High-frequency torsional motion transduction using optomechanical coupled oscillators
Bibliographic record
Abstract
Using light to measure an object’s motion is central to operating mechanical sensors that probe forces and fields. Cavity optomechanical systems embed mechanical resonators inside optical resonators. This enhances the sensitivity of optomechanical measurements, but only if the mechanical resonator does not spoil the properties of the optical cavity. For example, cavity optomechanical detection of resonators made from optically absorbing materials, or whose geometry does not possess suitable spatial symmetry, is challenging. Here we demonstrate a system that overcomes challenges in measuring high-frequency twisting motion of a nanodisk by converting them to vibrations of a photonic crystal cavity. Optomechanical readout of the cavity then enables measurement of the nanodisk’s torsional resonances with sensitivity 5.1 × 10 − 21 − 1.2 × 10 − 19 N m / H z for a mechanical frequency range of 5–800 MHz. The nanodisk can be outfitted with magnetic nanostructures or metasurfaces without affecting the optical properties of the cavity, making the system suitable for torque magnetometry and structured light sensing.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".