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Record W2900499786 · doi:10.25071/10315/35411

Graphene Oxide Reinforced Bio-Epoxy Polymers

2018· article· en· W2900499786 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

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsEpoxyGrapheneOxidePolymerMaterials scienceComposite materialPolymer scienceNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

The majority of epoxy/graphene oxide (GO) composite studies have been conducted on synthetic epoxies. This work presents the results on GO filler loadings of 0.1, 0.2 and 0.3 wt. % to a green bio-epoxy polymer. GO was synthesized from oxidation of graphite flakes. The epoxy/GO composites were prepared using a solution mixing route. Scanning electron microscopy (SEM) was used to examine the graphite and GO powder morphology and composite fractured surfaces. Fourier transform infrared (FTIR) spectroscopy was used to identify functional groups on the produced GO material. Tensile strength of pure and modified bio-epoxy composites was evaluated. SEM showed differences in fractured surfaces which implies the GO material was able to modify the bio-epoxy polymer. The FTIR results confirmed oxidation of the graphite was successful. The tensile strength and modulus improved by 23 % and 35 %, respectively as compared to the pure bio-epoxy with only 0.3 wt. % GO filler. Additions of GO to bio-epoxy revealed a significant enhancement in tensile strength and stiffness could be achieved with considerable lower filler loadings than traditional fillers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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