Chalcogenide Taper and Its Nonlinear Effects and Sensing Applications
Bibliographic record
Abstract
The nonlinear coefficient of chalcogenide glass is 200-1000 times larger than that of silica glass, and it is transparent in the 1-15 μm wavelength windows, which makes the nonlinear effects happen at much low power with a short length in near- and mid-infrared wavelength window. With tapered chalcogenide fibers, the power density in the core and the waveguide nonlinearity can be enhanced to make nonlinear signal processing unit at a compact size. The threshold of Raman scattering and supercontinuum generation is reduced due to the enhanced Kerr effect and enhanced optical power intensity. Phase-matching condition required in four-wave mixing (FWM) can be realized by tailoring fiber structures to engineer the chromatic dispersion, which enables new wavelengths creation over a large range at mW power and sub-meter length. The guided acoustic waves and longitudinal acoustic waves can be generated and detected in mW power with chalcogenide tapers. The high power density in the microwires and the high photosensitivity of chalcogenide glass in the 1550 nm band enable the inscription of FBGs in the fiber directly. The chalcogenide microwires are fragile and the core diameter cannot be tapered down to sub-microns, which can be mitigated by polymer coating that can provide mechanical strength. Polymers not only provide high mechanical strength as the coating and cladding materials but also bring over 10 times larger thermal expansion than chalcogenide cores, which enhances the sensor prospect of the chalcogenide fibers for temperature, strain, and acoustic sensing. This work reviews the present and emerging trends in investigation of chalcogenide tapers, mainly focusing on the fabrication procedure of chalcogenide microwires, the nonlinear effects, and sensing applications.
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".