Ambient fabrication of efficient triple cation perovskite-based near-infrared light-emitting diodes
Bibliographic record
Abstract
In addition to their widespread use as an outstanding light-harvesting material, solution-based organometallic halide perovskites have also recently emerged as a promising material for light-emitting diode (LED) applications. However, their stability under an ambient environment remains a challenge. Triple cation perovskites offer an appealing solution as it reduces the sensitivity to the processing conditions and improves the purity of the perovskite films. This work describes a facile ambient-processed thiocyanate-doped triple-cation perovskite Csx(MA0.17FA0.83)Pb(100-x)(I0.83Br0.17)3 used for high-performance perovskite-based LEDs with peak emission at 750 nm. Using the perovskite film tailoring technique by mixing DMF (N,N-Dimethylmethanamide) with perovskite precursor, we are able to reduce the perovskite grain size and optimize the film thickness while preserving its crystalline structure. With optimized processing techniques, we achieve a ∼90% improvement of the perovskite LEDs external quantum efficiency (EQE) from ∼3.1% to ∼5.9%. We believe this triple cation perovskite synthesis approach and film tailoring technique yields excellent device performances and constitutes a significant step towards low-cost and efficient LEDs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".