Tunable distributed sensing performance in Ca-based nanoparticle-doped optical fibers
Bibliographic record
Abstract
Rayleigh scattering enhanced nanoparticle-doped optical fibers is a technology very promising for distributed sensing applications, however, it remains largely unexplored. This work demonstrates for the first time the possibility of tuning Rayleigh scattering and optical losses in Ca-based nanoparticle-doped silica optical fibers by controlling the kinetics of the re-nucleation process that nanoparticles undergo during fiber drawing by controlling preform feed, drawing speed and temperature. A 3D study by SEM, FIB-SEM and optical backscatter reflectometry (OBR) reveals an early-time kinetics at 1870 °C, with tunable Rayleigh scattering enhancement 43.2–47.4 dB, regarding a long-haul single mode fiber, SMF-28, and associated sensing lengths of 3–5.5 m. At 2065 °C, kinetics is slower and nanoparticle dissolution is favored. Consequently, enhanced scattering values of 24.9–26.9 dB/m and sensing lengths of 135–250 m are attained. Finally, thermal stability above 500 °C and tunable distributed temperature sensitivity are proved, from 18.6 pm/°C to 23.9 pm/°C, ∼1.9–2.4 times larger than in a SMF-28. These results show the promising future of Rayleigh scattering enhanced nanoparticle-doped optical fibers for distributed sensing.
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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) | 0.000 | 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".