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Record W2084081697 · doi:10.1002/pc.21128

The effect of electric field parameters on the resistivity and induced percolation time of carbon black‐filled polystyrene composites

2011· article· en· W2084081697 on OpenAlexaff
Xiaohu Yan, Marianna Kontopoulou, Aristides Docoslis

Bibliographic record

VenuePolymer Composites · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsQueen's University
Fundersnot available
KeywordsMaterials scienceComposite materialPercolation thresholdCarbon blackElectric fieldPercolation (cognitive psychology)PolystyreneComposite numberElectrical resistivity and conductivityPolymerElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The present work examines the influence of externally applied alternating current (AC) electric fields on the formation of electrically conducting structures in carbon black/polystyrene (CB/PS) composites. It is shown that AC electric fields are capable of producing electrically conducting composites at filler concentrations substantially lower than the composite's percolation threshold by causing filler attraction and alignment into columnar structures with orientation normal to the electrode surface. The times required for the insulator‐to‐conductor transition (termed “percolation times”) in these CB/thermoplastic composites are measured as a function of important process parameters, such as applied voltage, electric field frequency, and filler concentration. The percolation times are shown to be inversely proportional to both the square root of the applied field intensity and to the power of 0.3 of the filler concentration, while they appear to be independent of AC field frequencies in the range 10 Hz to 10 KHz. These empirical scaling laws can be used as a guide for the selection of experimental conditions in future studies on electrified composites or for the selection of processing parameters in composites manufacturing. POLYM. COMPOS., 2011. © 2011 Society of Plastics Engineers

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 score0.021
Threshold uncertainty score0.358

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 science0.0000.000
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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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