Porous Polydimethylsiloxane–Silver Nanowire Devices for Wearable Pressure Sensors
Bibliographic record
Abstract
We demonstrate a simple, nonlithographic method for fabricating piezoresistive pressure sensors with a broad range of working pressures and low detection limit. Our wearable pressure sensor is fabricated by using two metallized, porous polymer layers which undergo a change in resistance as a function of pressure. This sensor has a sandwich structure composed of top and bottom sheets of porous polydimethylsiloxane (PDMS) fabricated by using a simple templating method. The inner face of each of these layers is coated with a layer of conductive silver nanowires (AgNWs). The AgNW layers are initially in light contact, and the device undergoes a change in resistance when pressure is applied perpendicular to the plane of the sheets. In these devices, the size of the pores is an important determinant of the device sensitivity and range. Powder templates of KCl, NaCl, and sugar are used to create different sized pores (60–90, 200–275, and 400–550 μm, respectively) in the elastomeric PDMS layers, which in turn affects the mechanical deformability of the devices. The sensors fabricated with KCl porous layers (which had the smallest pores) demonstrate the best performance, with a sensitivity of 14.1 kPa–1 (up to 3.5 kPa), 4.8 kPa–1 (up to 10 kPa), and 1.84 kPa–1 (up to 40 kPa) and good stability over 1000 loading and unloading cycles. Pressure sensors are also fabricated by decreasing the ratio of PDMS base to curing agent from the standard 10:1 formulation, which increases the stiffness of the viscoelastic PDMS layer and thus shortens the response time. The stiffest formulation (5:1 (base:curing agent)) sample gives the shortest response time of ∼47 ms. Wearable electronic applications of these devices, including pulse measurement, facial movements, and sound tracking, are demonstrated in this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".