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Record W2037106435 · doi:10.1103/physrevb.62.11405

Microstructural studies of organic light-emitting devices by Monte Carlo simulation of two-dimensional triangles

2000· article· en· W2037106435 on OpenAlexaff
Siew-Yen Cheng, Jian‐Sheng Wang, Gu Xu

Bibliographic record

VenuePhysical review. B, Condensed matter · 2000
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAmorphous solidMaterials scienceMonte Carlo methodImpurityOLEDAnnealing (glass)Relaxation (psychology)CrystallizationMolecular physicsCondensed matter physicsCrystallographyOpticsChemical physicsPhysicsLayer (electronics)NanotechnologyThermodynamicsComposite materialChemistry

Abstract

fetched live from OpenAlex

The fast degradation of organic light-emitting devices (OLEDs) remains as the main obstacle to the commercialization of OLEDs. Among the failure mechanisms proposed, crystallization of the amorphous ${\mathrm{Alq}}_{3}$ film that leads to the quenching of electroluminescence plays a crucial role and is little understood. Because in situ studies of the ${\mathrm{Alq}}_{3}$ layer, with the probing of thin organic film by, for example, x-ray diffraction, are very difficult, if not impossible, Monte Carlo simulation is therefore conducted. The molecular motion of ${\mathrm{Alq}}_{3}$ is simulated by two-dimensional triangles which interacted with a square-well potential in an isothermal-isobaric ensemble. Simulated results show the structural relaxation of ${\mathrm{Alq}}_{3}$ from amorphous to crystalline upon thermal annealing. To impede this ordering process, quenched impurities of various shapes were added to the system. It is found that impurities of circular shape and of low aspect ratio have relatively the highest disordering effect. This is in good agreement with the experimental results. In addition, the preferred geometry of the ${\mathrm{Alq}}_{3}$ system was also examined.

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 score0.017
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.326
Teacher spread0.309 · 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
Published2000
Admission routes1
Has abstractyes

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