Improving Sensitivity of Resonant Sensor Systems Through Strong Mechanical Coupling
Bibliographic record
Abstract
This paper reports on the first use of strongly coupled microresonators to improve the sensitivity of resonant sensing systems. To date, the research on coupled resonant sensors has concentrated on weakly coupled systems, which rely on the measurement of signal amplitudes. Strongly coupled resonant sensor systems, on the other hand, provide a frequency-shift output that results in improved accuracy, precision, and dynamic range. A system model is developed to investigate the effect of perturbations on the eigenvalues of resonator arrays. The model is used to study the system sensitivity to perturbations as the coupling strength between the resonators is increased and demonstrates a multi-fold increase in sensitivity for strong coupling. The developed theory is employed to design strongly coupled resonant sensor systems. Proof-of-concept devices were fabricated in a custom microfabrication process that allowed for inclusion of piezoresistors on structural layers. Experimental results were used to validate the theoretical model and demonstrated an improvement of more than 20% in sensitivity with moderate coupling ratios. This paper lays the foundation for the design of strongly coupled resonant sensor systems for single or multiple measurands.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".