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A review of electrical and thermal conductivities of epoxy resin systems reinforced with carbon nanotubes and graphene-based nanoparticles

2022· review· en· W4281689931 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.

Bibliographic record

VenuePolymer Testing · 2022
Typereview
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceEpoxyCarbon nanotubeGrapheneNanocompositeComposite materialThermalDispersion (optics)Thermal conductivityNanoparticleSurface modificationShrinkageAdhesiveCarbon fibersNanotechnologyComposite numberChemical engineering

Abstract

fetched live from OpenAlex

Epoxy (EP) resins exhibit desirable mechanical and thermal properties, low shrinkage during cuing, and high chemical resistance. Therefore, they are useful for various applications, such as coatings, adhesives, paints, etc. On the other hand, carbon nanotubes (CNT), graphene (Gr), and their derivatives have become reinforcements of choice for EP-based nanocomposites because of their extraordinary mechanical, thermal, and electrical properties. Herein, we provide an overview of the last decade's advances in research on improving the thermal and electrical conductivities of EP resin systems modified with CNT, Gr, their derivatives, and hybrids. We further report on the surface modification of these reinforcements as a means to improve the nanofiller dispersion in the EP resins, thereby enhancing the thermal and electrical conductivities of the resulting nanocomposites.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.177
Threshold uncertainty score0.624

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.075
GPT teacher head0.308
Teacher spread0.233 · 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