Ultrafast Fiber Laser Emitting at 2.8 µm Based on a SESAM and a Broadband FBG
Bibliographic record
Abstract
Ultrafast mid-infrared fiber lasers have been intensely studied in the last decade for the generation of high harmonics, molecular spectroscopy, material processing and remote sensing. Different designs have been investigated but most of them lacked the ease of use and reliability needed for their democratization. In this paper, we demonstrate a self-starting mode-locked mid-IR erbium-doped fiber laser based on a SESAM and a broadband uniform FBG that produces pulses as short as 15 ps. Different laser cavities were tested with varying FBG peak reflectance, spectral bandwidth and active fiber length. In addition, one cavity uses a pump combiner instead of injecting free-space the pump power through the fiber tip. The results of this study confirm that the FBG spectral bandwidth can efficiently control the duration of the almost Fourier-transform-limited pulses up to a limit seemingly dictated by the presence of water vapor in the laser cavity acting as narrow spectral filters. To a lower effect, the active fiber length influences the pulse duration. Finally, the use of an all-fiber pump combiner allows for a more compact and rugged design without altering the laser performances. This study represents a step towards the development of robust mid-infrared ultrafast all-fiber lasers.
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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".