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Record W4402186657 · doi:10.32920/26871445

Surface Modification of Reinforcing Fibres for Enhanced Interfacial Properties in Polymer Composites

2024· preprint· en· W4402186657 on OpenAlex
Jada Steer

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

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComposite materialMaterials scienceSurface modificationPolymerSurface (topology)Chemical engineering

Abstract

fetched live from OpenAlex

<p>The mechanical integrity of fibre-reinforced polymer (FRP) composites is dependent on the adhesion of the constituents at the fibre-matrix interface, as it is the primary location for stress transfer and dissipation. Surface modification of synthetic and natural reinforcing fibres allows for the modulation of the interphase region to promote enhanced mechanical properties in FRP composites. The present research aimed to understand the effect of graphene oxide (GO), a two-dimensional nanomaterial, and polyether amine (PEA), an amine-terminated, aliphatic polymer, based fibre surface coatings on the interfacial adhesion of carbon/epoxy and flax/epoxy composites. GO nanomodification, applied via dip coating, improved the interfacial adhesion of carbon/epoxy composites by 28%, while maintaining the inherent carbon fibre mechanical properties. Subsequently, flax/epoxy composites experienced a 30% and 46% improvement in the transverse fibre bundle tensile (TFBT) strength and strain-at-failure, respectively, upon the application of a bi-layer GO/PEA coating, producing a toughening effect within the interphase region.</p>

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 score0.004
Threshold uncertainty score0.576

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 science0.0000.001
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 head0.049
GPT teacher head0.287
Teacher spread0.238 · 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

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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