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Record W2081133060 · doi:10.1063/1.4913835

Electrolyte-gated polymer thin film transistors making use of ionic liquids and ionic liquid-solvent mixtures

2015· article· en· W2081133060 on OpenAlexaff
Jonathan Sayago, Xiang Meng, Francis Quenneville, Shuang Liang, E. Bourbeau, Francesca Soavi, Fabio Cicoira, Clara Santato

Bibliographic record

VenueJournal of Applied Physics · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsIonic liquidElectrolyteTransistorMaterials sciencePropylene carbonateThin-film transistorPolymerSolventOptoelectronicsChemical engineeringVoltageNanotechnologyElectrodeChemistryOrganic chemistryElectrical engineeringPhysical chemistryLayer (electronics)CatalysisComposite material

Abstract

fetched live from OpenAlex

Electrolyte-Gated (EG) transistors, making use of electrolytes as the gating medium, are interesting for their low operation voltage. Furthermore, EG polymer transistors offer the advantage of solution processing, low cost, and mechanical flexibility. Despite the intense research activity in EG transistors, clear guidelines to correlate the properties of the materials used for the transistor channel and electrolytes with the doping effectiveness of the transistor channel are yet to be clearly established. Here, we investigate the use of room temperature ionic liquids (RTILs) based on the [TFSI] anion (namely, [EMIM][TFSI], [BMIM][TFSI], and [PYR14][TFSI]), to gate transistors making use of MEH-PPV as the channel material. Morphological studies of MEH-PPV and RTIL films showed a certain degree of segregation between the two components. All the EG transistors featured clear drain-source current modulations at voltages below 1 V. Polar solvent additives as propylene carbonate were used to improve the transistor response time.

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 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 score0.108
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.238
Teacher spread0.213 · 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.

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

Citations15
Published2015
Admission routes1
Has abstractyes

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