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Record W2159773049 · doi:10.1002/pen.22060

Comparison between dew‐retted and enzyme‐retted flax fibers as reinforcing material for composites

2011· article· en· W2159773049 on OpenAlexafffundabout
Wei Hu, Minh‐Tan Ton‐That, Johanne Denault, Denis Rho, Yang Jian-zhong, Peter C. K. Lau

Bibliographic record

VenuePolymer Engineering and Science · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsBiotechnology Research InstituteNational Research Council Canada
FundersNational Research Council CanadaAgriculture and Agri-Food Canada
KeywordsRettingMaterials scienceUltimate tensile strengthComposite materialDewFiberPolypropyleneThermal stabilityScanning electron microscopeCrystallinityChemical engineering

Abstract

fetched live from OpenAlex

Abstract Two kinds of retted Canadian linseed flax fibers, dew‐retted (F1) and enzyme‐retted flax fibers (F2) were characterized in detail for their applications in composites, such as retting degree, thermal stability, tensile strength, and interfacial behavior in polypropylene (PP) matrix. It's clear from Scanning Electron Micrograph that the aspect ratio of F2 was much higher than that of F1 in the light of their separated elementary fibers in most cases. Instead, the elementary fibers of F1 remained tightly bundled into technical fiber wrapping with more non‐cellulose portions. This reflected its lower retting degree and resulted in its lower thermal stability. Single fiber tensile test and single fiber pull‐out test were used to evaluate the fiber tensile properties and fiber/PP interfacial shear strength, respectively. Better retting degree and fewer damages on F2 endowed F2 better tensile property. Consequently, higher aspect ratio, retting degree, and tensile strength proved F2 to be a kind of better reinforcing material than F1 for composites. POLYM. ENG. SCI., 2012. 2011 published by 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 score0.017
Threshold uncertainty score0.904

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 communication0.0000.001
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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

Citations12
Published2011
Admission routes3
Has abstractyes

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