MétaCan
Menu
Back to cohort
Record W2901934807 · doi:10.1039/c8ra08312f

Synergistic effect of functionalized graphene oxide and carbon nanotube hybrids on mechanical properties of epoxy composites

2018· article· en· W2901934807 on OpenAlex
Zehao Qi, Yefa Tan, Zhongwei Zhang, Li Gao, Cuiping Zhang, Jin Tian

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

VenueRSC Advances · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsImpact
FundersNational Natural Science Foundation of China
KeywordsEpoxyGrapheneCarbon nanotubeOxideComposite materialMaterials scienceComposite numberCarbon fibersNanotechnology

Abstract

fetched live from OpenAlex

esterification reaction and 3D structure hybrids were prepared by combining 1D carbon nanotube (CNT) and 2D functionalized GO through π-stacking interaction. Epoxy composites filled with 3D structure hybrids were fabricated. The results show that functionalized GO effectively improves the dispersibility of CNTs in epoxy matrix due to good compatibility. Excellent mechanical properties were achieved by epoxy composites filled with 3D structure hybrids. The fracture surface analysis indicated improved interfacial interaction between 3D structure hybrids and epoxy matrix, which may due to the covalent bonding formed between the epoxy molecular chain grafted on EGO and the hardener agent during the curing process. In the 3D structure filler network, the mechanisms of crack deflection/bifurcation induced by functionalized GO make the crack path tortuous, which causes the cracks to encounter more CNTs and then promote the mechanisms of CNT fracture and crack bridging, resulting in more energy dissipation. This is the key mechanism for its excellent reinforcing and toughening effects.

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 score0.003
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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