High-Performance Flexible Sensor Based on Multi-Level Microstructure
Bibliographic record
Abstract
Flexible pressure sensors have garnered extensive attention in wearable medical applications due to their stretchability and conformability to curved surfaces. However, limitations in sensitivity and measurement range can compromise their responsiveness to both subtle pressure variations and broader pressure ranges. Optimizing the sensing layer structure of the sensor can improve stress distribution and enhance overall performance. An innovative hierarchically responsive pyramid microstructure array sensor is designed in this study. Using high-precision 3D-printed molds, the sensor was manufactured with polydimethylsiloxane (PDMS) as the substrate material. Experimental results demonstrate that the sensor achieves high sensitivity (0.0741 kPa−1) in the low-pressure regime (0–15 kPa) while maintaining excellent linearity (R2=0.98) in the high-pressure range (15–90 kPa). Remarkably, the device exhibits fast response characteristics with 40 ms response time and 34 ms recovery time. This advancement provides a potential solution for human motion analysis and pathological detection applications.
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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 | 0.001 |
| 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".