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Record W2742464264 · doi:10.1002/pc.24473

Rotational molding of self‐hybrid composites based on linear low‐density polyethylene and maple fibers

2017· article· en· W2742464264 on OpenAlexafffund
Fatima Ezzahra Hanana, Denis Rodrigue

Bibliographic record

VenuePolymer Composites · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialUltimate tensile strengthMapleLinear low-density polyethyleneFiberPolyethyleneHigh-density polyethyleneModulusMolding (decorative)Young's modulus

Abstract

fetched live from OpenAlex

In this study, the morphological, physical, and mechanical properties of maple fiber self‐hybrid composites reinforced linear low‐density polyethylene (LLDPE) have been investigated for different concentration (10, 20, and 30%) and ratio (100/0, 75/25, 50/50, 25/75, and 0/100) of short (125–250 μm), medium (250–355 μm), and long (355–500 μm) fibers. Maple surface treatment with a coupling agent (maleated polyethylene, MAPE) was also investigated. The results show that surface treatment increased the tensile modulus and strength, and impact strength. Finally, the self‐hybrid composites gave better properties than single size fibers since a positive deviation from the linear law of mixture was observed, especially at 20% wt. For example, a 75/25 ratio of medium/short or long/short fibers produced a tensile modulus and tensile strength between 13% and 33% higher than composites formulated with a single fiber size. POLYM. COMPOS., 39:4094–4103, 2018. © 2017 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 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)0.0010.000

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 head0.011
GPT teacher head0.246
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 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

Citations30
Published2017
Admission routes2
Has abstractyes

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