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Record W4382066041 · doi:10.1177/00219983231186208

Effect of cellulose and lignin content on the mechanical properties and drop-weight impact damage of injection-molded polypropylene-flax and -pine fiber composites

2023· article· en· W4382066041 on OpenAlexafffund
Khaled Nasri, Éric Loranger, Lotfi Toubal

Bibliographic record

VenueJournal of Composite Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialHemicelluloseLigninUltimate tensile strengthPolypropyleneCelluloseComposite numberStiffnessDrop (telecommunication)Izod impact strength testFiber

Abstract

fetched live from OpenAlex

Designing bio-composites for structural applications requires a thorough understanding of their mechanical behavior. In this study, we examined the differences in the tensile strength and drop-weight impact response between polypropylene reinforced with flax fibers and that reinforced with pinewood short fibers, as both fibers differ in composition (cellulose, hemicellulose, and lignin) and length-to-diameter ratio. We found that flax fibers, which have higher cellulose content and are twice as long as pine fibers, increased the stiffness and shock resistance of bio-composite materials. However, pine fibers, which contain more lignin, showed increased material ductility and energy absorption. Impulse excitation, acoustic emission and micro-CT techniques were used to evaluate the post-impact mechanical properties and the contribution of each damage mechanism to the final material failure (tearing). The experimental results were used to validate a model based on finite elements. Our results revealed that the experimental and finite-element analyses were in good agreement.

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 score0.008
Threshold uncertainty score0.617

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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

Citations31
Published2023
Admission routes2
Has abstractyes

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