Bibliographic record
Abstract
Organic optoelectronic device behaviour is heavily dependent on interfacial effects due to the device architecture and thickness. Interfaces between the inorganic electrodes and the active organic layers play a defining role in the all of the electronic and stability processes that occur in organic light emitting diodes (OLEDs) and organic solar cells (OPVs). Amongst the many interlayers introduced at these interfaces to improve charge carrier movement and stability, LiF has proven to be the most successful and it is almost ubiquitous in all organic semiconductor devices. Implemented at both top and bottom contact interfaces, doped into the charge transporting layers, and used as encapsulants, LiF has played major roles in device performance and lifetime. This review highlights the use of LiF at both top and bottom contacts in organic optoelectronics, discusses the various mechanisms proposed for the utility of LiF at each interface, and explores its impact on device lifetimes. From examples relating to charge carrier flow, interfacial electronic level modification, and interfacial stability, a comprehensive picture of the role of LiF in organic devices can be formed. This review begins with a brief overview of the role of the interface in OLEDs and OPVs, and the general properties of LiF. Then, it discusses the implementation of LiF at the top contact electrode interface, followed by the bottom substrate contact electrode, examining both performance and degradation effects in both cases.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".