Extraordinary Mass Transport and Self‐Assembly: A Pathway to Fabricate Luminescent CsPbBr<sub>3</sub> and Light‐Emitting Diodes by Vapor‐Phase Deposition
Bibliographic record
Abstract
Abstract Halide perovskites have been shown to be promising materials in making light‐emitting diodes. At present, almost all of perovskite materials are made by solution‐based synthesis. There are very limited reports on fabricating perovskite LEDs by vapor‐phase deposition (VPD), a method that can be easily scaled up for commercial production. In this paper, dual‐source VPD is used to fabricate stable CsPbBr3 perovskite thin films with excellent luminescent properties. Scanning electron microscope and atomic force microscope studies show that CsPbBr3 films, when coated with a thin LiBr overlayer, demonstrate an extraordinary mass transport at room temperature to re‐assemble into well‐defined islands. LiBr is also shown to passivate nonradiative defects and boost photoluminescence performance of the CsPbBr3, improving the intensity by a factor of 11 for a nominal 18 nm perovskite film and leading to extremely narrow photoluminescence peaks (16 nm FWHM). This self‐assembled perovskite LED shows major improvement in the electroluminescence performance, almost tripling the brightness of reference devices. X‐ray photoelectron spectroscopy measurement shows that surface LiBr improves Cs/Pb chemical stoichiometry, reduces Br vacancies, and shift the Fermi energy level toward conduction band minimum.
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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 | 0.001 |
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".