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Record W4285741314 · doi:10.1039/d2cs00139j

Group 16 conjugated polymers based on furan, thiophene, selenophene, and tellurophene

2022· review· en· W4285741314 on OpenAlexafffund
Shuyang Ye, Victor Lotocki, Hao Xu, Dwight S. Seferos

Bibliographic record

VenueChemical Society Reviews · 2022
Typereview
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsThiopheneConjugated systemFuranPolymerizationHeteroatomChemistryPolymerCopolymerPolythiopheneCombinatorial chemistryNanotechnologyMaterials scienceOrganic chemistryConductive polymerRing (chemistry)

Abstract

fetched live from OpenAlex

Five-membered aromatic rings containing Group 16 elements (O, S, Se, and Te), also referred as chalcogenophenes, are ubiquitous building blocks for π-conjugated polymers (CPs). Among these, polythiophenes have been established as a model system to study the interplay between molecular structure, solid-state organization, and electronic performance. The judicious substitution of alternative heteroatoms into polythiophenes is a promising strategy for tuning their properties and improving the performance of derived organic electronic devices, thus leading to the recent abundance of CPs containing furan, selenophene, and tellurophene. In this review, we first discuss the current status of Kumada, Negishi, Murahashi, Suzuki-Miyaura, and direct arylation polymerizations, representing the best routes to access well-defined chalcogenophene-containing homopolymers and copolymers. The self-assembly, optical, solid-state, and electronic properties of these polymers and their influence on device performance are then summarized. In addition, we highlight post-polymerization modifications as effective methods to transform polychalcogenophene backbones or side chains in ways that are unobtainable by direct polymerization. Finally, the major challenges and future outlook in this field are presented.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.245
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations113
Published2022
Admission routes2
Has abstractyes

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