Correlation between exciton-induced degradation of organic/metal interfaces and energy barrier for electron injection at organic/metal interfaces in organic optoelectronic devices
Bibliographic record
Abstract
We study exciton-induced degradation of various organic/metal interfaces in organic optoelectronic devices. The results show that organic/metal interfaces are susceptible to irradiation in general, resulting in a deterioration in charge transport across the interfaces. We find that organic/metal interfaces containing the same organic material but different metals degrade quite differently, where interfaces with metals of high work function are more susceptible to exciton-induced degradation than those with metals of low work function. The results suggest a clear correlation between excitoninduced degradation of organic/metal interfaces and energy barrier for electron injection at organic/metal interfaces. Furthermore, the fact that the use of interfacial layers, which usually contain alkali metals of extremely low work function, can greatly improve organic/metal interfacial photo-stability is also, to a large extent, consistent with such correlation. The reason behind such correlation may stem from the difference in the strength of organic-metal bonds in organometallic compounds formed at different organic/metal interfaces and/or the difference in band bending of organic materials in the vicinity of organic/metal interfaces due to the use of metals of different work functions at the interfaces.
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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 | 0.000 | 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".