Boosting the performance of red PHOLEDs by exciton harvesting
Bibliographic record
Abstract
Significant development has been made on phosphorescent organic light emitting diodes (PHOLEDs) over the past decade, which eventually resulted in the commercialization of widely distributed active-matrix organic light emitting diode displays for mobile phones. However, higher efficiency PHOLEDs are still needed to further reduce the cost and lower the power consumption for general lighting and LED backlight applications. In particular, red PHOLEDs currently have in general the lowest efficiencies among the three primary colors, due most likely to the energy-gap law. Therefore, a number of groups have of made use of various device configurations, including insertion of a carrier blocking or exciton confining layer, doping the transport layers, as well as employing multiple emissive zone structures to improve the device efficiency. However, these approaches are rather inconvenient for commercial applications. In this work, we have developed a simpler way to boost the performance of red PHOLEDs by incorporating an exciton harvesting green emitter, which transfers a large portion of the energy to the co-deposited red emitter. A high external quantum efficiency (EQE) of 20.6% was achieved, which is among the best performances for red PHOLEDs.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".