Mid-infrared soliton self-frequency shift in chalcogenide glass
Bibliographic record
Abstract
Emerging applications in the mid-infrared (MIR) stimulate the growth and development of novel optical light sources. Soliton self-frequency shift (SSFS) in soft glass fiber currently shows great potential as an efficient approach toward the generation of broadly tunable femtosecond pulses in the MIR. In this work, we demonstrate a highly efficient tunable soliton source based on SSFS in chalcogenide glass. We show a simple and fully fiberized system to generate these continuously tunable Raman solitons over a broad spectral range of 2.047–2.667 µm, which consumes no more than 87 pJ per pulse. The spectral measurements suggest that the generated pulses are as short as 62 fs with a maximum power conversion efficiency of 43%. This result is realized thanks to an 8 cm long <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">A</mml:mi> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> </mml:mrow> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> </mml:math> microstructure optical fiber tapered into a microwire. Thanks to their broad transparency, their high nonlinearity, and their adjustable chromatic dispersion, chalcogenide microwires are promising components for the development of compact and highly efficient MIR optical sources with low power consumption.
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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) | 0.001 | 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".