Nickel Oxide Hole Injection Layers for Balanced Charge Injection in Quantum Dot Light‐Emitting Diodes
Bibliographic record
Abstract
Abstract Quantum dot (QD) light‐emitting diodes (QLEDs) are promising for next‐generation displays, but suffer from carrier imbalance arising from lower hole injection compared to electron injection. A defect engineering strategy is reported to tackle transport limitations in nickel oxide‐based inorganic hole‐injection layers (HILs) and find that hole injection is able to enhance in high‐performance InP QLEDs using the newly designed material. Through optoelectronic simulations, how the electronic properties of NiO x affect hole injection efficiency into an InP QD layer, finding that efficient hole injection depends on lowering the hole injection barrier and enhancing the acceptor density of NiO x is explored. Li doping and oxygen enriching are identified as effective strategies to control intrinsic and extrinsic defects in NiO x , thereby increasing acceptor density, as evidenced by density functional theory calculations and experimental validation. With fine‐tuned inorganic HIL, InP QLEDs exhibit a luminance of 45 200 cd m −2 and an external quantum efficiency of 19.9%, surpassing previous inorganic HIL‐based QLEDs. This study provides a path to designing inorganic materials for more efficient and sustainable lighting and display technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".