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Record W4401616513 · doi:10.1021/acsmacrolett.4c00397

Highly Transmissive, Processable, Highly Conducting and Stable Polythiophene Derivatives via Direct (Hetero)arylation Polymerization

2024· article· en· W4401616513 on OpenAlexafffund
Catherine Beaumont, Thomas Lemieux, Stefania Aivali, Mona Hamidzad Sangachin, Akpeko Gasonoo, Tristan Marcoux St-Pierre, Marissa Bélanger, Serge Beaupré, Gregory C. Welch, Mario Leclerc

Bibliographic record

VenueACS Macro Letters · 2024
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of CalgaryUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolythiopheneMaterials scienceIndium tin oxideTransmittanceFabricationPolymerizationElectrical conductorCoatingConductive polymerOxideTransparent conducting filmTin oxideOptoelectronicsSheet resistanceElectrodeDopingNanotechnologyComposite materialThin filmPolymerLayer (electronics)

Abstract

fetched live from OpenAlex

The development of modern optoelectronic devices increases the need for lightweight and flexible transparent conductors. It is thus essential to develop new eco-friendly materials that can be easily processed for the fabrication of such devices. For this purpose, the synthesis of self-doped, highly conducting, transmissive, and water-processable polythiophene derivatives was performed via the direct heteroarylation polymerization method and a protection/deprotection strategy. Stable conductivities up to 1000 S cm –1 have been obtained. Champion materials ( P1 and P7 ) were scaled and processed via the roll-to-roll compatible slot-die coating method to demonstrate their large area applicability. The best coated films exhibited optical transmittance greater than 79% at 550 nm with sheet resistances of 116 Ω □ –1 . These values are comparable to indium–tin oxide on plastic and thus present a viable alternative to metal oxide-based electrodes.

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 score0.018
Threshold uncertainty score0.873

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 communication0.0000.001
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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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

Citations11
Published2024
Admission routes2
Has abstractyes

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