Promoting Energy Transfer via Manipulation of Crystallization Kinetics of Quasi‐2D Perovskites for Efficient Green Light‐Emitting Diodes
Bibliographic record
Abstract
Abstract Quasi‐2D (Q‐2D) perovskites are promising materials applied in light‐emitting diodes (LEDs) due to their high exciton binding energy and quantum confinement effects. However, Q‐2D perovskites feature a multiphase structure with abundant grain boundaries and interfaces, leading to nonradiative loss during the energy‐transfer process. Here, a more efficient energy transfer in Q‐2D perovskites is achieved by manipulating the crystallization kinetics of different‐ n phases. A series of alkali‐metal bromides is utilized to manipulate the nucleation and growth of Q‐2D perovskites, which is likely associated with the Coulomb interaction between alkali‐metal ions and the negatively charged PbBr 6 4– frames. The incorporation of K + is found to restrict the nucleation of high‐ n phases and allows the subsequent growth of low‐ n phases, contributing to a spatially more homogeneous distribution of different‐ n phases and promoted energy transfer. As a result, highly efficient green Q‐2D perovskites LEDs with a champion EQE of 18.15% and a maximum brightness of 25 800 cd m –2 are achieved. The findings affirm a novel method to optimize the performance of Q‐2D 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".