The influence of the electron transport and emission layers on the morphological instability of QDLEDs
Bibliographic record
Abstract
The exceptional luminescence properties of colloidal quantum dots (QDs) make them advantageous for use as an electroluminescent material in light emitting devices (QDLEDs). Drastic improvements in the performance of QDLEDs have been achieved through the use of inorganic electron transport layers and organic hole transport layers (HTLs), yet the electroluminescence stability of QDLEDs remains insufficient for commercial applications. To address the issue of QDLED stability, significant work has been done to reduce charge imbalance and Auger recombination in the QDs which arises from the large energy level mismatch between the valence band of the QD and the highest occupied molecular orbital (HOMO) of the HTL. This work identifies morphological stability within QDLEDs as an additional degradation mechanism limiting device stability. Interaction between the HTL and surface roughness of the underlying layers appears to be a critical parameter to address in QDLED design. Studies of QDLEDs using electrical measurements and electroluminescence imaging elucidate upon the role that morphological stability plays in the degradation of electroluminescent 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 | 0.001 |
| 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) | 0.001 | 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".