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Record W4321033385 · doi:10.1021/acs.macromol.2c02248

Toward Defect Suppression in Polythiophenes Synthesized by Direct (Hetero)Arylation Polymerization

2023· article· en· W4321033385 on OpenAlexafffund
Samuel Brassard, Mona Hamidzad Sangachin, Mario Leclerc

Bibliographic record

VenueMacromolecules · 2023
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolymerMonomerPolymer chemistryPolymerizationSize-exclusion chromatographyChemistryDifferential scanning calorimetryDiamineBranching (polymer chemistry)SolventMoietyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we have investigated different reaction conditions for defect suppression in polythiophene derivatives synthesized by direct (hetero)arylation polymerization (DHAP). The well-known PQT12 polymer was used as a model due to its simple structure which facilitated analysis. 2-Bromo-3,4′-didodecyl-2:2′,5′:2″,5″:2‴-quaterthiophene and 2-bromo-3″,4‴-didodecyl-2:2′,5′:2″,5″-2‴-quaterthiophene monomers allowed the analysis of both homocoupling defects and β-branching defects due to their asymmetric structure as well as the effect of β-protection. The properties of the resulting polymers were analyzed using size-exclusion chromatography, differential scanning calorimetry, UV–visible absorption spectroscopy, and 1 H NMR spectrometry. Some model compounds were synthesized to help with end-groups analysis, revealing debromination as the main obstacle to chain growth. The highest quality polymer was obtained when using toluene as a solvent, Pd 2 dba 3 as a palladium(0) catalyst, neo -decanoic acid (NDA) as a carboxylic acid additive, and a dual-ligand system with diamine TMEDA and phosphine P( o -OMePh) 3 . This study also revealed the importance of the choice of the monomers for the preparation of well-defined conjugated polymers.

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 score0.003
Threshold uncertainty score0.541

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 head0.020
GPT teacher head0.260
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

Citations14
Published2023
Admission routes2
Has abstractyes

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