Quantum Dots in Two-Dimensional Perovskite Matrices for Efficient Near-Infrared Light Emission
Bibliographic record
Abstract
Quantum-dot-in-perovskite solids are excellent candidates for infrared light-emitting applications. The first generation of dot-in-perovskite light-emitting diodes (LEDs) has shown bright infrared electroluminescence with tunable emission wavelength; however, their performance has been limited by degradation of the active layer at practical operating voltages. This arises from the instability of the three-dimensional (3D) organolead halide perovskite matrix. Herein we report the first dot-in-perovskite solids that employ two-dimensional (2D) perovskites as the matrix. 2D perovskite passivation is achieved via an in situ alkylammonium/alkylamine substitution carried out during the quantum dot (QD) ligand exchange process. This single-step film preparation process enables deposition of the QD/perovskite active layers with thicknesses of 40 nm, over seven times thinner than the first-generation dot-in-perovskite thin films that relied on a multistep synthesis. The dot-in-perovskite film roughness improved from 31 nm for the first-generation films to 3 nm for films as a result of this new approach. The best devices exhibit external quantum efficiency peaks exceeding 2% and radiances of ∼1 W sr –1 m –2, with an improved breakdown voltage up to 7.5 V. Compared to first-generation dot-in-perovskites, this new process reduces materials consumptions 10-fold and represents a promising step toward manufacturable devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".