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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".