Near-infrared quantum cutting luminescence in Pr3+/Yb3+ doped lead bismuth borate glass
Bibliographic record
Abstract
Abstract In this paper, thermally stable lead-bismuth-borate glasses were doped with 0.5 mol% of Pr3+ ions at several concentration levels of Yb3+ ions. Structural characterizations were performed via Raman, differential scanning calorimetry, optical absorption and fluorescence spectra. The Judd–Ofelt intensity parameter, $${\Omega }_2$$ Ω 2 , of Pr3+ doped glass was comparatively higher than those from reported ones, which reflects the increase of co-valency and asymmetry of chemical bonds in the local environment of Pr3+. Near-infrared emission in 900–2200 nm wavelength range was recorded through 443 nm blue laser pumping. Visible to near-IR quantum cutting and concentration quenching mechanisms were discussed to understand the luminescent behaviour. Intense IR emission ( $$\sim 1.0\,\upmu {\text {m}})$$ ∼ 1.0 μ m ) features generated by absorbing one visible photon leads to quantum efficiencies close to 128% in Pr3+/Yb3+ co-doped samples which may improve the solar spectrum absorption and accordingly, increase the efficiency of c-Si solar cells. Emission cross-section, lifetime, figure of merit and gain bandwidth corresponding to Pr3+: $$^3F_2 \rightarrow ^3H_4$$ 3 F 2 → 3 H 4 ( $$\sim 2.0\,\upmu$$ ∼ 2.0 μ m) were comparatively reported suggesting that the glass with molar composition 0.5Pr3+/0.1Yb3+ might be a potential candidate for $$\sim 2.0\,\upmu$$ ∼ 2.0 μ m laser operation with low pump threshold.
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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) | 0.000 | 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".