Design Guidelines of Stretchable Pressure Sensors‐Based Triboelectrification
Bibliographic record
Abstract
In this paper, a comprehensive study for a stretchable self‐powered pressure sensor is presented to investigate the structural parameters effect on the sensor sensitivity, and the pressure sensor is designed based on a vertical contact‐separation mode triboelectric nanogenerator (TENG). By addressing the solid mechanics, electrical, and surface science effects, we discussed the fundamental physics of the TENG pressure sensor. Modeling and simulation by finite element method (FEA) are conducted to clarify the effects of the structural dimensions on the performance of the pressure sensor. In addition, the relationship between the deformation of an interfacial micro‐nano structure and the applied pressure on a triboelectric energy harvester (TEH) is analyzed for different microstructures patterns and dimensions. Finally, a comparison is conducted between TENG pressure sensor based on the contact‐separation mode (CS) and single electrode mode (SE). The results show that both structure sizes of the pressure sensor and TENG operating modes have a significant influence on the pressure sensitivity of the sensor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".