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Record W3192790039 · doi:10.1002/anie.202107830

Preparation of Patterned and Multilayer Thin Films for Organic Electronics via Oxygen‐Tolerant SI‐PET‐RAFT

2021· article· en· W3192790039 on OpenAlexafffund
Jade Poisson, Alexander M. Polgar, Michele Fromel, Christian W. Pester, Zachary M. Hudson

Bibliographic record

VenueAngewandte Chemie International Edition · 2021
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOLEDMaterials scienceRaftPolymerOptoelectronicsOrganic electronicsLayer (electronics)PhotolithographyNanotechnologyPhotoinduced electron transferThin filmOrganic semiconductorElectron transferPhotochemistryChemistryCopolymerComposite material

Abstract

fetched live from OpenAlex

An oxygen-tolerant approach is described for preparing surface-tethered polymer films of organic semiconductors directly from electrode substrates using polymer brush photolithography. A photoinduced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) approach was used to prepare multiblock polymer architectures with the structures of multi-layer organic light-emitting diodes (OLEDs), including electron-transport, emissive, and hole-transport layers. The preparation of thermally activated delayed fluorescence (TADF) and thermally assisted fluorescence (TAF) trilayer OLED architectures are described. By using direct photomasking as well as a digital micromirror device, we also show that the surface-initiated (SI)-PET-RAFT approach allows for enhanced control over layer thickness, and spatial resolution in polymer brush patterning at low cost.

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 score0.060
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.010
GPT teacher head0.271
Teacher spread0.261 · 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

Citations37
Published2021
Admission routes2
Has abstractyes

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