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Record W2121963373 · doi:10.1177/0731684415588938

Natural fiber reinforced polyester composites: A literature review

2015· review· en· W2121963373 on OpenAlexaff
Shayesteh Haghdan, Gregory D. Smith

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2015
Typereview
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceThermosetting polymerComposite materialSynthetic fiberNatural fiberPolyesterComposite numberFiberPolymerGlass fiber

Abstract

fetched live from OpenAlex

Many composite products are made of thermosetting polymers reinforced with synthetic fibers. Despite the high mechanical properties associated with these fibers they are heavy and expensive compared with natural fibers. The use of natural plant fibres, combinations of natural and synthetic fibers, and wood furnish as reinforcement in polyester matrix for making low cost engineering materials has generated much interest recently. Natural fibers with good specific stiffness and strength, low density, low embodied energy, and good biodegradability have an advantage over synthetic fibers. Despite these benefits they have poor compatibility with the matrix due to their hydrophilic nature. This paper reviews the literature on the effects of chemical treatments on fiber–matrix interfacial adhesion and the wettability of natural fibers by polyester. The efficiency of incorporating glass fiber into the natural fiber for the purpose of reducing water uptake and increasing the stiffness of composite is also discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.287
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations74
Published2015
Admission routes1
Has abstractyes

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