Mn<sup>4+</sup>-Activated Oxyfluoride CsNaWO<sub>2</sub>F<sub>4</sub> Phosphor: Enhancement in the Water Stability and Thermal-Quenching Resistance
Bibliographic record
Abstract
Mn 4+ -activated oxyfluoride phosphors play an important role in solid-state lighting and display areas due to the suitable red-light emission. However, the hydrolysis and thermal quenching of the phosphors restrict their practical applications. In this work, we prepared a novel CsNaWO 2 F 4:Mn 4+ oxyfluoride phosphor with high water stability and better thermal-quenching resistance; similar phosphors X 2 WO 2 F 4:Mn 4+ (X = Na, Cs) were used as reference. A new structure model and CIF files were established by simulation. Theoretical calculations and experiments were performed to investigate the increase in water stability and thermal-quenching resistance. The luminous properties are also discussed in detail. The results show that CsNaWO 2 F 4:Mn 4+ exhibits a superior moisture resistance, maintaining 92.2% of the initial intensity when soaked in deionized water for 1 h and 81.7% for 24 h. A favorable thermal-quenching resistance is obtained compared with the reference, retaining about 50% of its initial intensity at 373 K. Furthermore, the WLED device fabricated with as-prepared CsNaWO 2 F 4:Mn 4+ phosphors, YAG:Ce 3+ phosphors, and a 455 nm chip achieved a correlated color temperature (CCT) of 4701 K, a color rendering index (Ra) of 81.9, and a lumen efficiency of 142.52 lm/W.
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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".