Recent improvements on mid-IR chalcogenide optical fibers
Bibliographic record
Abstract
Fabrication process of arsenic-sulfide (As-S) and arsenic-selenide (As-Se) optical fibers has been improved to enhance the transmission in the mid-IR region. Typical attenuation spectrum of As-S or As-Se optical fibers shows impurities bands, such as S-H, Se-H, O-H, which limit their operation and cause the increase of the attenuation loss in the mid-IR. Precursors purification methods and glass processing were improved to minimise those impurities bands. Regarding As- S fibers, the attenuation around 2.7 μm is 0.12 dB/m and S-H concentration is lower than 0.3 ppm. In the case of As-Se fibers, the minimum of attenuation located at 6 μm is 0.2 dB/m and Se-H concentration is lower than 0.5 ppm. Efforts have been also made to improve the mechanical properties which are usually affected by several parameters such as drawing conditions or heterogeneous inclusions contained in the glass. The double-crucible method gives high quality core/clad interface and consequently increases the strength of the fiber. Inclusions consist mainly of carbon and silica particles. Those impurities enter the glass from initial precursors and are also formed by interaction with the apparatus material. Thanks to the process improvement, impurities particles are minimized and tensile strengths up to 0.32 GPa and 0.41 GPa are reached for As-Se and As-S fibers respectively.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".