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Electric field induced alignment of multiwalled carbon nanotubes in polymers and multiscale composites

2015· article· en· W2068477711 on OpenAlexafffund
Maxime Arguin, Frédéric Sirois, Daniel Therriault

Bibliographic record

VenueAdvanced Manufacturing Polymer & Composites Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceElectrical resistivity and conductivityComposite materialElectric fieldNanocompositeCarbon nanotubeEpoxyElectrical conductorComposite numberPolymerFabricationAnisotropyElectrical engineering

Abstract

fetched live from OpenAlex

Carbon fiber reinforced polymers (CFRPs) have a highly anisotropic electrical resistivity, which limits their use in electrical applications. In this contribution, an electric field was used to align multiwalled carbon nanotubes (MWCNTs) to create preferential conductive pathways within a nanocomposite and a multiscale composite in order to reduce their resistivity. Investigation on epoxy containing MWCNTs have shown that an electric field of 40 V mm−1 or higher applied for 2 h can lead to a reduction of the resistivity parallel to the field up to four orders of magnitude with only 0·01 wt-% loading. In the case of CFRPs reinforced with 0·01 and 0·1 wt-% of MWCNTs, we observed reductions of the through the thickness resistivity of 36 and 99% respectively, when an electric field of 60 V mm−1 was applied for 2 h during the fabrication of the samples.

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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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