Overview of laser imprinted refractive index changes and related thermal stability in mid-infrared optical glasses
Bibliographic record
Abstract
Various glass compositions adapted to mid-infrared (heavy metal oxide, fluoride and chalcogenide glasses) were irradiated by femtosecond laser and their refractive index variations characterized as a function of laser pulse energy. Their thermal stability was studied through isochronal annealing conditions in order to determine their erasure temperatures. The measured index variations were compared to values collected from literature for a broad range of glass candidates. Additional chalcogenide glasses studied in this article (e.g. GeSbS and 75GeS 2 -15In 2 S 3 –10CsCl) present very high refractive index variations (up to 5.5·10 −2 ), almost 3 times the maximum value reported in heavy metal oxide glasses (2·10 −2 , Corning 9754) or SiO 2 , bringing interest for the development of mid-infrared optical devices. Nevertheless, they can be limited by their relatively low thermal stability, since the refractive index variations fully erase around their glass transition temperatures (typ. 200–300 °C for chalcogenide glasses studied herein). • Fs laser direct writing creates index changes in mid-IR glasses, being useful for optical devices and sensors. • Chalcogenide glasses offer the highest refractive index contrast compared to heavy metal oxide and fluoride glasses. • In chalcogenides, these changes disappear at 200–300 °C during isochronal annealing, due to structural relaxation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".