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Record W2048203363 · doi:10.1039/c3nr33727h

Carbon nanotube electrodes in organic transistors

2013· review· en· W2048203363 on OpenAlexaff
Irina Valitova, Michele Amato, Farzaneh Mahvash, Giovanni Cantele, Antonio Maffucci, Clara Santato, Richard Martel, Fabio Cicoira

Bibliographic record

VenueNanoscale · 2013
Typereview
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsCarbon nanotubeMaterials scienceElectrodeNanotechnologyCarbon nanotube field-effect transistorTransistorCarbon nanotube quantum dotNanotubeThin-film transistorOrganic semiconductorField-effect transistorScope (computer science)Carbon fibersOptoelectronicsComputer scienceElectrical engineeringChemistryVoltageLayer (electronics)Composite materialEngineeringComposite number

Abstract

fetched live from OpenAlex

The scope of this Minireview is to provide an overview of the recent progress on carbon nanotube electrodes applied to organic thin film transistors. After an introduction on the general aspects of the charge injection processes at various electrode-semiconductor interfaces, we discuss the great potential of carbon nanotube electrodes for organic thin film transistors and the recent achievements in the field.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations37
Published2013
Admission routes1
Has abstractyes

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