Analytical Modeling and Experimental Validation of Triboelectric Behavior in Kirigami Flexible Capacitive Sensors
Bibliographic record
Abstract
Flexible capacitive sensors have attracted extensive attention in recent years, especially in their application for biomedical electrophysiological sensing, as they improve comfort and flexibility while being more robust to some types of motion artifacts (MAs). Due to their contactless nature, they are still susceptible to triboelectrification which, because of their flexibility, appears to be stronger and more unpredictable compared to their rigid counterparts. In this work, we propose a novel analytical model to predict and physically justify the triboelectric behavior of flexible capacitive sensors applied to nonflat surfaces. In particular, we consider the general case of an electrode conforming to a spherical surface, which loses contact because of a transversal motion. The model takes into account both the effect of the triboelectric voltage and the varying coupled capacitance, describing the different phases of the movement. Finally, electrical measurements were performed on the sensor, reproducing the same setup and dynamics in the laboratory. The results were compared to the analytical model and discussed: both the analytical and experimental results exhibit similar trends and voltage characteristics, with spike duration for each speed of 4.1, 2.1, and 0.9 s for the modeled effect and 4.6, 2.5, and 1.1 s for the corresponding experimental results. The presented analytical model was revealed to be accurate in describing the MAs caused by the considered motion and represents an important tool for describing and predicting similar artifacts for flexible capacitive sensors.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".