Modulation in charge distribution via polyethylenimine-incorporation into ZnO electron transport layer and its impact on quantum-dots light emitting device stability
Bibliographic record
Abstract
Polyethylenimine (PEI) is sometimes used as a passivation layer at the interface between ZnO electron transport layer and quantum-dots emission layer in quantum-dots light emitting devices (QDLEDs). We recently find that blending ZnO with PEI (ZnO:PEI) is advantageous over using it in a separate layer in terms of device stability. In this work, a comparative study between the ZnO:PEI with a neat ZnO ETL is conducted. The ZnO:PEI ETL results in improvement in both EQE and lifetime of QDLEDs compared to the ZnO ETL. By replacing the ZnO ETL with the ZnO:PEI ETL, delayed EL measurements reveal changes in charge distribution across the QDLED. Applying a reverse bias pulse shows that the reversible delayed EL components in the QDLED with the ZnO:PEI ETL stemmed from the electrons placed in a hole transport layer (HTL). The electrons in the HTL induce an annihilation of accumulated holes at the QD EML/HTL that can be a cause of device degradation. The result provides a new insight into the importance of managing charge distribution across the QDLED via ZnO ETL modification for realizing highly stable QDLEDs.
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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".