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Record W4407199524 · doi:10.1177/08927057251319772

Influence of fiber concentration and length on the dielectric, mechanical, and thermal properties of maple wood fiber-reinforced polypropylene

2025· article· en· W4407199524 on OpenAlexafffund
Farshid Basiji, Fouad Erchiqui, Ahmed Koubaa, Ismaeil Ghasemi

Bibliographic record

VenueJournal of Thermoplastic Composite Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersFondation de l’Université du Québec en Abitibi-Témiscamingue
KeywordsMaterials scienceComposite materialMaplePolypropyleneFiberDielectricThermal

Abstract

fetched live from OpenAlex

Natural fiber-reinforced polymer composites are gaining popularity due to their sustainability and enhanced properties compared to pure polymers. Recently, these composites have been utilized as dielectric materials. In this study, polypropylene (PP) reinforced with maple wood fibers of different lengths (50, 75, and 100 µm) and various fiber concentrations (5%, 10%, 15%, and 20%) were examined. The effects of fiber length and concentration on the dielectric, mechanical, and thermal properties were investigated. All composites exhibited a higher dielectric constant and conductivity than pure PP, along with a lower loss factor. This suggests that adding maple wood fibers generally enhances the dielectric properties. As fiber concentration increases, the dielectric constant tends to rise while the loss factor tends to decrease. TGA results indicated that adding more fibers reduces thermal stability at low temperatures but increases stability at high temperatures. Mechanical testing revealed an increase in strength but a decrease in elongation. However, fiber length did not significantly impact the mechanical properties. SEM analysis showed a uniform distribution of fibers at 5% weight that improves strength, while a 20% weight leads to clustering that weakens the composite.

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)0.001
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 score0.002
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations6
Published2025
Admission routes2
Has abstractyes

Explore more

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