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Record W3030196633 · doi:10.3390/polym12061231

Controlled Synthesis of Poly(pentafluorostyrene-ran-methyl methacrylate) Copolymers by Nitroxide Mediated Polymerization and Their Use as Dielectric Layers in Organic Thin-film Transistors

2020· article· en· W3030196633 on OpenAlexafffund
Alexander J. Peltekoff, Mathieu N. Tousignant, Victoria E. Hiller, Owen A. Melville, Benoît H. Lessard

Bibliographic record

VenuePolymers · 2020
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsMaterials scienceCopolymerDielectricMethyl methacrylateDielectric spectroscopyGate dielectricPolymerizationOrganic field-effect transistorPolymerThin-film transistorThin filmNitroxide mediated radical polymerizationChemical engineeringPolymer chemistryField-effect transistorElectrodeComposite materialTransistorRadical polymerizationOptoelectronicsNanotechnologyLayer (electronics)Chemistry

Abstract

fetched live from OpenAlex

A library of statistically random pentafluorostyrene (PFS) and methyl methacrylate (MMA) copolymers with narrow molecular weight distributions was produced, using nitroxide mediated polymerization (NMP) to study the effect of polymer composition on the performance of bottom-gate top-contact organic thin-film transistors, when utilized as the dielectric medium. Contact angle measurements confirmed the ability to tune the surface properties of copolymer thin films through variation of its PFS/MMA composition, while impedance spectroscopy determined the effect of this variation on dielectric properties. Bottom-gate, top-contact copper phthalocyanine (CuPc) based organic thin-film transistors were fabricated using the random copolymers as a dielectric layer. We found that increasing the PFS content led to increased field-effect mobility, until a point after which the CuPc no longer adhered to the polymer dielectric.

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 categoriesMeta-epidemiology (narrow)
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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.182
Teacher spread0.176 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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