Cubic-Shaped and Rod-Shaped YPO<sub>4</sub> Nanocrystal-Doped Optical Fibers: Implications for Next Generation of Fiber Lasers
Bibliographic record
Abstract
Engineering silica optical fibers by nanoparticle doping is a promising technology that allows the introduction of new functionalities and extends their applicable fields. However, the knowledge gap about the impact of the extreme fabrication temperatures on the nanoparticle features prevents field progress. Herein, we demonstrate that the particularities of fiber fabrication, such as fast-heating rates and quenching heat treatments, can be leveraged to explore unlikely phenomena at the nanoscale under standard laboratory conditions. Tetragonal cubic-shaped and monoclinic rod-shaped YPO 4 nanocrystals are in situ nucleated in a silica-based fiber core glass, slightly modified with Ge and P, which shows for the first time, the possibility of doping optical fibers with this type of nanostructures, in terms of shape, composition, and structure of the nanocrystals. Structural and anisotropic differences allow engineering differently their shape and composition in the fiber core by tailoring the drawing temperature, as revealed by a thorough study consisting of scanning electron microscopy (SEM), high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), electron energy loss spectroscopy (EELS), and high-resolution transmission electron microscopy (HRTEM). This work demonstrates, for the first time, the possibility of doping optical fibers fabricated by modified chemical vapor deposition (MCVD) with anisotropic nanostructures, as well as the stability of the monoclinic YPO 4 phase. These findings open up new avenues to study shape-dependent properties of rare-earth orthophosphate (REPO 4 ) nanostructures in optical fibers which will allow incorporating unprecedented functionalities and will have an impact in several fields of application, such as fiber lasers and optical fiber amplifiers, among others.
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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 | 0.001 | 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".