Synergistic Design of Resonant Elements and Force Multipliers to Boost the Sensitivity in Resonant Sensing Applications
Bibliographic record
Abstract
This letter introduces a versatile design methodology to enhance the sensitivity of resonant sensors through geometrical modifications to their resonant elements and force-multiplier mechanisms. These improvements are achieved, first, by modifying the boundary conditions of the common resonant beam sensing elements to increase their responsiveness to axial forces and, second, by enhancing the efficiency of force-multiplier mechanisms to go beyond basic force-multiplication of the presently used lever structures. The effectiveness of these modifications is validated through simulations and experimental testing of fabricated test structures. We also demonstrate the utility of the approach through the analysis, design, and fabrication of several resonant accelerometers as prime candidates that benefit from these advancements. Experimental results demonstrate a 35% increase in device sensitivity compared to a baseline resonant accelerometer while offering a higher quality factor for the resonant beam elements, which can help improve the resolution of the sensor. The proposed approach does not require changes to microfabrication processes and can be utilized to improve the performance of various devices through simple structural modifications.
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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) | 0.001 | 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 | 0.001 | 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".