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Record W2095661431 · doi:10.1002/chem.200701717

Thiophene Polymer Semiconductors for Organic Thin‐Film Transistors

2008· article· en· W2095661431 on OpenAlexaff
Beng S. Ong, Yiliang Wu, Yuning Li, Ping Liu, Hualong Pan

Bibliographic record

VenueChemistry - A European Journal · 2008
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsXerox (Canada)
Fundersnot available
KeywordsMaterials scienceThin-film transistorTransistorSemiconductorOrganic semiconductorStackingOptoelectronicsThiopheneElectronicsField-effect transistorPolymerSiliconOrganic electronicsNanotechnologyFlexible electronicsElectrical engineeringOrganic chemistryChemistryComposite materialLayer (electronics)Engineering

Abstract

fetched live from OpenAlex

Printed organic thin-film transistors (OTFTs) have received great interests as potentially low-cost alternative to silicon technology for application in large-area, flexible, and ultra-low-cost electronics. One of the critical materials for TFTs is semiconductor, which has a dominant impact on the transistor properties. We review here the structural studies and design of thiophene-based polymer semiconductors with respect to solution processability, ambient stability, molecular self-organization, and field-effect transistor properties for OTFT applications. We show that through judicial monomer design, delicately controlled pi-conjugation, and strategically positioned pendant side-chain distribution, novel solution-processable thiophene polymer semiconductors with excellent self-organization ability to form extended lamellar pi-stacking orders can be developed. OTFTs using semiconductors of this nature processed in ambient conditions have provided excellent field-effect transistor properties.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.011
GPT teacher head0.178
Teacher spread0.167 · 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 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

Citations289
Published2008
Admission routes1
Has abstractyes

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Same venueChemistry - A European JournalSame topicOrganic Electronics and PhotovoltaicsFrench-language works237,207