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Record W2079853251 · doi:10.1116/1.582232

Selective doping of multilayer organic light emitting devices

2000· article· en· W2079853251 on OpenAlexaff
Jacky W. Y. Lam, T. C. Gorjanc, Ye Tao, M. D’lorio

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2000
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsInstitute for Microstructural SciencesUniversity of Ottawa
Fundersnot available
KeywordsOLEDMaterials scienceElectroluminescenceDopingOptoelectronicsDopantIndium tin oxideLayer (electronics)ExcitonIndiumQuantum efficiencyDiodeNanotechnology

Abstract

fetched live from OpenAlex

Doping of organic light emitting diodes (OLEDs) has well-established benefits such as tuning emission wavelengths, as well as enhancing device lifetime and quantum efficiency. The use of low percentage doping within the emissive layer is an established tool for the study of energy transfer processes in OLEDs, allowing one to trace electron and hole movement and exciton formation. Delta doping, in which a thin layer of the dopant material alone is incorporated in the device, has the added advantage of a thinner sensing layer and an electroluminescence spectrum distinct from that of the host material. In an ongoing effort to further our understanding of electroluminescent emission processes, we have fabricated multilayer OLEDs which incorporate a narrow (<1 nm) delta-doped DCM region within the emissive layer. The devices studied were deposited on indium tin oxide (ITO) on glass substrates using thermal evaporation, with a structure of ITO, TPD (40 nm)/selectively doped Alq3 (40 nm)/Al (100 nm). The relationship between the doping profile and the emission characteristics will be discussed.

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)0.001
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 score0.017
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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

Citations16
Published2000
Admission routes1
Has abstractyes

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