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Record W2891773321 · doi:10.3390/molecules23092324

Theoretical Calculations for Highly Selective Direct Heteroarylation Polymerization: New Nitrile-Substituted Dithienyl-Diketopyrrolopyrrole-Based Polymers

2018· article· en· W2891773321 on OpenAlexafffund
Thomas Bura, Serge Beaupré, Marc‐André Légaré, Olzhas A. Ibraikulov, Nicolas Leclerc, Mario Leclerc

Bibliographic record

VenueMolecules · 2018
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooCompute CanadaUniversité Laval
KeywordsMoietyPolymerizationNitrilePolymerConjugated systemElectron affinity (data page)Materials scienceMonomerFluorineOrganic electronicsCopolymerChemistryCombinatorial chemistryPhotochemistryPolymer chemistryOrganic chemistryMoleculePhysics

Abstract

fetched live from OpenAlex

Direct Heteroarylation Polymerization (DHAP) is becoming a valuable alternative to classical polymerization methods being used to synthesize π-conjugated polymers for organic electronics applications. In previous work, we showed that theoretical calculations on activation energy (Ea) of the C–H bonds were helpful to rationalize and predict the selectivity of the DHAP. For readers’ convenience, we have gathered in this work all our previous theoretical calculations on Ea and performed new ones. Those theoretical calculations cover now most of the widely utilized electron-rich and electron-poor moieties studied in organic electronics like dithienyl-diketopyrrolopyrrole (DT-DPP) derivatives. Theoretical calculations reported herein show strong modulation of the Ea of C–H bond on DT-DPP when a bromine atom or strong electron withdrawing groups (such as fluorine or nitrile) are added to the thienyl moiety. Based on those theoretical calculations, new cyanated dithienyl-diketopyrrolopyrrole (CNDT-DPP) monomers and copolymers were prepared by DHAP and their electro-optical properties were compared with their non-fluorinated and fluorinated analogues.

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: none
Teacher disagreement score0.683
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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