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Record W2770400125 · doi:10.3390/polym9120642

Low-Cost Carbon Fillers to Improve Mechanical Properties and Conductivity of Epoxy Composites

2017· article· en· W2770400125 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

VenuePolymers · 2017
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of TorontoOntario Tech University
FundersHorizon 2020 Framework Programme
KeywordsMaterials scienceEpoxyComposite materialBiocharCarbon nanotubeUltimate tensile strengthCarbon blackFiller (materials)Carbon fibersComposite numberPyrolysisChemical engineeringNatural rubber

Abstract

fetched live from OpenAlex

In recent years, low-cost carbons derived from recycled materials have been gaining attention for their potentials as filler in composites and in other applications. The electrical and mechanical properties of polymer composites can be tuned using different percentages and different kind of fillers: either low-cost (e.g., carbon black), ecofriendly (e.g., biochar), or sophisticated (e.g., carbon nanotubes). In this work, the mechanical and electrical behavior of composites with biochar and multiwall carbon nanotubes dispersed in epoxy resin are compared. Superior mechanical properties (ultimate tensile strength, strain at break) were noticed at low heat-treated biochar (concentrations 2⁻4 wt %). Furthermore, dielectric properties in the microwave range comparable to low carbon nanotubes loadings can be achieved by employing larger but manageable amounts of biochar (20 wt %), rending the production of composites for structural and functional application cost-effective.

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)0.000
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 score0.003
Threshold uncertainty score0.588

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 head0.029
GPT teacher head0.259
Teacher spread0.230 · 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