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Record W2020263051 · doi:10.1021/nl8033152

Carbon Nanotubes as Injection Electrodes for Organic Thin Film Transistors

2009· article· en· W2020263051 on OpenAlexaff
Carla M. Aguirre, Céline Ternon, Matthieu Paillet, P. Desjardins, Richard Martel

Bibliographic record

VenueNano Letters · 2009
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversité de MontréalPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceElectrodeOptoelectronicsCarbon nanotube quantum dotNanotechnologyTransistorNanotubeThin-film transistorField-effect transistorCarbon fibersPotential applications of carbon nanotubesOptical properties of carbon nanotubesComposite materialChemistryVoltageLayer (electronics)Electrical engineeringComposite number

Abstract

fetched live from OpenAlex

We have investigated the charge injection efficiency of carbon nanotube electrodes for organic semiconducting layers and compared their performance to that of traditional noble metal electrodes. Our results reveal that charge injection from a single carbon nanotube electrode is more than an order of magnitude more efficient than charge injection from metal electrodes. Moreover, organic thin film transistors that use arrays of carbon nanotube electrodes display considerable effective mobilities (0.14 cm(2)/(V.s)) and nearly ideal linear output characteristics. These results indicate that carbon nanotubes should be considered a viable alternative to metal electrodes for next-generation organic field-effect transistors.

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 score0.007
Threshold uncertainty score0.766

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 head0.004
GPT teacher head0.185
Teacher spread0.181 · 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

Citations78
Published2009
Admission routes1
Has abstractyes

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