stability. However, existing soft capacitive strain sensors utilize intrinsically stiff conductors as soft electrodes, limiting the sensitivity, repeatability, and hysteresis. To address these issues, this work proposes a soft capacitive strain sensor based on liquid metal. The soft electrodes are composed of a liquid metal-nickel particle conductive paste, which combines fluidity and low surface tension, thereby eliminating the elastic modulus mismatch between the soft electrodes and the elastomers. This design achieves high sensitivity, excellent repeatability, and low hysteresis. The sensor employs a three-electrode structure, which enhances resistance to parasitic and stray capacitance without applying shielding layers. The measurement and the self-shielding of the three-electrode structure are investigated through simulation analyses. Additionally, the influence of the elastic modulus compatibility between the soft electrodes and the elastomers is investigated through experiments and simulations. Furthermore, the application of this sensor in the field of wearable devices is demonstrated.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".