Bright and Stable Light-Emitting Diodes Based on Perovskite Quantum Dots in Perovskite Matrix
Bibliographic record
Abstract
Light-emitting diodes (LEDs) based on metal halide perovskite quantum dots (QDs) have achieved impressive external quantum efficiencies; however, the lack of surface protection of QDs, combined with efficiency droop, decreases device operating lifetime at brightnesses of interest. The epitaxial incorporation of QDs within a semiconducting shell provides surface passivation and exciton confinement. Achieving this goal in the case of perovskite QDs remains an unsolved challenge in view of the materials’ chemical instability. Here, we report perovskite QDs that remain stable in a thin layer of precursor solution of perovskite, and we use strained QDs as nucleation centers to drive the homogeneous crystallization of a perovskite matrix. Type-I band alignment ensures that the QDs are charge acceptors and radiative emitters. The new materials show suppressed Auger bi-excition recombination and bright luminescence at high excitation (600 W cm –2 ), whereas control materials exhibit severe bleaching. Primary red LEDs based on the new materials show an external quantum efficiency of 18%, and these retain high performance to brightnesses exceeding 4700 cd m –2 . The new materials enable LEDs having an operating half-life of 2400 h at an initial luminance of 100 cd m –2, representing a 100-fold enhancement relative to the best primary red perovskite LEDs.
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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".