Tailoring the Energy Landscape in Quasi-2D Halide Perovskites Enables Efficient Green-Light Emission
Bibliographic record
Abstract
Organo-metal halide perovskites are a promising platform for optoelectronic applications in view of their excellent charge-transport and bandgap tunability. However, their low photoluminescence quantum efficiencies, especially in low-excitation regimes, limit their efficiency for light emission. Consequently, perovskite light-emitting devices are operated under high injection, a regime under which the materials have so far been unstable. Here we show that, by concentrating photoexcited states into a small subpopulation of radiative domains, one can achieve a high quantum yield, even at low excitation intensities. We tailor the composition of quasi-2D perovskites to direct the energy transfer into the lowest-bandgap minority phase and to do so faster than it is lost to nonradiative centers. The new material exhibits 60% photoluminescence quantum yield at excitation intensities as low as 1.8 mW/cm 2, yielding a ratio of quantum yield to excitation intensity of 0.3 cm 2 /mW; this represents a decrease of 2 orders of magnitude in the excitation power required to reach high efficiency compared with the best prior reports. Using this strategy, we report light-emitting diodes with external quantum efficiencies of 7.4% and a high luminescence of 8400 cd/m 2 .
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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 | 0.001 | 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".