Optical investigation and thermal stabilization of germanium-gallium-containing bismuthate glasses towards NIR-MIR applications
Bibliographic record
Abstract
The progress in the utilization of heavy metal oxide glasses in mid-infrared photonic applications has been hindered by the complication of fabricating thermally stable glass compositions as well as optical fibers with acceptable optical losses. To overcome these obstacles, a range of BaO-Ga 2 O 3 -GeO 2 -Bi 2 O 3 glasses were fabricated by increasing the BaO content while decreasing Bi 2 O 3 . Differential scanning calorimetry studies revealed that the thermal stability of the glasses increases up to 27.5 mol% BaO at which the glass does not yield a crystallization peak even at very low heating rates. High linear and nonlinear refractive index of the glasses were also shown. Spectroscopic properties of the thermally most stable glass were investigated by various Er 3+ -doping content. The luminescence spectra and lifetime values showed suitable results, even with high doping content, for applications requiring optical signal amplification. A multimode optical fiber with an undoped core was drawn by rod-in-tube technique which yielded an optical loss of 10 dB/m for a 3-meter-long fiber. With appropriate precursor purification, glass dehydration and glass fabrication optimization steps, BaO-Ga 2 O 3 -GeO 2 -Bi 2 O 3 glasses are important alternatives to PbO-GeO 2 and TeO 2 -based glasses for MIR applications in optical fiber or bulk glass form.
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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".