Flexible Capacitive Kirigami Electrode: Experimental Investigation and Analytical Model
Bibliographic record
Abstract
Flexible electronics have recently garnered increasing attention due to their promise for a wide range of applications, from energy storage to flexible screens, over sensors to wearables. The trend in flexible electronics is toward the fabrication of devices composed of fully flexible materials and components. However, flexible devices still present many limitations, primarily due to the negative Poisson ratio of the materials used, which affects the dimensions and characteristics of the materials when bent or stretched. In this work, we propose a novel design for capacitive electrodes aimed to improve the flexibility of devices without affecting the properties of the materials involved: the design is based on Kirigami, the traditional Japanese art of paper cutting which, when applied to engineering, enables the shaping of stretchable and deformable structures by performing simple cuts on traditional substrates and films, thereby ensuring compatibility with existing manufacturing methods. We formulate a novel analytical model to explain in detail the behavior of the Kirigami electrode applied to an arbitrary nonflat surface. The efficacy of the novel design has been verified by means of capacitive measurements in different nonflat configurations: the results obtained confirm both the validity of the analytical model and the robustness of the novel Kirigami approach, with capacitive values increasing up to 115% for the Kirigami electrode compared to the non-Kirigami one.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".