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Record W2089296391 · doi:10.1002/app.30177

Multiple percolation in a carbon‐filled polymer composites via foaming

2009· article· en· W2089296391 on OpenAlexaff
Michael R. Thompson, Ghodratollah Hashemi Motlagh, K. J. Oxby, Andrew N. Hrymak

Bibliographic record

VenueJournal of Applied Polymer Science · 2009
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Foaming and Composites
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceComposite materialCarbon blackComposite numberPercolation (cognitive psychology)Percolation thresholdElectrical resistivity and conductivityConductivityElectrical conductorPolymerFiberCarbon fibersCopolymerChemistry

Abstract

fetched live from OpenAlex

Abstract This article describes an investigation into the effects of foaming on the electrical conductivity for a carbon‐filled cyclic olefin copolymer (COC) composite incorporating both chopped carbon fibers (cCF) and carbon black (CB). Foamed and solid samples were injection molded and then analyzed for cell size, fiber length, fiber orientation, and electrical conductivity. Foamed samples exhibited higher electrical conductivity in the through‐plane direction for materials containing only CB or composites containing both filler types, and reduced electrical conductivity in the cCF‐filled composites. The increased electrical property gained by foaming was attributed to multiple percolation with CB aggregates forming more effective conductive clusters and networks in the continuous polymer phase during growth of the gas domains. A mechanism for the phenomenon was proposed based on these experimental observations. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

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)0.000
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 score0.009
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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

Citations27
Published2009
Admission routes1
Has abstractyes

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