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Record W1990014966 · doi:10.1177/0021998307076489

On the Electrical Conductivity of Particulate Composites

2007· article· en· W1990014966 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Composite Materials · 2007
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceParticulatesComposite materialElectrical resistivity and conductivityVolume fractionPhase (matter)Dispersion (optics)Composite numberFiller (materials)ConductivityMatrix (chemical analysis)EpoxyPhysics

Abstract

fetched live from OpenAlex

Two new equations are developed for effective electrical conductivity of concentrated particulate composites using a differential scheme along with the solution of an infinitely dilute dispersion of particles in a continuous matrix. The proposed equations are evaluated using 16 sets of experimental data on the electrical conductivity of two-phase particulate systems. The following model developed in the paper describes the experimental data very well: (σ/σm)1/3 (σd — σm)/ (σd — σ) = (1 — φ/φm )—αφm ) where σ, σm and σd are electrical conductivities of composite, matrix, and dispersed phase (filler) respectively, φ is volume fraction of filler, φm is the maximum packing volume fraction of filler, and α is a constant of the order of unity. In the special case of α = 1 and φm = 1, this model reduces to the well-known Bruggeman equation for the electrical conductivity of two-phase particulate systems. The predictions of the proposed model are significantly different from the predictions of the existing general effective media (GEM) model.

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)0.001
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 score0.004
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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