Fabrication of chirped fiber Bragg gratings in a non-uniform single-core As$_{2}$Se$_{3}$-PMMA tapered fiber
Bibliographic record
Abstract
A chirped fiber Bragg grating (FBG) is fabricated using a non-uniform single-core As2Se3-PMMA tapered fiber that is pre-stretched by 0.52 mm during the inscription process. The inscription is based on a standing wave formed by two counter-propagating continuous-wave lights in the stretched non-uniform single-core As2Se3-PMMA taper. A periodical refractive index change by the standing wave in the high photosensitivity As2Se3core is induced, which creates a FBG along the taper. A strain gradient is formed along the grating due to the non-uniform structure when the elongation is changed, which produces a varying shift in the Bragg spacing at different position along the taper leading to a tunable chirped grating. The central wavelength and bandwidth are linearly dependent on the strain change applied to the chirped grating. The wide tuning range is attributed to the low stiffness of the chalcogenide core and PMMA cladding. An exponential dependence of the amplitude of the chirping spectrum on the wave number is derived, which shows the potential for applications such as weak forces detections. Furthermore, long chirped FBGs can be easily inscribed using standing waves in As2Se3-PMMA tapered fibers, which is far beyond the practical length of phase masks and is desirable for chromatic dispersion compensation in high-bit-rate optical fiber transmission systems and distributed sensors. A 50 cm long chirped FBG with chirped wavelength range of ~15 nm is inscribed and characterized. Fabrication of chirped FBGs in non-uniform single-core As2Se3-PMMA tapers opens the path towards the realization of novel sensors and devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".