Engineering nanoparticle features to tune Rayleigh scattering in nanoparticles-doped optical fibers
Bibliographic record
Abstract
Rayleigh scattering enhanced nanoparticles-doped optical fibers are highly promising for distributed sensing applications, however, the high optical losses induced by that scattering enhancement restrict considerably their sensing distance to few meters. Fabrication of long-range distributed optical fiber sensors based on this technology remains a major challenge in optical fiber community. In this work, it is reported the fabrication of low-loss Ca-based nanoparticles doped silica fibers with tunable Rayleigh scattering for long-range distributed sensing. This is enabled by tailoring nanoparticle features such as particle distribution size, morphology and density in the core of optical fibers through preform and fiber fabrication process. Consequently, fibers with tunable enhanced backscattering in the range 25.9-44.9 dB, with respect to a SMF-28 fiber, are attained along with the lowest two-way optical losses, 0.1-8.7 dB/m, reported so far for Rayleigh scattering enhanced nanoparticles-doped optical fibers. Therefore, the suitability of Ca-based nanoparticles-doped optical fibers for distributed sensing over longer distances, from 5 m to more than 200 m, becomes possible. This study opens a new path for future works in the field of distributed sensing, since these findings may be applied to other nanoparticles-doped optical fibers, allowing the tailoring of nanoparticle properties, which broadens future potential applications of this technology.
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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 | 0.001 |
| 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".