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Record W4253080510 · doi:10.26434/chemrxiv.9946931

Interlayer Engineering of Flexible and Large Area Red Organic Light Emitting Diodes Based on a N-Annulated Perylene Diimide Dimer

2019· preprint· en· W4253080510 on OpenAlexaff
Mohammad Rahmati, Majid Pahlevani, Gregory C. Welch

Bibliographic record

VenueChemRxiv · 2019
Typepreprint
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMaterials scienceOLEDPerylenePEDOT:PSSDiimideLayer (electronics)Surface roughnessDiodeLight-emitting diodeElectrodeOptoelectronicsAnalytical Chemistry (journal)NanotechnologyOpticsChemistryFluorescenceComposite material

Abstract

fetched live from OpenAlex

Flexible red OLEDs based on a quadruple layer stack in-between electrodes with 160 mm2 active area were fabricated in ambient air on PET via slot-die coating. For the OLED structure PET/ITO/PEDOT:PSS/PVK/PFO:tPDI2N-EH/ZnO/Ag the ink formulations and coating parameters for each layer were systematically evaluated and optimized. The air-stable red-light emitting material tPDI2N-EH was successfully utilized as blended homogeneous film with PFO for the emitting layer. The use of an organic hole-transport layer (PVK) and inorganic electron injection layer (ZnO) significantly improved the brightness of the reference device from 4 cd/m2 to 303 cd/m2. Surface analysis using AFM measurements showed that PVK interlayer reduced the surface roughness of the hole injection layer (PEDT:PSS) from 0.45 nm to 0.17 nm, which improved the ability to form uniform emitting layers on top. In addition, the ZnO interlayer improved the average roughness of the device from 1.26 nm to 0.85 nm and reduced the turn-on voltage of the device from 5.0 V to 2.8 V.

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 score0.000
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)0.0000.000

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 head0.227
Teacher spread0.215 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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