Spectrally Tunable and Stable Electroluminescence Enabled by Rubidium Doping of CsPbBr<sub>3</sub> Nanocrystals
Bibliographic record
Abstract
Abstract Perovskite nanocrystals exhibit high photoluminescence quantum yields (PLQYs) and tunable bandgaps from ultraviolet to infrared. However, blue perovskite light‐emitting diodes (LEDs) suffer from color instability under applied bias. Developing narrow‐bandwidth deep‐blue emitters will maximize the color gamut of display technologies. Mixed anion approaches suffer from halide segregation that leads to their spectral instability. Here instead, a mixed cation strategy is employed whereby Rb + is directly incorporated during synthesis into CsPbBr 3 nanocrystals. Blue‐emitting perovskite quantum dots (QDs) with stable photoluminescence, PLQYs greater than 60%, tunable emission from 460 to 500 nm, and narrow emission linewidths (<25 nm) are reported. The strategy retains a pure bromine crystal structure resulting in color‐pure stable electroluminescence at operating voltages of up to 10 V, peak external quantum efficiencies (EQEs) of 0.87% and 0.11% for sky‐blue (490 nm), and deep‐blue (464 nm) devices. The sky‐blue devices exhibit the highest combined luminance of 93 cd m −2 at an EQE of 0.75%, the best reported to date of perovskite QD 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".