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Record W4251678728 · doi:10.1002/anie.201914458

Approaching the Integer‐Charge Transfer Regime in Molecularly Doped Oligothiophenes by Efficient Decarboxylative Cross‐Coupling

2020· article· en· W4251678728 on OpenAlexafffund
Jiang Tian Liu, Hannes Hase, Sarah Taylor, Ingo Salzmann, Pat Forgione

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsMcGill UniversityCentre in Green Chemistry and CatalysisConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCentre in Green Chemistry and Catalysis
KeywordsThiopheneMaterials scienceDopingCharge (physics)Organic semiconductorOrganic electronicsCoupling (piping)Modularity (biology)Computational chemistryChemical physicsCombinatorial chemistryPhotochemistryOptoelectronicsChemistryOrganic chemistryVoltagePhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract A library of symmetrical linear oligothiophene was prepared employing decarboxylative cross‐coupling reaction as the key transformation. Thiophene potassium carboxylate salts were used as cross‐coupling partners without the need of co‐catalyst, base, or additives. This method demonstrates complete chemoselectivity and is a comprehensive greener approach compared to the existing methods. The modularity of this approach is demonstrated with the preparation of discreet oligothiophenes with up to 10 thiophene repeat units. Symmetrical oligothiophenes are prototypical organic semiconductors where their molecular electrical doping as a function of the chain length can be assessed spectroscopically. An oligothiophene critical length for integer charge transfer was observed to be 10 thiophene units, highlighting the potential use of discrete oligothiophenes as doped conduction or injection layers in organic electronics applications.

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 score0.064
Threshold uncertainty score0.468

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 head0.024
GPT teacher head0.271
Teacher spread0.247 · 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

Citations27
Published2020
Admission routes2
Has abstractyes

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