Degradation Mechanisms in Organic Light-Emitting Diodes with Polyethylenimine as a Solution-Processed Electron Injection Layer
Bibliographic record
Abstract
In this work, we investigate the performance and operational stability of solution-processed organic light-emitting diodes (OLEDs), which comprise polyethylenimine (PEI) as an electron injection layer (EIL). We show that the primary degradation mechanism in these OLEDs depends on the cathode metal that is used in contact with the EIL. In the case of Al, the deterioration in OLED performance during electrical driving is mainly caused by excitons which reach and subsequently degrade the emitter/PEI interface. In contrast, in the case of Ag, device performance degradation occurs due to an additional mechanism: hole accumulation at the emitter/PEI interface and a consequent drop in the emitter quantum yield. As a result, the operational lifetime of OLEDs that use PEI as EIL can vary significantly with the cathode material, and at a current density of 20 mA cm –2, LT50 lifetimes of ∼200 h and <10 h are obtained for Al and Ag, respectively. Finally, we show that the first degradation mechanism can be significantly slowed by using a mixture of PEI and ZnO nanoparticles as EIL. As a result, the operational lifetime of OLEDs with an Al cathode is increased to more than 1000 h, without adversely affecting device performance. This lifetime is significantly longer than that of a LiF/Al reference OLED.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".