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Record W2769964017 · doi:10.1038/s41598-018-21531-6

Quantitative Analysis of Charge Distribution in Bi-Emissive layer White Organic Light-Emitting Diodes with Two Fluorescent Dopants

2018· article· en· W2769964017 on OpenAlexaff
Ji Young Kim, Woo Young Kim

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElectroluminescenceOLEDMaterials scienceElectronDiodeOptoelectronicsDopantLight-emitting diodeLayer (electronics)ElectrodeMolecular physicsChemistryDopingPhysicsNanotechnology

Abstract

fetched live from OpenAlex

This work seeks to establish a quantitative method which can estimate the holes and electrons ratio in the emission zones. We fabricated multilayered white organic light-emitting diodes (WOLEDs) with the device structure of ITO/NPB(80 nm)/MADN:BUBD-1(7%)(20 nm)/MADN:DCJTB(0.3%)(20 nm)/TPBi(X nm)/LiF(2 nm)/Al as a case study on the charge recombination distribution in the emissive layer. The result shows a trend in the charge recombination ratio depending on the electron transport layer thickness. We obtained an empirical relationship between electron transport layer thicknesses and emission ratio in EML. In addition, the electroluminescent spectra were analyzed by fitting a Gaussian distribution for the two emissive layers to calculate the intensity ratio of the energy transitions. The arrival time of hole and electrons from each electrode was determined using the thickness and mobility of TPBi as electron transport layer. From these initial results, we derived an empirical mechanism to meet with a linear relationship that can allow us to design custom- made WOLEDs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.190
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
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 head0.015
GPT teacher head0.276
Teacher spread0.261 · 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 teacher head, 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

Citations7
Published2018
Admission routes1
Has abstractyes

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