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Record W4308865133 · doi:10.5267/j.esm.2022.8.001

Kenaf-Coir based hybrid nano-composite: an analytical and representative volume element analysis

2022· article· en· W4308865133 on OpenAlexvenueno aff
Shikha Parashar, V. K. Chawla

Bibliographic record

VenueEngineering Solid Mechanics · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialRepresentative elementary volumeKenafComposite numberNanocompositeVolume (thermodynamics)FiberBiocompositeEpoxyVolume fractionElastic modulusMicrostructure

Abstract

fetched live from OpenAlex

The increasing demand for good and improved polymeric composites has led to a surge in the number of researches on hybrid composites, strengthened and enforced with the natural fibres. This paper mainly analyses and presents the attributes of hybrid composites made from natural fibres and carbon nano-tube (CNT) nanoparticles. A novel hybrid composite considered in this research includes kenaf and coir fibres with CNT nanoparticles embedded in an epoxy matrix. The proposed hybrid nanocomposite’s elastic features are calculated by using different analytical models like Chamis, Mori-Tanaka, Nielson elastic models etc and also with the help of Representative Volume Element Analysis (RVE). The content of fibre volume is varied in four different samples and it is found that upon varying the content of fibre volume, the mechanical properties like longitudinal modulus and transverse modulus got affected. The results evaluated from different analytical models are observed to be in good agreement with each other and also with the results of RVE analysis.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.249
Teacher spread0.241 · 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 designSimulation or modeling
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

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