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Record W2922399591 · doi:10.1002/adom.201801612

Construction of High‐Quality Cu(I) Complex‐Based WOLEDs with Dual Emissive Layers Achieved by an “On‐and‐Off” Deposition Strategy

2019· article· en· W2922399591 on OpenAlexaff
Jiayi Li, Xiaoyue Li, Yu Tan, Xiao Yu, Fanglong Yuan, Zhiwei Liu, Zuqiang Bian, Qiong‐Hua Jin, Zheng‐Hong Lu, Chunhui Huang

Bibliographic record

VenueAdvanced Optical Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversity of Toronto
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Beijing Municipality
KeywordsOLEDMaterials scienceQuantum efficiencyCarbazoleOptoelectronicsQuinolineElectroluminescenceLayer (electronics)PhotochemistryChemistryNanotechnology

Abstract

fetched live from OpenAlex

Abstract Inexpensive and highly luminescent Cu(I) complexes exhibit great potential as emitters in organic light‐emitting diodes (OLEDs), especially in white OLEDs (WOLEDs). In this work, two compounds 9‐(3‐(quinolin‐4‐yl)phenyl)‐9H‐carbazole (CzPQ) and 9,9′‐(quinoline‐4,6‐diylbis(3,1‐phenylene))bis(9H‐carbazole) (2CzPQ) are designed and synthesized to form Cu(I) complexes in situ by codeposition with copper iodide (CuI). The corresponding OLEDs show an orange red emission with a maximum external quantum efficiency (EQE) of 6.7%. Based on this result, an “on‐and‐off” strategy is proposed to achieve WOLEDs with the combination of Cu(I) complex layer and the pure ligand layer by controlling the shutter of CuI source on and off. The optimized WOLEDs give a maximum EQE of 2.4% with excellent Commission Internationale de L'Eclairage (CIE) coordinates of (0.32, 0.33) and a remarkable color‐rendering index (CRI) of 94. It can be forecasted that the device efficiency would be improved by tuning the chemical structure of the ligand.

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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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
Published2019
Admission routes1
Has abstractyes

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