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Record W4412653278 · doi:10.1021/acsami.5c07394

Viable Regiochemical Control in Semiconducting Polymers for Field-Effect Transistors: From High-Mobility Enhancement toward High Deformability

2025· review· en· W4412653278 on OpenAlexaff
So‐Huei Kang, Sang Myeon Lee, Kyu Cheol Lee, Changduk Yang

Bibliographic record

VenueACS Applied Materials & Interfaces · 2025
Typereview
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsMcGill University
FundersKorea Institute of Energy Technology Evaluation and PlanningNational Research Foundation of Korea
KeywordsMaterials scienceRegioselectivityPolymerStackingIntermolecular forceOrganic electronicsConjugated systemNanotechnologyTransistorField-effect transistorFabricationMoleculeOrganic chemistryChemistryCatalysisComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

The molecular properties of semiconducting polymers with regioisomeric structural factors can be effectively tuned based on their regiochemistry. Thus, the molecular dynamics, intermolecular stacking, and film morphology of these polymers have been tuned via regiochemical control to ultimately enhance their electronic properties in organic field-effect transistors (OFETs). This strategy can also be employed to induce high deformability in organic electronics. In this review, studies on the regiochemistry of conjugated polymers based on the dimensions of regiochemical factors and synthesis methods are summarized to enable the control of the regiochemistry of substituents, side chains, and backbone linkages that can impact their optoelectronic properties and solid-state morphologies. Moreover, technological developments in the fabrication of OFETs with highly enhanced mobility toward stretchable electronics have been discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.250
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes1
Has abstractyes

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