Digital microelectromechanical sensor with an engineered polydimethylsiloxane (PDMS) bridge structure
Bibliographic record
Abstract
Functional electronic devices integrated on flexible substrates are of great interest in both academia and industry for their potential applications in wearable technologies. Recently, there have been an increasing number of investigations on developing new materials for flexible strain sensors and pressure sensors, with the aim of achieving better sensitivity and detection ranges. However, the analog signal outputs of these sensors are accompanied with challenges regarding device reproducibility and reliability. Here we designed and fabricated a new class of sensors-digital microelectromechanical (MEM) sensors for wearable technologies. Our digital MEM sensors were implemented with the polydimethysiloxane (PDMS) bridge on flexible substrates, and provided digital signal outputs based on electrical insulating-to-conducting transitions. By engineering the PDMS bridge structure, we could tune the sensitivity of the digital MEM sensor for various applications. These digital MEM sensors were used in bending tests: they were integrated on glove fingers and used to detect gestures. These sensors were also used as force sensors: they were used on human wrists to monitor heart rates. The device was experimentally found to maintain its performance level even after 10 000 cycles of bending or pressing. The digital output of our devices allows a higher tolerance for device fabrication to be set. Furthermore, our devices can be engineered for desired specifications in various potential applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".