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Record W2020080882 · doi:10.1063/1.2041817

Transient quantum yield evolution of organic light-emitting devices submitted to low-level excitation: A photon counting investigation

2005· article· en· W2020080882 on OpenAlexaff
Normand Beaudoin, Serge Gauvin

Bibliographic record

VenueApplied Physics Letters · 2005
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsOLEDExcitationQuantum yieldOptoelectronicsTransient (computer programming)Yield (engineering)PhotonMaterials scienceQuantumPhoton countingElectric fieldOpticsComputer scienceNanotechnologyPhysicsQuantum mechanicsFluorescence

Abstract

fetched live from OpenAlex

The continuously growing interest in organic light-emitting devices (OLEDs) requires exhaustive experimental investigations to validate the theoretical models. Here, we propose a solution to the problem of the measurement of quantum yield when devices are probed with low-level signals that minimize perturbations. Our approach is based on the wave form recovery methodology. Under pulsed excitation, our results reveal a considerable increase of the quantum yield throughout the excitation pulse. This enhancement persists for 10 ms. The procedure has the capability to reveal features of the transient behaviors and aging that are blurred when OLEDs are operated with normal driving electric field and current density. This approach may provide a complementary way to a thorough investigation of the electronic processes and aging mechanisms of OLEDs.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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