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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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
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 teacher head, not a consensus.

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

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