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Record W3198612285 · doi:10.1002/pol.20210503

Molecular design strategies for <scp>high‐performance</scp> organic electrochemical transistors

2021· article· en· W3198612285 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Polymer Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsBioelectronicsTransistorTransconductanceMaterials scienceSemiconductorNanotechnologyElectrochemistryElectrolyteOrganic semiconductorMolecular engineeringMaterial DesignDesign elements and principlesBiosensorChemistryElectrodeOptoelectronicsElectrical engineeringEngineeringVoltageSystems engineering

Abstract

fetched live from OpenAlex

Abstract Organic electrochemical transistors (OECTs) utilize ion flow from the electrolyte to modulate the electrical conductivity of the whole bulk organic semiconductor channel. With the characteristic of mixed ionic‐electronic conducting in the entire volume, OECTs exhibit high transconductance and act as good transducers, particularly in bioelectronics. To gain high‐performance OECTs, developing novel high‐performance polymeric semiconductors is important. In this article, operation principles, performance evaluations, and polymerization methods are first discussed. We then analyze the molecular design strategies for high‐performance OECT materials and highlight the characteristics and effects of backbone design and side chain engineering. Finally, we discuss some neglected and unsolved issues and provide an outlook for the OECTs research and development.

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.

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)0.001
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 score0.096
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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