Bend, stretch, and touch: Locating a finger on an actively deformed transparent sensor array
Bibliographic record
Abstract
The development of bendable, stretchable, and transparent touch sensors is an emerging technological goal in a variety of fields, including electronic skin, wearables, and flexible handheld devices. Although transparent tactile sensors based on metal mesh, carbon nanotubes, and silver nanowires demonstrate operation in bent configurations, we present a technology that extends the operation modes to the sensing of finger proximity including light touch during active bending and even stretching. This is accomplished using stretchable and ionically conductive hydrogel electrodes, which project electric field above the sensor to couple with and sense a finger. The polyacrylamide electrodes are embedded in silicone. These two widely available, low-cost, transparent materials are combined in a three-step manufacturing technique that is amenable to large-area fabrication. The approach is demonstrated using a proof-of-concept 4 × 4 cross-grid sensor array with a 5-mm pitch. The approach of a finger hovering a few centimeters above the array is readily detectable. Light touch produces a localized decrease in capacitance of 15%. The movement of a finger can be followed across the array, and the location of multiple fingers can be detected. Touch is detectable during bending and stretch, an important feature of any wearable device. The capacitive sensor design can be made more or less sensitive to bending by shifting it relative to the neutral axis. Ultimately, the approach is adaptable to the detection of proximity, touch, pressure, and even the conformation of the sensor surface.
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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 | 0.001 | 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".