ZnSe:(Cr,Fe) laser crystal matrices: Challenges related to doping
Bibliographic record
Abstract
We investigate co-doped ZnSe:(Cr, Fe) laser crystals with an approach that includes both optical spectroscopy and theoretical modeling. We found that concentrations of chromium and iron up to 1018 cm-3 are optimal for obtaining a homogeneous solid solution in designing laser crystals. Higher dopant concentrations lead to the formation of clusters with spinel structure in the ZnSe matrix. The optimal effectiveness of ZnSe laser media with active Cr2+ and Fe2+ elements is achieved at temperatures of around 100 K. Heating induces charge transfers like Fe2+→Fe3+ and Cr2+→Cr3+, while cooling results in temperature quenching. A theoretical model has been developed to explain the observed temperature evolution of the absorption spectrum. The types and values of distortions of the Cr- and Fe-based coordination complexes are determined. We have found that the Jahn-Teller distortions are crucial for modelling the temperature-dependent changes of the absorption spectra.
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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 | 0.001 | 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".