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Record W2973847434 · doi:10.1021/acsami.9b09857

Polymer Encapsulants for Threshold Voltage Control in Carbon Nanotube Transistors

2019· article· en· W2973847434 on OpenAlexafffund
François Lapointe, Ashish Sapkota, Jianfu Ding, J. Lefebvre

Bibliographic record

VenueACS Applied Materials & Interfaces · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsNational Research Council Canada
FundersNational Research Council CanadaSunchon National University
KeywordsMaterials scienceCarbon nanotubeTransistorPolymerNanotubeNanotechnologyFabricationRectificationDiodeCarbon nanotube field-effect transistorOptoelectronicsField-effect transistorVoltageComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

Although carbon nanotube transistors present outstanding performances based on key metrics, large-scale uniformity and repeatability required in printable electronics depend greatly on proper control of the electrostatic environment. Through a survey of polymer dielectric encapsulants compatible with printing processes, a simple correlation is found between the measured interfacial charge density and the onset of conduction in a transistor, providing a rational route to control the electrical characteristics of carbon nanotube transistors. Smooth and continuous balancing of the properties between unipolar p-type and n-type transport is achieved using a molar fraction series of poly(styrene- co -2-vinylpyridine) statistical copolymers combined with an electron-donating molecule. We further demonstrate the easy fabrication of a p–n diode which shows a modest rectification of 8:1.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.008
GPT teacher head0.225
Teacher spread0.218 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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Same venueACS Applied Materials & InterfacesSame topicCarbon Nanotubes in CompositesFrench-language works237,207