Tunable Rayleigh scattering in low-loss Sr-based nanoparticle-doped optical fibers: Controlling nanoparticle features throughout preform and fiber fabrication
Bibliographic record
Abstract
Alkaline earth nanoparticles in-situ grown on silica-based optical fibers are promising for distributed sensing applications. To date, only Ca-based nanoparticles have been proven to be suitable compositions for long-range distributed fiber sensing based on Rayleigh scattering enhancement. Herein, we extend this approach and demonstrate for the first time that Sr-based nanoparticles grown in-situ in silica-based optical fiber cores are suitable to fabricate low-loss Rayleigh scattering enhanced nanoparticle-doped optical fibers with tunable performance. A thorough microstructural study about the influence of preform and fiber manufacture conditions on the nanoparticle characteristics reveal their great impact, and the possibility of considerably tailoring their features throughout fabrication process. This simultaneously determines the tunability of the induced Rayleigh scattering and optical attenuation. Consequently, we improve the state-of-art trade-off between Rayleigh scattering enhancement and two-way optical losses, showing values of 26.4–43.8 dB, regarding a SMF-28, and 0.2–5.1 dB/m, respectively. This allows long-range sensing lengths from 8.6 m to 132 m. Our findings provide new insights into in-situ grown alkaline earth nanoparticles and establish solid guidelines to tailor nanoparticle features in nanoparticle-doped optical fibers, which open new prospects in optical fiber community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".