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Record W2044604590 · doi:10.1088/0957-4484/17/6/003

Effective amino-functionalization of carbon nanotubes for reinforcing epoxy polymer composites

2006· article· en· W2044604590 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNanotechnology · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceCarbon nanotubeSurface modificationEpoxyNanocompositeComposite materialRaman spectroscopyPolymerCuring (chemistry)Composite numberDifferential scanning calorimetryFourier transform infrared spectroscopyChemical engineering

Abstract

fetched live from OpenAlex

An effective functionalization method was investigated to take full advantage of the exceptional performance of both carbon nanotubes and epoxy polymer for composite application. Epoxy polymer curing agent, EPI-W, was grafted to the single-walled carbon nanotubes through diazotization. Fourier transformed infrared spectroscopy, Raman spectroscopy, differential scanning calorimetry, dynamical mechanical analysis and thermo-gravimetric analysis were performed to characterize the functionalization effect. The degree of functionalization was estimated to be 1 in 50 carbons in the nanotube framework. The elastic modulus of the nanocomposite was enhanced 24.6% with only 0.5 wt% loading of functionalized carbon nanotubes, in contrast to the 3.2% increase of un-functionalized carbon nanotube reinforced composite. This significant improvement suggested an effective way to realize an industrial application of nanotubes reinforcing epoxy composite.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.038
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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