Direct synthesis of high quantum yield lead‐free CsCu<sub>2</sub>I<sub>3</sub> powder in water and its application in yellow LED
Bibliographic record
Abstract
Abstract Yellow light‐emitting diodes (LEDs) with a wavelength of 570–590 nm can reduce the excitability of peripheral nerves and the sensitivity of the skin, stimulate collagen synthesis, and tighten the skin, which plays an important role in skin rejuvenation. In general, commercial LEDs are made of phosphor excited by ultraviolet chips. It is very important for the development of yellow light emitters with high luminous efficiency, good stability, and environmental protection. For the first time, a simple organic structural unit (2‐methylimidazole, 2‐MIM) was used to collect a mixture of two metal precursors (CsI and CuI) and successfully synthesized an all‐inorganic lead‐free yellow light CsCu2I3 powder in water. The prepared CsCu2I3 powder exhibited excellent optical properties and considerable stability. Finally, a phosphor‐converted LED (pc‐LED) device was fabricated via the CsCu2I3 phosphor coated on a 310 nm ultraviolet chip. The pc‐LED device's electroluminescence spectra may be a good fit for the blood's absorption regions. Therefore, this work provides a facile method for the synthesis of novel lead‐free metal halide CsCu2I3 powder in eco‐friendly solvents. In addition, the stable and efficient CsCu2I3 powder shows promising exciting potential applications in photoluminescence and phototherapy fields.
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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".