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Record W2023983892 · doi:10.1039/c2sc21740f

New opportunities for organic electronics and bioelectronics: ions in action

2012· article· en· W2023983892 on OpenAlexafffund
Giuseppe Tarabella, Farzaneh Mohammadi, Nicola Coppedé, Francesco Barbero, Salvatore Iannotta, Clara Santato, Fabio Cicoira

Bibliographic record

VenueChemical Science · 2012
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioelectronicsOrganic electronicsTransistorElectrolyteElectronicsNanotechnologyMaterials scienceOrganic semiconductorMolecular electronicsElectrochemistryIonIonic bondingChemistryMoleculeOptoelectronicsElectrical engineeringBiosensorElectrodeVoltageEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

This perspective deals with the coupling of ionic and electronic transport in organic electronic devices, focusing on electrolyte-gated transistors. These include electrolyte-gated organic field-effect transistors (EG-OFETs) and organic electrochemical transistors (OECTs). EG-OFETs, based on molecules and polymers, can be operated at low electrical bias (about 1 V or below) and permit unprecedented charge carrier densities within the transistor channel. OECTs can be operated in aqueous environment as efficient ion-to-electron converters, thus providing an interface between the worlds of biology and electronics. The exploration and the exploitation of coupled ionic and electronic transport in organic materials brings together different disciplines such as materials science, physics, chemistry, electrochemistry, organic electronics and biology.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.007
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.087
GPT teacher head0.318
Teacher spread0.231 · 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

Citations152
Published2012
Admission routes2
Has abstractyes

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