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Record W2396824454 · doi:10.1021/acsami.6b02946

Influence of the Guest on Aggregation of the Host by Exciton–Polaron Interactions and Its Effects on the Stability of Phosphorescent Organic Light-Emitting Devices

2016· article· en· W2396824454 on OpenAlexafffund
Yingjie Zhang, Hany Aziz

Bibliographic record

VenueACS Applied Materials & Interfaces · 2016
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhosphorescenceMaterials scienceElectroluminescencePhosphorescent organic light-emitting diodeExcitonPolaronChemical physicsOptoelectronicsCommon emitterHost (biology)Aggregation-induced emissionOLEDPhase (matter)NanotechnologyOpticsChemistryPhysicsCondensed matter physicsOrganic chemistry

Abstract

fetched live from OpenAlex

The root causes of the differences in electroluminescence stability among phosphorescent organic light-emitting devices (PHOLEDs) utilizing different emitter guests are studied. The results show that the host material plays a more influential role in limiting device stability in comparison to the guest. During the operation of a PHOLED, the host undergoes aggregation as a result of interactions between the excitons and positive polarons. The rate of this aggregation is found to be the limiting factor for device lifetime and is influenced by the choice of the guest material and its concentration. Finally, it is shown that phase segregation between the host and the guest is an important aspect of the aggregation process. As a result of this segregation, energy transfer from the host to the guest becomes increasingly less efficient, resulting in the observed gradual loss in electroluminescence efficiency in the devices over time. The findings explain why PHOLEDs utilizing different guest materials but otherwise identical material systems can have significantly different lifetimes and provide an answer to a long-lasting question in the field.

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 score0.005
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.007
GPT teacher head0.213
Teacher spread0.206 · 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

Citations43
Published2016
Admission routes2
Has abstractyes

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