Porous Polydimethylsiloxane–Silver Nanowire Devices for Wearable Pressure Sensors
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide 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 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".