Chlorine Vacancy Passivation in Mixed Halide Perovskite Quantum Dots by Organic Pseudohalides Enables Efficient Rec. 2020 Blue Light-Emitting Diodes
Bibliographic record
Abstract
Blue-emitting perovskites can be easily attained by precisely tuning the halide ratio of mixed halide (Br/Cl) perovskites (MHPs). However, the adjustable halide ratio hinders the passivation of Cl vacancies, the main source of trap states leading to inferior performance of blue MHP light-emitting diodes (LEDs). Here, we report a strategy for passivating Cl vacancies in MHP quantum dots (QDs) using nonpolar solvent-soluble organic pseudohalide [ n -dodecylammonium thiocyanate (DAT)], enabling blue MHP LEDs with greatly enhanced efficiency. Density functional theory calculations reveal that the thiocyanate (SCN – ) groups fill in the Cl vacancies and remove electron traps within the bandgap. DAT-treated CsPb(Br x Cl 1– x ) 3 QDs exhibit near unity (∼100%) photoluminescence quantum yields, and their blue (∼470 nm) LEDs are spectrally stable with an external quantum efficiency of 6.3%, a record for perovskite LEDs emitting in the range of 460–480 nm relevant to Rec. 2020 display standards, and a half-lifetime of ∼99 s.
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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".