Atomic Layer Deposition Stabilizes Nanocrystals, Enabling Reliably High‐Performance Quantum Dot LEDs
Bibliographic record
Abstract
Abstract Quantum dot light‐emitting diodes (QD‐LEDs) with stable high efficiencies are crucial for next‐generation displays. However, uncontrollable aging, where efficiency initially increases during storage (positive aging) but is entirely lost upon extended aging (negative aging), hinders further device development. It is uncovered that it is chemical changes to nanocrystal (NC)‐based electron transport layer (ETL) that give rise to positive aging, their drift in structure and morphology leading to transiently improved charge injection balance. Using grazing‐incidence small‐angle X‐ray scattering, it is found that ZnMgO NCs undergo size‐focusing ripening during aging, improving size uniformity and creating a smoother energy landscape. Electron‐only device measurements reveal a sevenfold reduction in trap states, indicating enhanced surface passivation of ZnMgO. These insights, combined with density functional theory calculations of ZnMgO surface binding, inspire an atomic layer deposition (ALD) strategy with Al₂O₃ to permanently suppress surface traps and inhibit NC growth, effectively eliminating aging‐induced efficiency loss. This ALD‐engineered ZnMgO ETL enables reproducible external quantum efficiencies (EQEs) of 17% across 30 batches of LEDs with a T60 of 60 h at an initial luminance of 4500 cd m−2, representing a 1.6‐fold increase in EQE and a tenfold improvement in operating stability compared to control devices.
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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".