Kenaf-Coir based hybrid nano-composite: an analytical and representative volume element analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".