Towards REPO4 nanocrystal-doped optical fibers for distributed sensing applications
Bibliographic record
Abstract
Abstract Rayleigh scattering enhanced nanoparticle-doped optical fibers, for distributed sensing applications, is a new technology that offers unique advantages to optical fiber community. However, the existing fabrication technology, based on in situ grown alkaline earth nanoparticles, is restricted to few compositions and exhibit a great dependence on many experimental conditions. Moreover, there is still several uncertainties about the effect of drawing process on the nanoparticle characteristics and its influence on the scattering enhancement and the induced optical loss. In this work, we shed light on all these issues that prevent the progress in the field and demonstrate the suitability of doping optical fibers with YPO4 nanocrystals for developing tunable Rayleigh scattering enhanced nanoparticle-doped optical fibers. An exhaustive 3D microstructural study reveals that their features are closely linked to the fiber drawing process, which allow the size and shape engineering at the nanoscale. In particular, the YPO4 nanocrystals preserve their features to a large extent when the optical fibers are drawn below 1950 °C, which allows obtaining homogeneous nanocrystal features and optical performance. Fabricated fibers exhibit a tunable enhanced backscattering in the range of 15.3–54.3 dB, with respect to a SMF-28 fiber, and two-way optical losses in the range 0.3–160.7 dB/m, revealed by Optical Backscatter Reflectometry (OBR) measurements. This allows sensing lengths from 0.3 m up to more than 58 m. The present work suggests a bright future of YPO4 nanocrystals for distributed sensing field and open a new gate towards the incorporation of other rare-earth orthophosphate (REPO4) nanocrystals with pre-defined characteristics that will overcome the limitations of the current in situ grown alkaline earth-based technology.
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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".