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Record W2434406797 · doi:10.1002/pc.24097

Modification of bamboo fibers/bio‐based epoxy interface by nano‐reinforced coatings

2016· article· en· W2434406797 on OpenAlexaff
Florent Gauvin, Clément Richard, Mathieu Robert

Bibliographic record

VenuePolymer Composites · 2016
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsCégep de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceEpoxyComposite materialFlexural strengthCoatingUltimate tensile strengthFiberContact angleFlexural modulusScanning electron microscopeNanoparticleNanotechnology

Abstract

fetched live from OpenAlex

This study investigates the improvement of the interface between bamboo fibers (BF) and a bio‐based epoxy polymer by coating the surface of BF with nanoparticles. Unidirectional BF bundles were dip‐coated with three different types of coating: bio‐based epoxy, bio‐based epoxy containing silanized silica fume, and bio‐based epoxy containing starch nanoparticles. Scanning electron microscopy and dynamic contact angle tensiometry were performed to characterize the fiber surface. Tensile tests were conducted to study the benefit of the coating on the fiber properties. Bio‐based epoxy/BF composites were molded and both tensile and flexural tests were conducted to study the fiber/matrix interface. Experimental results show that coated BF bundles are more hydrophobic and up to 30% stiffer and resistant than untreated fibers. The nano‐reinforced interface enhances the flexural stress and modulus up to 25% and 20%, respectively, depicting a better fiber/matrix interface. POLYM. COMPOS., 39:1534–1542, 2018. © 2016 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 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 score0.018
Threshold uncertainty score0.913

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 science0.0010.000
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 teacher head, 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

Citations18
Published2016
Admission routes1
Has abstractyes

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