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Record W3158734040 · doi:10.6000/1929-5995.2021.10.2

Influence of Hybridization on Mechanical Properties and Water Absorption in Jute and Jute /E-Glass Reinforced Composites

2021· article· en· W3158734040 on OpenAlexvenueno aff
Vitor Lacerda, Ana C. Conceição, Catarina da N. Scherer, Michel G. Santos, Mirtânia Antunes Leão

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialAbsorption of waterComposite numberUltimate tensile strengthFlexural strengthGlass fiberShear (geology)Moisture

Abstract

fetched live from OpenAlex

The use of composite materials is increasingly present not only in several industrial areas but also in daily life. With the escalating environmental concern, it is necessary the search for materials that prove to be ecologically sustainable and economically viable. Vegetable fibers have been presenting themselves as an excellent alternative, however, because their disadvantage of having a high moisture absorption capacity, the introduction of synthetic fibers, forming hybrid composites, is a good solution since it combines the characteristics of both types of reinforcements. The present research aims to carry out a comparative study between two types of laminated polymer composites, one reinforced with Jute fibers and the other hybrid, composed of Jute and E-glass fibers. The mechanical performances of the composites submitted to the Uniaxial Tensile, Three-point Flexural and Shear tests were evaluated, as well as fracture analyzes. A comparative study to analyze the influence of hybridization on moisture absorption was carried out. The results showed that the hybridized composite showed an increase in the elastic modulus, as well as in its resistance to flexion and shear, which shows the formation of a more rigid material. Hybridization also changed the moisture content of the composite.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
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 head0.030
GPT teacher head0.310
Teacher spread0.280 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Research Updates in Polymer ScienceSame topicNatural Fiber Reinforced CompositesFrench-language works237,207