All chalcogenide Raman parametric Laser, Wavelength Converter and\n Amplifier in a Single Microwire
Bibliographic record
Abstract
Compact, power efficient and fiber compatible lasers, wavelength converters\nand amplifiers are vital ingredients for the future fiber optic systems and\nnetworks. Nonlinear optical effects, like Raman scattering and parametric four\nwave mixing, offer a way to realize such devices. Here we use a single\nchalcogenide microwire to realize a device that provides the functions of a\nStokes Raman parametric laser, a four wave mixing anti Stokes wavelength\nconverter, and an ultra broadband Stokes/anti Stokes Raman amplifier or\nsupercontinuum generator. The device operation relies on ultrahigh Raman and\nKerr gain (upto five orders of magnitude larger than in silica fibers),\nprecisely engineered chromatic dispersion and high photosensitivity of the\nchalcogenide microwire. The Raman parametric laser operates at a record low\nthreshold average (peak) pump power of 52 \\muW (207 mW) and a slope efficiency\nof >2%. A powerful anti Stokes signal is generated via the nonlinear four wave\nmixing process. As amplifier or the broadband source, the device covers a\nwavelength (frequency) range of >330 nm (47 THz) when pumped at a wavelength of\n1550 nm. Owing to the underlying principle of operation of the device being the\nnonlinear optical processes, the device is anticipated to operate over the\nentire transmission window of the chalcogenide glass ({\\lambda}~1 10 \\mum).\n
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".