MétaCan
Menu
Back to cohort
Record W2059746627 · doi:10.1063/1.3117518

Enhancement of current-voltage characteristics of multilayer organic light emitting diodes by using nanostructured composite films

2009· article· en· W2059746627 on OpenAlexaff
Nguyễn Năng Định, Tran Thi Chung Thuy, Tran Quang Trung, Vo‐Van Truong

Bibliographic record

VenueJournal of Applied Physics · 2009
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsConcordia University
FundersAbdus Salam International Centre for Theoretical Physics
KeywordsMaterials scienceOLEDPEDOT:PSSIndium tin oxideSpin coatingNanocompositeThin filmOptoelectronicsConductive polymerLayer (electronics)ElectroluminescencePolymerChemical engineeringNanotechnologyComposite material

Abstract

fetched live from OpenAlex

With the aim of improving the photonic efficiency of an organic light emitting diode (OLED) and its display duration, both the hole transport layer (HTL) and the emitting layer (EL) were prepared as nanostructured thin films. For the HTL, nanocomposite films were prepared by spin-coating a homogeneous solution of low molecular weight poly(4-styrenesulfonate) (PEDOT-PSS) and surfactant-capped TiO2 nanocrystals onto low resistivity indium tin oxide (ITO) substrates; for the EL, nancrystalline titatium oxide (nc-TiO2)-embedded Poly[2-methoxy-5-(2′-ethyl-hexyloxy)-1,4-phenylene vinylene] (MEH-PPV+nc-TiO2) conjugate polymers were spin-coated onto the HTL. Also, for a shallow contact of Al/LiF/MEH-PPV instead of Al/MEH-PPV a super LiF thin film was deposited onto the EL by vacuum evaporation. The resulting multilayer OLED had the following structure of Al/LiF/MEH-PPV+nc-TiO2/PEDOT-PSS+nc-TiO2/ITO. Characterization of the nanocomposite films showed that both the current-voltage (I-V) characteristics and the photoluminescent properties of the nanocomposite materials were significantly enhanced in comparison with the standard polymers. OLEDs made from these layers would exhibit a large photonic efficiency.

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)0.000
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 score0.008
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

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 head0.018
GPT teacher head0.272
Teacher spread0.254 · 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

Citations33
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicConducting polymers and applicationsFrench-language works237,207