Uniform and Large‐Area Cesium‐Based Quasi‐2D Perovskite Light‐Emitting Diodes Using Hot‐Casting Method
Bibliographic record
Abstract
Abstract Metal halide perovskites have deep valence band maximums (VBMs). For example, the VBM of CsPbBr 3 is 5.8–6.3 eV. Conjugated polymers can be a potential candidate for the hole transport layer because of their deep highest occupied molecular orbital levels, but their poor compatibility with a hydrophilic perovskite precursor results in the formation of a noncontinuous perovskite film. In addition, antisolvent dripping methods for fabricating perovskite films cause spatially inhomogeneous nucleation, which is undesirable for large‐scale applications. In this work, efficient and large‐area perovskite light‐emitting diodes (PeLEDs) are developed by introducing a poly(9‐vinylcarbazole) (PVK) interlayer and employing a hot‐casting method (substrate preheating). The PVK interlayer increases the depth of the VBM of NiO x (from 5.1 to 5.5 eV), resulting in efficient hole injection. The thermal energy of the preheated substrate facilitates not only the growth of a continuous and pinhole‐free perovskite film, but also the formation of a highly crystalline and preferentially oriented perovskite structure, resulting in improved luminescence properties. Therefore, PeLEDs fabricated using an optimal preheating temperature show an improved external quantum efficiency (from 3.08% to 8.44%) with spatially uniform electroluminescence. Finally, the development of uniform and large‐area PeLEDs (with an active area of 12.8 cm 2 ) is demonstrated.
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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".