High-Performance Flexible Organic Light-Emitting Diodes Using Embedded Silver Network Transparent Electrodes
Bibliographic record
Abstract
Because of their mechanical flexibility, organic light-emitting diodes (OLEDs) hold great promise as a leading technology for display and lighting applications in wearable electronics. The development of flexible OLEDs requires high-quality transparent conductive electrodes with superior bendability and roll-to-roll manufacturing compatibility to replace indium tin oxide (ITO) anodes. Here, we present a flexible transparent conductor on plastic with embedded silver networks which is used to achieve flexible, highly power-efficient large-area green and white OLEDs. By combining an improved outcoupling structure for simultaneously extracting light in waveguide and substrate modes and reducing the surface plasmonic losses, flexible white OLEDs exhibit a power efficiency of 106 lm W(-1) at 1000 cd m(-2) with angular color stability, which is significantly higher than all other reports of flexible white OLEDs. These results represent an exciting step toward the realization of ITO-free, high-efficiency OLEDs for use in a wide variety of high-performance flexible applications.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".