Elastomeric Cavity Opto‐Mechanics: Low‐Power Soliton Frequency Combs
Bibliographic record
Abstract
Abstract An innovative, previously unexplored approach that leverages elastomeric membranes (EM) to develop a highly deformable cavity optomechanical resonator is proposed. This resonator generates multi soliton frequency combs (FCs) with low power consumption, a phenomenon of great interest in the realm of nonlinear light‐matter interactions. This approach marks a breakthrough due to its streamlined simplicity, utilizing a single continuous‐wave (CW) laser pump and an external acoustic wave. Matching the acoustic wave frequency with natural frequencies of the EM resonator accompanied by mechanical Kerr nonlinearities and dispersion give rise to the formation of mechanical FCs. The hyperelastic mechanical resonator and electromagnetic cavity resonance are parametrically coupled within the microwave frequency range, catalyzing the generation of mechanical FCs and their seamless transformation into optomechanical solitons within the optical domain with remarkable efficiency. Achieving stable pulse trains with a free spectral range ranging from 2 to 9 kHz using a few millimeter‐sized cavity by supplying 2 to 4 mW pump power marks a pivotal advancement in chip‐scale optomechanical resonators. This breakthrough holds transformative potential in domains such as quantum computing and spectroscopy. This method is fundamental to the creation of a mechanical Kerr medium, effectively bypassing the reliance on high mechanical quality‐factors.
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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) | 0.002 | 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".