Unveiling Structural Insights into Nanocrystal-Doped Optical Fibers via Confocal Raman Microscopy
Bibliographic record
Abstract
An in-depth characterization of nanoparticle-doped optical fibers is crucial to understand the potential new functionalities of the engineered glass and thus their applicability fields. The high temperatures of the manufacturing process strongly affect the nanoparticle features, and therefore, their analysis is necessary after fiber drawing. However, the difficulties associated with the use of atomic resolution microscopies to analyze the nanoparticle features in the fiber core, mainly related to sample preparation and expensive costs, usually prevent their study. In this work, we overcome some of those limitations and demonstrate, for the first time, the suitability of structurally and microstructurally studying in detail nanocrystals contained in a fiber core of ∼10 μm by combining confocal Raman microscopy, Rayleigh light-scattering microscopy, and scanning electron microscopy (SEM). A thorough study of cubic-shaped and rod-shaped YPO 4 nanocrystals contained in optical fibers reveals their crystallization in tetragonal (t) and monoclinic (m) structures, respectively. The symmetric (ν 1 ) and asymmetric stretching (ν 3 ) Raman modes display a different and remarkable red shift as particle size decreases in both types of nanocrystals, which in the case of the cubic-shaped nanocrystals is fitted to an exponential function along with a Raman peak broadening. Moreover, their Raman dependence vs temperature is evaluated up to 600 °C, observing a phonon softening that follows a linear behavior, which is discussed in detail. These findings add new insights to pure m-YPO 4, which was unknown to date, and the REPO 4 family and open up new avenues that can be extrapolated to other nanostructures incorporated into optical fiber cores, which will advance progress in the field of nanoparticle-doped optical fibers.
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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) | 0.001 | 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".