Investigation of Ga<sub>2</sub>O<sub>3</sub>-BaO-GeO<sub>2</sub> glasses for ultrafast laser inscription
Bibliographic record
Abstract
From the perspective of developing robust mid-infrared (mid-IR) integrated photonic devices, barium-gallium-germanium (BGG) oxide glasses are strong candidates among other mid-IR glasses. Indeed, compared to fluoride, tellurite or chalcogenide glasses, BGG glasses present the highest thermal and chemical stabilities, while transmitting light up to 6 µm. In parallel to this, technological advances in ultrafast direct laser writing (UDLW)-based devices are driving the development of novel photonic glasses. Specifically, there is a need to identify the most efficient mid-infrared transmitting BGG glass compositions for sustaining the UDLW process. In this article, we thoroughly investigate the BGG physicochemical properties through absorption and Raman spectroscopies, refractive index, density, and glass transition temperature measurements in two relevant glass series: one via a Ga3+/Ge4+ ratio fixed to 1 and a barium content varying from 25 to 40 cationic percent, the other via a 2Ba2+/Ga3+ ratio fixed to 1 and a germanium content varying from 20 to 80 cationic percent. In the meantime, we explore the photosensitivity of these glasses under UDLW. Our findings reveal the valuable role of both barium and gallium ions, notably through their concentration, structural stabilization sites and viscosity influence. Finally, we demonstrate the fabrication of an 8.2 cm-long UDLW-induced waveguide with propagation losses of < 0.3 dB.cm-1 at 1550 nm.
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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".