Synthesis and Characterization of Ce‐Doped Y<sub>3</sub>Al<sub>5</sub>O<sub>12</sub> (YAG:Ce) Nanopowders Used for Solid‐State Lighting
Bibliographic record
Abstract
Nano‐Ce‐doped Y3Al5O12 (YAG:Ce) powders were synthesized by using a sol‐gel low temperature combustion method, followed by thermal annealing. The annealing temperature for enriching nanoparticles was optimized and found to be 1000°C. The process for enriching uniform nanoparticles of YAG:Ce powder was carried out by using the nanosteam technique (NST). The nanoparticles obtained from this NST treatment had a size in the range of 9–20 nm. Measurements of the photoluminescence spectra of the dispersed YAG:Ce nanoparticles solutions showed a blue shift in the photoemission with a value of ca. 10 nm in the green region. WLEDs made from the blue LED chip coated with the nano‐YAG:Ce + MEH‐PPV composite epoxy exhibit white light with a broad band luminescent spectrum and a high color rending index (CRI). The photoluminescence spectra of the YAG:Ce nanoparticles showed a potential application of the prepared nanostructured YAG:Ce phosphor not only in energy‐efficient solid‐state lighting, but also in optoelectronic devices, including organic composite solar cells. In addition, it is suggested that NST can be applied for the enrichment of uniform inorganic nanoparticles.
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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".