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Record W3212976916 · doi:10.1063/5.0068537

Rheological, electrical, and dynamic thermomechanical properties: Comparison between multiwall and double-wall carbon nanotubes in polylactide and polyamide 11

2021· article· en· W3212976916 on OpenAlexafffund
Zeinab Mousavi, Marie‐Claude Heuzey, Musa R. Kamal, Emmanuel Flahaut, Pierre J. Carreau

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMcGill UniversityPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbon nanotubeNanocompositePolyamidePercolation thresholdMaterials scienceRheologyComposite materialDynamic mechanical analysisPercolation (cognitive psychology)PolymerElectrical resistivity and conductivity

Abstract

fetched live from OpenAlex

In this study, multiwall carbon nanotubes (MWCNTs) and double-wall carbon nanotubes (DWCNTs) have been dispersed in polylactide (PLA) and polyamide 11 (PA11) using an internal mixer. Rheological characterization confirmed the formation of carbon nanotube (CNT) networks in PLA and PA11 attributed to well-dispersed CNTs in the respective matrices. A lower rheological percolation threshold of PLA/MWCNT nanocomposites (less than 0.5 wt. %) compared to PA11/MWCNT nanocomposites (about 2 wt. %) confirmed the greater affinity of CNTs for PLA. The threshold for DWCNTs was below 0.5 wt. % in PLA and between 0.5 and 1 wt. % in PA11. PLA-based nanocomposites also showed higher electrical conductivity values compared to PA11-based nanocomposites. Nanocomposites containing DWCNTs exhibited higher electrical conductivities compared to those containing MWCNTs due to the higher aspect ratio of DWCNTs. Dynamic mechanical thermal analysis showed enhanced storage modulus values and reduced damping behavior with increasing content of CNTs for both polymers.

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)0.000
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 score0.031
Threshold uncertainty score0.885

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 studies0.0000.000
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 head0.028
GPT teacher head0.273
Teacher spread0.245 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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