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Record W1986355791 · doi:10.1021/am200523z

Toughening of Epoxy Matrices with Reduced Single-Walled Carbon Nanotubes

2011· article· en· W1986355791 on OpenAlexaff
Yadienka Martinez‐Rubi, Behnam Ashrafi, Jingwen Guan, Christopher T. Kingston, Andrew Johnston, Benoît Simard, Vahid Mirjalili, Pascal Hubert, Libo Deng, Robert J. Young

Bibliographic record

VenueACS Applied Materials & Interfaces · 2011
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMcGill UniversityNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceEpoxyComposite materialCarbon nanotubeToughnessUltimate tensile strengthRaman spectroscopyTougheningDynamic mechanical analysisComposite numberFracture toughnessCovalent bondPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Reduced single-walled carbon nanotubes (r-SWCNT) are shown to react readily at room temperature under inert atmosphere conditions with epoxide moieties, such as those in triglycidyl p-amino phenol (TGAP), to produce a soft covalently bonded interface around the SWCNT. The soft interface is compatible with the SWCNT-free cross-linked cured matrix and acts as a toughener for the composite. Incorporation of 0.2 wt % r-SWCNT enhances the ultimate tensile strength, toughness and fracture toughness by 32, 118, and 40%, respectively, without change in modulus. A toughening rate (dK(IC)/dwt(f)) of 200 MPa m(0.5) is obtained. The toughening mechanism is elucidated through dynamic mechanical analyses, Raman spectroscopy and imaging, and stress-strain curve analyses. The method is scalable and applicable to epoxy resins and systems used commercially.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0020.000

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 head0.023
GPT teacher head0.220
Teacher spread0.197 · 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

Citations81
Published2011
Admission routes1
Has abstractyes

Explore more

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