Near and mid-infrared emissions implications of hydroxyl groups in Er<sup>3+</sup>-doped tellurite glasses: enhancing luminescent properties via purification processes
Bibliographic record
Abstract
Tellurite glasses are increasingly attracting interest from the photonics industry due to their promising spectroscopic properties. However, intrinsic absorption losses caused by impurities, especially hydroxyl groups (OH - ) remain a significant challenge for achieving efficient infrared emission. This work presents what we believe to be a new purification method for Er 3+ -doped tellurite glasses using diethyl zinc to treat oxide precursors. Glasses with the composition 69.7 TeO 2 – 25 ZnO – 5 La 2 O 3 – 0.3 Er 2 O 3 (mol %) were fabricated at each step of the purification and characterized. The effect of the purification in the reduction of OH content is evidenced in the decreased absorption coefficient from 2.57 to 0.22 cm - ¹ at 3300 nm after applying the chemical treatment, which accounted for a decrease in the number of OH ions/cm 3 from 2.88 × 10 19 to 2.70 × 10 18 . The purification process significantly improved the optical properties, enabling consecutive emissions at 1535, 2700, and 3280 nm when pumped with an optical parametric oscillator (OPO) laser tuned to 522 nm. With the reduction of OH content, there was an increase in the emission efficiency from 78.6 to 93.9% and 7.3 to 10.7% for the emissions for 4 I 13/2 → 4 I 15/2 and 4 I 11/2 → 4 I 13/2 , respectively.
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".