Optimizing ZnO–Quantum Dot Interface with Thiol as Ligand Modification for High‐Performance Quantum Dot Light‐Emitting Diodes
Bibliographic record
Abstract
Abstract As the electron transport layer in quantum dot light‐emitting diodes (QLEDs), ZnO suffers from excessive electrons that lead to luminescence quenching of the quantum dots (QDs) and charge‐imbalance in QLEDs. Therefore, the interplay between ZnO and QDs requires an in‐depth understanding. In this study, DFT and COSMOSL simulations are employed to investigate the effect of sulfur atoms on ZnO. Based on the simulations, thiol ligands (specifically 2‐hydroxy‐1‐ethanethiol) to modify the ZnO nanocrystals are adopted. This modification alleviates the excess electrons without causing any additional issues in the charge injection in QLEDs. This modification strategy proves to be effective in improving the performance of red‐emitting QLEDs, achieving an external quantum efficiency of over 23% and a remarkably long lifetime T95 of >12 000 h at 1000 cd m−2. Importantly, the relationship between ZnO layers with different electronic properties and their effect on the adjacent QDs through a single QD measurement is investigated. These findings show that the ZnO surface defects and electronic properties can significantly impact the device performance, highlighting the importance of optimizing the ZnO–QD interface, and showcasing a promising ligand strategy for the development of highly efficient QLEDs.
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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".