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Record W2122001045 · doi:10.1002/pc.22943

<i>In situ</i> thermal reduction of graphene oxide forming epoxy nanocomposites and their dielectric properties

2014· article· en· W2122001045 on OpenAlexaff
Maurizio Toselli, Davide Fabiani, Paolo Mancinelli, M.F. Frechétte, Thomas Heid, Éric David, Andrea Saccani

Bibliographic record

VenuePolymer Composites · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsÉcole de Technologie SupérieureHydro-Québec
Fundersnot available
KeywordsMaterials scienceEpoxyComposite materialGrapheneOxideDielectricNanocompositeGraphiteGraphite oxideThermal conductivityElectrical resistivity and conductivityNanotechnology

Abstract

fetched live from OpenAlex

The electrical properties of epoxy based composites modified by low amounts of graphite oxide, below the conduction threshold, have been investigated. The composites have been prepared without the use of solvents by direct sonication of graphite oxide (GO) powders and of chemically modified and partially reduced GO powders in the based epoxy monomer. Through a mild thermal treatment, in situ reduction of the previously dispersed GO has been obtained directly inside the epoxy resins. The changes in the electrical response of the materials thus obtained have been compared to that of pristine unmodified epoxy resin. Data so far collected underline the possibility to tune the electrical conductivity of the composites within two orders of magnitude and to increase the values of permittivity without significantly worsening dielectric losses. POLYM. COMPOS., 36:294–301, 2015. © 2014 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.225
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2014
Admission routes1
Has abstractyes

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