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Record W2480718862 · doi:10.1177/0967391111019002-308

Integration of Single-Walled Carbon Nanotubes into a Single Component Epoxy Resin and an Industrial Epoxy Resin System

2011· article· en· W2480718862 on OpenAlexafffund
Jingwen Guan, Behnam Ashrafi, Yadienka Martinez‐Rubi, Yunfa Zhang, Christopher T. Kingston, Andrew Johnston, Benoît Simard

Bibliographic record

VenuePolymers and Polymer Composites · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular Sciences
FundersMcGill University
KeywordsEpoxyMaterials scienceComposite materialCarbon nanotubeToughnessFracture toughnessComposite epoxy material

Abstract

fetched live from OpenAlex

Single-walled carbon nanotubes (SWCNT) exhibit amongst the best mechanical, thermal and electrical properties of any known material. With their very high aspect ratios, SWCNT are well-suited to making ultra-light multifunctional structural composites. In this work, covalent chemistry is used to integrate SWCNT into a single component epoxy resin (aerospace grade MY0510) as well as an industrialized epoxy resin system for sporting goods. In particular, reduced SWCNT react directly with epoxide groups to create direct connections to the resin backbone. As the reduction process naturally exfoliates the SWCNT bundles, excellent dispersion is readily obtained. Substantial mechanical property improvements of the modified resin and carbon fibre composites have been observed through well-controlled processing. Their mechanical properties, specifically impact resistance, compression after impact strength and fracture toughness of the modified resin and fibre composites are discussed.

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 score0.001
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)0.0010.000

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 head0.041
GPT teacher head0.240
Teacher spread0.199 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

Explore more

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