Tailoring ultrabroadband near‐infrared luminescence in Bi-doped germanosilicate glasses
Bibliographic record
Abstract
Abstract Bi-doped glasses and optical fibers are extensively studied since they present broadband optical amplification in the near-infrared region (NIR), in which the optical telecommunication industry greatly depends for the transmission of optical signals. There are many scientific challenges about the NIR luminescent emissions from Bi ions, such as understanding its origin and further improving the associated optical amplification capacity. In this work, Bi-doped germanosilicate glass compositions with ultrabroadband NIR luminescence were fabricated, in the range of 925–1630 nm, which covers O, E, S, C, and L-telecommunication bands. An in-depth analysis of the impact of modifying excitation wavelengths, Bi content, and GeO2/SiO2 concentration ratio in the glass matrix demonstrates the possibility of considerably manipulating the Bi NIR luminescence, in terms of tuning emission parameters such as bandwidth, up to ~ 490 nm, and luminescence intensity. Based on theoretical and experimental luminescence data retrieved from the fabricated glasses, we demonstrate that the origin of broadband luminescence under all the considered excitation wavelengths can be ascribed to optical transitions of Bi0 ions. Therefore, an energy level diagram for Bi0 is proposed. We anticipate that our findings can provide clarifications to the existing uncertainty in the origin of Bi NIR emission, which will be useful to fabricate efficient future optical fiber amplifiers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".