Zero-Power MEMS Resonant Mass Sensor Inspired by Piezoelectric Vibration Energy Harvesting
Bibliographic record
Abstract
Resonant mass sensors have been studied and developed for many years. However, their application as a portable testing platform is limited due to a need for external bulky optical systems, impedance/network analyzers, or complex signal processing circuitry to detect the resonant frequency shift. In this study, we propose an innovative yet simple method for determining the resonant frequency shift and the associated mass amount using the amplitude of the generated AC signal from piezoelectric microcantilevers. Our approach involves designing a T-shaped cantilever with two identical proof masses at the tip to improve energy conversion efficiency and quality factor. When particles are exposed to the surface of the T-shaped cantilever, the operational resonant frequency shifts, causing a specific reduction in the amplitude of the generated voltage. By measuring this shift in the resonant frequency using the amplitude of the generated voltage, important information about the applied mass can be obtained. In addition, we employ machine learning techniques to accurately assess the mass amount of particles based on their frequency response. Our experiments confirm the capability of our proposed technique for detecting resonant frequency shifts in mass sensors, offering a promising approach for developing portable and efficient sensing platforms.
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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 | 0.001 |
| 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".