Bibliographic record
Abstract
After efficiency, lifetime is the second most important parameter for organic devices. Interfaces play a major role in that lifetime. This article reviews the current state of the art with regards to interfacial stability and control of electrode/active layer interfaces to understand the performance of organic optoelectronic devices. From examples relating to interfacial chemical reactions, interfacial morphological changes, and interfacial electronic level modification, a comprehensive picture of the role of the organic–electrode interfaces in device stability can be formed. The review begins with a brief overview of degradation in organic devices, including definitions and measurement approaches. It is then broken into two sections. The first focuses on the bottom contact (substrate) interface, specifically chemical reactions and dewetting, as the two main mechanisms of device degradation. The second section examines the top contact interface, which is prone to oxidation, interdiffusion, blistering and delamination, and inhomogeneous loss of performance (dark spots). For both sections, various approaches to overcoming device instabilities are given, with special attention to the various interlayers that have been introduced into devices for improved stability. Each section also includes examples where the main degradation mechanism is used advantageously to produce novel device architectures and surprising solutions to device degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".