Entropy-Driven Strongly Confined Low-Toxicity Pure-Red Perovskite Quantum Dots for Spectrally Stable Light-Emitting Diodes
Bibliographic record
Abstract
Spectrally stable pure-red perovskite quantum dots (QDs) with low lead content are essential for high-definition displays but are difficult to synthesize due to QD self-purification. Here, we make use of entropy-driven quantum-confined pure-red perovskite QDs to fabricate light-emitting diodes (LEDs) that have low toxicity and are efficient and spectrum-stable. Based on experimental data and first-principles calculations, multiple element alloying results in a 60% reduction in lead content while improving QD entropy to promote crystal stability. Entropy-driven QDs exhibit photoluminescence with 100% quantum yields and single-exponential decay lifetimes without alteration of their morphology or crystal structure. The pure-red LEDs utilizing entropy-driven QDs have spectrally stable electroluminescence, achieving a brightness of 4932 cd/m 2, a maximum external quantum efficiency of over 20%, and a 15-fold longer operational lifetime than the CsPbI 3 QD-based LEDs. These achievements demonstrate that entropy-driven QDs can mitigate local compositional heterogeneity and ion migration.
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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".