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Record W2946218436 · doi:10.1103/physrevb.99.205201

Triplet exciton diffusion in metalorganic phosphorescent host-guest systems from first principles

2019· article· en· W2946218436 on OpenAlexaff
Xander de Vries, Pascal Friederich, Wolfgang Wenzel, R. Coehoorn, P. A. Bobbert

Bibliographic record

VenuePhysical review. B./Physical review. B · 2019
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversity of Toronto
FundersDeutsche ForschungsgemeinschaftEuropean Commission
KeywordsExcitonPhosphorescenceAb initioKinetic Monte CarloDiffusionMaterials scienceIntramolecular forceMolecular physicsTriplet stateMarcus theoryDensity functional theoryChemical physicsAtomic physicsPhysicsCondensed matter physicsMonte Carlo methodExcited stateChemistryComputational chemistryFluorescenceQuantum mechanics

Abstract

fetched live from OpenAlex

We present an ab initio computational study of triplet exciton diffusion in four phosphorescent emitters commonly used in organic light-emitting diodes (OLEDs). By kinetic Monte Carlo simulations, triplet diffusion lengths are obtained for these emitters in neat films and as a guest in two different hosts. The triplet transfer rates governing the diffusion contain a transfer integral factor that includes both F\"orster and Dexter contributions and a Franck-Condon weighted density of vibrational states that includes the coupling to all intramolecular vibrations in a fully quantum mechanical way. We find that at guest concentrations around 10 mol% the F\"orster transfer contribution is most important. At larger concentrations of about 30--40 mol% the Dexter contribution becomes dominant. We show that obtaining the triplet transfer rates by the semiclassical Marcus theory yields diffusion lengths that are too short and that using a simple cubic lattice in combination with the often used Miller-Abrahams rates instead of using a real morphology with the ab initio rates leads to an underestimation of the diffusion lengths due to transfers down in energy that are too slow.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.006

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 head0.015
GPT teacher head0.304
Teacher spread0.289 · 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.

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

Citations25
Published2019
Admission routes1
Has abstractyes

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