Study of organic light emitting devices with 5,6,11,12-tetraphenylnaphthacene (rubrene)-doped hole transport layer
Bibliographic record
Abstract
Doping the hole transport layer (HTL) of organic light emitting devices (OLEDs) was found to increase device operational stability. To this effect, the role of 5,6,11,12-tetraphenylnaphthacene (rubrene), a widely dopant for HTLs, in increasing OLED stability has been widely investigated. However, significant disagreements between various explanations for the increased stability, ranging from rubrene being a charge injection promoter, to its being a charge trap, still exist. We conducted an in-depth study on the influence of rubrene doping of HTL on device stability. The study was carried out on OLEDs of structure: indium-tin-oxide (ITO) anode/N,N'-di(naphthalene-1-yl)-N,N'-diphenyl-benzidine (NPB) HTL / tris(8-hydroxyquinoline) aluminum (AlQ3) electron transport layer / Mg:Ag cathode, in which different portions of the HTL were doped with rubrene. Compared to undoped devices, stability of OLEDs in which HTL doping was limited to only a thin interfacial layer at either the ITO or AlQ3 interface was essentially the same, whereas, stability of OLEDs in which a substantial portion of the HTL was doped was about an order of magnitude higher, and approached that of devices where the whole HTL was doped. In addition, for a fixed thickness of the doped portion, device stability was found to be essentially independent of the thickness of the undoped portion. The results demonstrate that increasing OLEDs stability by means of doping the HTL is associated with changes in bulk HTL hole transport properties rather than interfacial properties, and is consistent with OLED degradation mechanism based on instability of cationic AlQ3 species.
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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 | 0.000 |
| 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) | 0.000 | 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".