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Record W2325013464 · doi:10.1021/am201824k

Large-Scale Production of PMMA/SWCNT Composites Based on SWCNT Modified with PMMA

2012· article· en· W2325013464 on OpenAlexaff
R. A. W. Fraser, Karen Stoeffler, Behnam Ashrafi, Yunfa Zhang, Benoît Simard

Bibliographic record

VenueACS Applied Materials & Interfaces · 2012
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsMaterials scienceComposite materialUltimate tensile strengthCarbon nanotubeDifferential scanning calorimetryScanning electron microscopeThermogravimetric analysisIzod impact strength testMethyl methacrylateCompoundingCharpy impact testPolymerizationPolymerChemical engineering

Abstract

fetched live from OpenAlex

In this work, a two-step method consisting of in situ polymerization of polymethyl methacrylate (PMMA) in the presence of single-walled carbon nanotubes (SWCNT), followed by the redispersion of the resulting compound in dimethylformamide (DMF), was used to fabricate SWCNT modified with PMMA (SWCNT-PMMA). Raman spectroscopy revealed that PMMA was merely wrapped around the SWCNT when raw SWCNT or purified SWCNT were used as the starting material. However, PMMA was covalently bonded to SWCNT when acid treated SWCNT (SWCNT-COOH) was used as the starting material. SWCNT-PMMA compounds were further diluted in pure PMMA by conventional melt compounding at large scale (several kilograms) to obtain transparent composites containing 0.09 wt % SWCNT. The micro- and nano-dispersion of the SWCNT in the composites were analyzed using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The thermal and mechanical properties of the composites were determined by thermal gravimetric analysis (TGA), differential scanning calorimetry (DSC), tensile testing, and Charpy impact testing. At the the low SWCNT loading studied, the tensile properties remain unchanged, whereas the impact strength improves by 20%.

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)
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 score0.006
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 head0.011
GPT teacher head0.234
Teacher spread0.223 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

Explore more

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