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Thermal Conductivity of Carbon Nanofiber Reinforced Polymer Composites

2004· article· en· W2744561069 on OpenAlexaff
Shu Fujiwara, Kazuki ENOMOTO, Toshiyuki Yasuhara, Naoto Ohtake, Hiroya Murakami, Junichi TERAKI

Bibliographic record

VenueThe proceedings of the JSME annual meeting · 2004
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsComposite materialMaterials scienceThermal conductivityCarbon nanofiberPolypropyleneCarbon nanotubePolymerMolding (decorative)NanofiberConductivityFiller (materials)Carbon fibersComposite numberChemistry

Abstract

fetched live from OpenAlex

Carbon nanofibers(CNFs) and Carbon nanotubes(CNTs) are expected to be fillers of polymer matrix composites. We have measured thermal conductivity of polypropylene(PP) matrix composites filled with vapor grown carbon fibers (VGCFs) and vaper grown carbon nanofibers(VGNFs). Specimens were fabricated by injection molding with changing the concentration of the fillers in the composites from 0 to 50wt%. Thermal conductivity increased with increasing the filler concentration and the value of 3.46W/mK was obtained when VGCF concentration was 50wt% with highly oriented along measuring direction of thermal conductivity. This value is approximately seventeen fold higher than that of the pure PP(0.20W/mK).

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 score0.025
Threshold uncertainty score0.748

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 science0.0010.000
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 head0.188
Teacher spread0.182 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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