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Record W2063165535 · doi:10.1002/app.41807

Development and characterization of flax fiber reinforced biocomposite using flaxseed oil‐based bio‐resin

2014· article· en· W2063165535 on OpenAlexafffund
Richard W. Evitts

Bibliographic record

VenueJournal of Applied Polymer Science · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsBiocompositeFlexural strengthMaterials scienceComposite materialUltimate tensile strengthFlexural modulusFiberMolding (decorative)Absorption of waterIzod impact strength testStyreneYoung's modulusNatural fiberPolymerComposite numberCopolymer

Abstract

fetched live from OpenAlex

ABSTRACT In this study, acrylated epoxidized flaxseed oil (AEFO) resin is synthesized from flaxseed oil, and flax fiber reinforced AEFO biocomposites is produced via a vacuum‐assisted resin transfer molding technique. Different amounts of flax fiber and styrene are added to the resin to improve its mechanical and physical properties. Both flax fiber and styrene improve the mechanical properties of these biocomposites, but the flexural strength decreases with an increase in styrene content. The mass increase during water absorption testing is less than 1.5% (w/w) for all of the AEFO‐based biocomposites. The density of the AEFO resin is 1.166 g/cm3, which increases to 1.191 g/cm3 when reinforced with 10% (w/w) flax fiber. The flax fiber reinforced AEFO‐based biocomposites have a maximum tensile strength of 31.4 ± 1.2 MPa and Young's modulus of 520 ± 31 MPa. These biocomposites also have a maximum flexural strength of 64.5 ± 2.3 MPa and a flexural modulus of 2.98 ± 0.12 GPa. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41807.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2014
Admission routes2
Has abstractyes

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