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Record W2029867590 · doi:10.1063/1.4918497

Evaluation of mechanical properties and durability performance of HDPE-wood composites

2015· article· en· W2029867590 on OpenAlexaff
M. Tazi, Fouad Erchiqui, Hamid Kaddami, Mohamed Bouazara, B. Poaty

Bibliographic record

VenueAIP conference proceedings · 2015
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité du Québec à ChicoutimiUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsMaterials scienceComposite materialUltimate tensile strengthHigh-density polyethyleneAbsorption of waterComposite numberSawdustPolymerCrystallinityThermoplasticPolyethylene

Abstract

fetched live from OpenAlex

The objective of this work is to evaluate the mechanical properties and durability performance of bio-composite materials made from sawdust and thermoplastic polymer (HDPE). For the preparation of the composites, sawdust in different proportions with Maleic Anhydride grafted Polyethylene (MAPE) as the coupling agent was used. The thermal and mechanical properties were successively characterized. The results indicate that adding wood fillers to a polymer matrix increases the degree of crystallinity and improves the tensile strength and ductility of composites. On the contrary, resistance to water absorption decreases as a function of the wood fillers. Scanning electron microscopy (SEM) was used to analyze morphological structure alteration when exposed to intense weathering. The biodegradability of bio-composites up to 97 days was also investigated; the results indicate that, by increasing the filler content, the amount of weight loss increased as well. In other words, even though the addition of sawdust to thermoplastic polymer improves the mechanical performance of a composite material, it also accelerates the biodegradation rate of the composite. An optimum amount of filler content might compromise the effect of biodegradation and mechanical properties of composite materials

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)0.002
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 score0.063
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.082
GPT teacher head0.273
Teacher spread0.191 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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