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Record W1530344605 · doi:10.1002/pssa.201228514

The combination of two p‐doped layers for improving the hole current of organic light‐emitting diodes

2013· article· en· W1530344605 on OpenAlexaff
Lei Chen, Dashan Qin, Yuhuan Chen, Guifang Li, Mingxia Wang, Dayan Ban

Bibliographic record

Venuephysica status solidi (a) · 2013
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversity of Waterloo
FundersNational Science Foundation
KeywordsOLEDOhmic contactDopingMaterials scienceDiodeBiphenylOptoelectronicsHeterojunctionConductivityCurrent densityChemistryNanotechnologyPhysical chemistryLayer (electronics)Physics

Abstract

fetched live from OpenAlex

Abstract The combination of MoO3‐doped 4,4‐N,N‐bis [N‐1‐naphthyl‐N‐phenyl‐amino]biphenyl (NPB:MoO3) and 4,4′‐N,N′‐dicarbazole‐biphenyl (CBP:MoO3) was used to enhance the hole conduction in organic light‐emitting diodes (OLEDs). It is found that the OLED using NPB:MoO3 10 nm/CBP:MoO3 5 nm showed a much increased current density than the one using NPB:MoO3 5 nm/CBP:MoO3 5 nm at a given voltage larger than 4 V, mainly because the hole transport barrier across the p‐doped heterojunction in the former device became smaller than that in the latter device with the driving voltage increasing, despite the fact that the 10‐nm NPB:MoO3 in the former device caused more Ohmic loss than the 5‐nm one in the latter device. As a result of the higher conductivity of NPB:MoO3 than that of CBP:MoO3, the OLED using the combination of 15‐nm NPB:MoO3 and 5‐nm CBP:MoO3 showed significantly increased performance than the one using the single 20‐nm CBP:MoO3. We provide a useful way of advancing the OLEDs toward the practical applications in general lighting and flat‐panel displays.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.269
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2013
Admission routes1
Has abstractyes

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