High sensitivity, thermal noise-driven aluminum-based resonant MEMS humidity sensor
Bibliographic record
Abstract
The integration of connected devices and the Internet of Things in the era of Industry 4.0 has led to the transformation of various sectors. Sensors, particularly for humidity measurement, play a pivotal role in applications such as respiration and wound monitoring, human–machine interfaces, fuel cells, and circuit failure detection. While conventional humidity sensors dominate the market, gravimetric-based sensing offers promise but faces challenges due to complex fabrication, high cost, and nonlinearity. This study explores gravimetric platforms for humidity sensing and proposes linear and nonlinear schemes to detect humidity. The linear sensors exhibit consistent frequency shifts with humidity changes, while the nonlinear sensors introduce a turn-around point where nonlinear effects counteract mass-loading. We address this limitation by splitting the dynamic range into low and high ranges on either side of this point, thereby creating one-to-one relationships. Both sensor types demonstrate high sensitivity (from 39068 to −23568 ppm/%RH for nonlinear and 5809 ppm/%RH for linear) and repeatability, emphasizing their suitability for real-time humidity monitoring. Moreover, the thermal noise-driven resonators underlying the sensors do not require external input, thereby enhancing their suitability for low-power applications. These findings provide a foundation for innovative real-time sensing applications and offer insights into optimizing sensitivity and stability in humidity sensing and other applications.
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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".