Improved response time of flexible microelectromechanical sensors employing eco-friendly nanomaterials
Bibliographic record
Abstract
Flexible force/pressure sensors are of interest for academia and industry and have applications in wearable technologies. Most of such sensors on the market or reported in journal publications are based on the operation mechanism of probing capacitance or resistance changes of the materials under pressure. Recently, we reported the microelectromechanical (MEM) sensors based on a different mechanism: mechanical switches. Multiples of such MEM sensors can be integrated to achieve the same function of regular force/pressure sensors while having the advantages of ease of fabrication and long-term stability in operation. Herein, we report the dramatically improved response time (more than one order of magnitude) of these MEM sensors by employing eco-friendly nanomaterials-cellulose nanocrystals. For instance, the incorporation of polydimethysiloxane filled with cellulose nanocrystals shortened the response time of MEM sensors from sub-seconds to several milliseconds, leading to the detection of both diastolic and systolic pressures in the radial arterial blood pressure measurement. Comprehensive mechanical and electrical characterization of the materials and the devices reveal that greatly enhanced storage modulus and loss modulus play key roles in this improved response time. The demonstrated fast-response flexible sensors enabled continuous monitoring of heart rate and complex cardiovascular signals using pressure sensors for future wearable sensing platforms.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".