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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 OpenAlex
Fatima Ezzahra Hanana, Denis Rodrigue

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.
fundA Canadian funder is recorded on the work.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.035
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.246
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