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Record W4389789955 · doi:10.1177/07316844231220708

Long-term water aging of composites with bamboo fiber and montmorillonite filler

2023· article· en· W4389789955 on OpenAlexaff
Mouad Chakkour, Mohamed Ould Moussa, Ismail Khay, M. Ballı, Tarak Ben Zineb

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceComposite materialAbsorption of waterUltimate tensile strengthMontmorilloniteComposite numberSwellingScanning electron microscopeFiber

Abstract

fetched live from OpenAlex

This article examines the degradation mechanisms and mechanical properties of unfilled and montmorillonite-filled bamboo fiber-composites, under long-term water aging (up to 120 days) at room temperature. The main findings show that the water absorption and hydrolysis processes simultaneously occur during aging. The presence of clay fillers (3 wt %) reduces the water absorption content of composites from 18.69% to 15.68% after 120 aging days. However, their moisture absorption rate increases after adding particles. Scanning electron microscope images confirm the development of structural damage induced by the extra-swelling of particles, enabling more water absorption after long-term aging. Interestingly, the unfilled composites lose about 18.7% and 17.94% of their initial tensile strength and modulus after 120 aging days, which is attributed to the swelling/plasticization mechanisms and the chemical degradation at the interface, respectively. However, the tensile strength and modulus of montmorillonite-filled composites drop by 35.78% and 32.47%, respectively. This poor resilience to aging is attributed to the development of microstructural damage. The results of this work provide a clear insight into the long-term mechanical properties of composite materials and shed light on their systematic aging mechanisms, with a view to their application in harsh environments.

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)0.000
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 score0.007
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.221
Teacher spread0.212 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

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