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Record W4401256309 · doi:10.1039/d4sc03649b

Triplet dynamics reveal loss pathways in multi-resonance thermally activated delayed fluorescence emitters

2024· article· en· W4401256309 on OpenAlexafffund
Alexandra N. Stuart, Katrina Bergmann, Inseong Cho, William J. Kendrick, Zachary M. Hudson, Wallace W. H. Wong, Girish Lakhwani

Bibliographic record

VenueChemical Science · 2024
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversity of British Columbia
FundersCentre of Excellence in Exciton ScienceNatural Sciences and Engineering Research Council of CanadaAustralian Research CouncilAlliance de recherche numérique du CanadaKillam Trusts
KeywordsDynamics (music)FluorescenceResonance (particle physics)ChemistryPhotochemistryNuclear magnetic resonanceChemical physicsPhysicsAtomic physicsOptics

Abstract

fetched live from OpenAlex

, using transient absorption spectroscopy to spectrally resolve the triplet population(s). We identify multiple triplet populations with distinct spectral contributions, and resolve the dynamics between them. Unlike conventional donor-acceptor TADF materials that have previously been studied, we find these triplet states are not formed in equilibrium, instead exhibiting a slow evolution from a high-energy triplet to a low-energy triplet. Delayed fluorescence predominantly reflects the lifetime of the high-energy triplet state, indicating that the formation of the low-energy triplet is a loss pathway for TADF. We also find that greater amounts of the low-energy triplet are formed in a higher dielectric environment, which leads to less delayed fluorescence. These triplet dynamics have significant implications for TADF in devices, as depending on the identity of the triplet formed by electrical excitation, there will either be a significant barrier to TADF, or a competing nonradiative decay pathway.

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 score0.320
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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