An investigation on the cyclic temperature-dependent performance behaviors of ultrabright air-stable QLEDs
Bibliographic record
Abstract
Abstract The aerobic and thermal stability of quantum-dot light-emitting diodes (QLEDs) is an important factor for the practical applications of these devices under harsh environmental conditions. We demonstrate all-solution-processed amber QLEDs with an external quantum efficiency (EQE) of > 14% with almost negligible efficiency roll-off (droop) and a peak brightness of > 600,000 cd/m2, unprecedented for QLEDs fabricated under ambient air conditions. We investigate the device efficiency and brightness level at a temperature range between − 10 and 85 °C in a 5-step cooling/heating cycle. We conducted the experiments at brightness levels higher than 10,000 cd/m2, required for outdoor lighting applications. Our device performance proves thermal stability, with minimal standard deviation in the performance parameters. Interestingly, the device efficiency parameters recover to the initial values upon returning to room temperature. The variations in the performance are correlated with the modification of charge transport characteristics and induced radiative/non-radiative exciton relaxation dynamics at different temperatures. Being complementary to previous studies on the subject, the present work is expected to shed light on the potential feasibility of realizing aerobic-stable ultrabright droop-free QLEDs and encourage further research for solid-state lighting applications.
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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 | 0.001 |
| 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) | 0.002 | 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".