Relations between phosphorus/aluminum concentration ratio and photodarkening rate and loss in Yb-doped silica fibers
Bibliographic record
Abstract
The relations between dopant concentrations (phosphorus and aluminum) and photodarkening rate, excess loss, and activation energies in ytterbium-doped silica fibers are experimentally investigated. It is shown that increasing the concentration of phosphorus from 0.2 to 2.5 mol% in phosphorus/aluminum codoped fiber cores decreases the photodarkening excess loss by a factor of 8 and the photodarkening rate by a factor of 10. Moreover, the effective number of ytterbium ions involved in the photodarkening process increases from 4 to more than 6 for tested phosphorus/aluminum concentration ratios varying from 0.1 to 1 respectively. In contrast, increasing the aluminum concentration from 2 to 5 mol% for a fixed phosphorus concentration of 0.2 mol% has negligible effect on the initial photodarkening rate or the effective number of ytterbium ions involved in the process, but still decreases the photodarkening excess loss by a factor of 5. Those results suggest photodarkening activation energies of 5.2 eV for ytterbium/aluminum-codoped silica fibers and more than 7.8 eV for ytterbium/phosphorus/aluminum-codoped silica fibers. The net improvement in photodegradation of fiber amplifiers based on such phosphorus and aluminum codoping is measured experimentally and numerically simulated. The output power loss of 1064-nm ytterbium-doped LMA fiber amplifiers with phosphorus/aluminum ratios of 0.1 and 0.6 is reduced after 10 000 hours from 17% to less than 2%, respectively. Better understanding of the effects of phosphorus and aluminum on photodarkening will help to design reliable and efficient ytterbium-doped fiber amplifiers.
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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 | 0.001 |
| 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".