Enhancement of Red Emission (<sup>4</sup>F<sub>9/2</sub> → <sup>4</sup>I<sub>15/2</sub>) via Upconversion in Bulk and Nanocrystalline Cubic Y<sub>2</sub>O<sub>3</sub>:Er<sup>3+</sup>
Bibliographic record
Abstract
In this paper, we investigate as a function of erbium concentration (1, 2, 5, 10 mol %) the upconversion properties of bulk and nanocrystalline Er 3+ doped cubic Y 2 O 3 . After excitation at short wavelength (488 nm) or with upconversion pumping (815 nm), green and red emissions were observed for both nanocrystalline and bulk samples in the visible region of the spectrum, while blue emission was observed in bulk Y 2 O 3:Er 3+ only. The upconverted emission after 815 nm excitation revealed an enhancement of the red [ 4 F 9/2 → 4 I 15/2 ] emission with respect to the green [( 2 H 11/2, 4 S 3/2 ) → 4 I 15/2 ] emission when the dopant concentration is increased. However, the magnitude of the red enhancement in the bulk material differs slightly from its nanocrystalline counterpart, with the nanocrystalline material showing a higher degree of dependence on the dopant Er 3+ concentration. It is believed that two distinct mechanisms are responsible for populating the 4 S 3/2 and 4 F 9/2 levels, with the latter being more efficient at higher Er 3+ ion concentration.
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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) | 0.001 | 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".