Progressive quenching of luminescence from quantum dot thin films in proximity with ZnMgO in unencapsulated stacks
Bibliographic record
Abstract
ZnMgO nanoparticles (NPs) are being increasingly used as the electron transport layer (ETL) in state-of-the-art quantum-dot light-emitting devices (QLEDs) instead of ZnO. However, the impact of ZnMgO on the luminescence properties of quantum dots (QDs) is much less understood. Here, we compare ZnMgO and ZnO NPs for their quenching effect on Cd-based QDs photoluminescence (PL), immediately and over time. Time-resolved photoluminescence (TRPL) and steady-state PL results show that ZnMgO NPs decreases the QDs’ luminescence more than ZnO NPs and that the behavior continues progressively over time. The surface topography of the samples containing different ETLs is studied using atomic force microscopy (AFM) and optical PL images. Additionally, time of flight secondary ion mass spectroscopy (TOF-SIMS) measurements are conducted to investigate the potential diffusion of some species from ETL into the QDs layer. The results confirm that morphological changes and out-diffusion of some species from the ZnMgO layer can likely play a role in the QDs PL quenching. This study sheds light on the limitations of ZnMgO for the long-term stability of QLEDs, specifically for blue QLEDs where using ZnMgO is essential for efficient electron injection.
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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 | 0.000 |
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".