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Record W3159831447 · doi:10.1155/2021/6630408

Effect of Temperature on the Viscoelastic Properties of Carbon Nanotube Reinforced Polypropylene Composites

2021· article· en· W3159831447 on OpenAlexaff
Jun Huang, Denis Rodrigue, Ling Dong

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité Laval
FundersScience and Technology Plan Project of Wenzhou, ChinaWenzhou Municipal Science and Technology BureauNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceCarbon nanotubePolypropyleneComposite materialViscoelasticityNanocompositeVolume fractionStress relaxationUltimate tensile strengthFinite element methodCreepStructural engineering

Abstract

fetched live from OpenAlex

Finite element method (FEM) is used to analyze the mechanical properties of carbon nanotubes (CNTs) reinforced polypropylene (PP) composites. Firstly, polypropylene is assumed as a viscoelastic material, while carbon nanotubes are assumed as linear elastic materials to study the effect of temperature on the mechanical properties of neat PP and CNT/PP nanocomposites. Secondly, to compare the viscoelastic properties of neat PP and CNT/PP nanocomposites, the relaxation time at a specific temperature is used to investigate the relaxation of the nanocomposites for fixed tensile displacements. Thirdly, the effect of CNT volume fraction on the viscoelastic properties of nanocomposites is studied at different temperatures. Finally, to better understand the stress distribution along the CNT axial direction, a single carbon nanotube is isolated in the matrix to compare the stress distribution with nonisolated CNTs.

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)0.001
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 score0.010
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.211
Teacher spread0.207 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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