Development of ytterbium-doped oxyfluoride glasses for laser cooling applications
Bibliographic record
Abstract
Oxyfluoride glasses doped with 2, 5, 8, 12, 16 and 20 mol% of ytterbium (Yb(3+)) ions have been prepared by the conventional melt-quenching technique. Their optical, thermal and thermo-mechanical properties were characterized. Luminescence intensity at 1020 nm under laser excitation at 920 nm decreases with increasing Yb(3+) concentration, suggesting a decrease in the photoluminescence quantum yield (PLQY). The PLQY of the samples was measured with an integrating sphere using an absolute method. The highest PLQY was found to be 0.99(11) for the 2 mol% Yb(3+): glass and decreases with increasing Yb(3+) concentration. The mean fluorescence wavelength and background absorption of the samples were also evaluated. Upconversion luminescence under 975 nm laser excitation was observed and attributed to the presence of Tm(3+) and Er(3+) ions which exist as impurity traces with YbF3 starting powder. Decay curves for the Yb(3+): (2)F5/2 → (2)F7/2 transition exhibit single exponential behavior for all the samples, although lifetime decrease was observed for the excited level of Yb(3+) with increasing Yb(3+) concentration. Also observed are an increase in the PLQY and a slight decrease in lifetime with increasing the pump power. Finally, the potential of these oxyfluoride glasses with high PLQY and low background absorption for laser cooling applications is discussed.
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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) | 0.001 | 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".