NEMS generated electromechanical frequency combs
Bibliographic record
Abstract
This paper presents a novel technique for low-power generation of frequency combs (FC) over a wide frequency range. It leverages modal interactions between electrical and mechanical resonators in electrostatic NEMS operating in air to provide a simple architecture for FC generators. A biased voltage signal drives the electrical resonator at resonance which is set to match an integer submultiple of twice the mechanical resonator's resonance. Experimental results demonstrate that the NEMS displacement exhibit more than 150 equidistant peaks in the case of a 2:1 modal interaction and more than 60 equidistant peaks in the case of a 1:1 modal interaction. In both cases, the Free Spectral Range (FSR) was equal to the mechanical resonance frequency. Comparison between the FCs generated by the 2:1 and 1:1 modal interactions demonstrate the superiority of the former in terms of bandwidth and stability. The superior phase coherence of the FC generated via the 2:1 modal interaction was demonstrated via time-domain analysis. Our technique has the flexibility to generate multiple frequency combs and to fine-tune their FSR depending on the number of mechanical modes accessible to and the order of the activated modal interaction. It can be integrated into portable devices and is well aligned with modern miniaturization technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".