Highly-doped SiC resonator with ultra-large tuning frequency range by Joule heating effect
Bibliographic record
Abstract
Tuning the natural frequency of a resonator is an innovative approach for the implementation of mechanical resonators in a broad range of fields such as timing applications, filters or sensors. The conventional electrothermal technique is not favorable towards large tuning range because of its reliance on metallic heating elements. The use of metallic heaters could limit the tuning capability due to the mismatch in thermal expansion coefficients of materials forming the resonator. To solve this drawback, herein, the design, fabrication, and testing of a highly-doped SiC bridge resonator that excludes the use of metallic material as a heating element has been proposed. Instead, free-standing SiC structure functions as the mechanical resonant component as well as the heating element. Through the use of the Joule heating effect, a frequency tuning capability of almost ∆f/fo ≈ 80% has been demonstrated. The proposed device also exhibited a wide operating frequency range from 72.3 kHz to 14.5 kHz. Our SiC device enables the development of highly sensitive resonant-based sensors, especially in harsh environments.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".