High‐Performance Inverted Perovskite Quantum Dot Light‐Emitting Diodes Enabled by Dual Synergistic Interfacial Passivation
Bibliographic record
Abstract
Abstract Inverted perovskite quantum dot light‐emitting diodes (Pe‐QLEDs) hold significant promise for next‐generation displays due to their compatibility with n ‐type thin‐film transistor‐driven active‐matrix panels, a critical advantage for industrial integration. However, interfacial reactions between the ZnO electron transport layer (ETL) and perovskite quantum dots (Pe‐QDs) induce severe degradation and fluorescence quenching, undermining device efficiency and operational stability. To resolve this, we introduce a dual synergistic interfacial passivation strategy employing pentaerythritol tetrakis(3‐mercaptopropionate) (PETMP) as a multifunctional buffer layer. The PETMP layer addresses interfacial incompatibility through two synergistic mechanisms: (I) Thiol groups in PETMP form robust S─Zn bonds with the ZnO ETL, passivating surface oxygen vacancies, improving film morphology, and reducing the electron injection barrier. (II) Concurrently, these thiols coordinate with undercoordinated Pb ions on the Pe‐QD surface, enhancing luminescence efficiency and suppressing non‐radiative recombination. This dual synergistic passivation strategy yields inverted green Pe‐QLEDs with a record maximum external quantum efficiency (EQE) of 24.35%, doubling the performance of conventional devices using polyvinyl pyrrolidone (PVP) buffers (EQE = 12.61%). Additionally, the optimized interface resulting from this strategy significantly enhances the operational stability of the devices. This work establishes PETMP‐based passivation as a transformative approach for high‐performance inverted Pe‐QLEDs and other optoelectronic 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".