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Record W4220826003 · doi:10.18280/rcma.320107

Physical, Thermal and Mechanical Characterization of a New Material Composite Based on Fibrous Wood Particles of Date Palm Tree

2022· article· en· W4220826003 on OpenAlexvenueno aff
Tarek Djoudi, Hocine Djemai, Mabrouk Hecini, Ahmida Ferhat

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsComposite materialMaterials scienceComposite numberAgglomerateUltimate tensile strengthFlexural strengthParticle sizeChemistry

Abstract

fetched live from OpenAlex

The objective of this work is to valorize the waste from date palms tree which is often abandoned in the palm groves The aim is to produce a new composite based on this waste that can be used as an alternative material to the conventional insulators. The approach consists in making a composite material from petiole wood (WPC) in different particle sizes (0 to 1, 1 to 3 and 3 to 5) mm. We then characterized the physical, thermal and mechanical properties of this new material (WPC). The results obtained proved the relative anisotropy of the material and the effect of the particle size distribution on these properties. The composites (WPC) had low density in the range (0.16-0.56) g/cm3 and also exhibited low thermal conductivity, in the range (0.109-0.122)W/mK°. These weak properties make it possible to use (WPC) as an effective insulator. These characteristics were quite acceptable in comparison with other thermal insulation materials such as cork agglomerate and traditional wood. The interesting mechanical properties of the new composite (WPC) have been shown by the tensile tests and the three-point flexural tests. These results make it possible to valorize these materials (WPC) for possible industrial applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.049
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.033
GPT teacher head0.259
Teacher spread0.226 · 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.

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

Citations10
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicNatural Fiber Reinforced CompositesFrench-language works237,207