Deep analysis of chemically treated Jute/Kenaf and glass fiber reinforced with SiO2 nanoparticles by utilizing RSM optimization
Bibliographic record
Abstract
• Natural jute/kenaf fibers enhance epoxy composites' flexural, hardness traits. • Silane (5 %-15 %) and SiO 2 (3 %-5 %) optimize fiber composite performance. • ANOVA evaluates silane and SiO 2 's effect on composite properties. • RSM-optimized blend boosts flexural by 26 % and hardness by 28 %. • 5 % SiO 2 , 10 % silane, 20-min dip yields ideal eco-friendly composite. In recent years, eco-friendly materials have gained significant attention, especially in substituting synthetic fibers with natural fibers in epoxy matrices. This study focuses on enhancing the flexural and hardness properties of composites reinforced with natural jute and kenaf fibers. The fibers were treated with different concentrations of silane (5 %, 10 %, and 15 %), varied silane dipping times (10, 20, and 30 min), and different amounts of SiO 2 nanofiller (3 %, 4 %, and 5 % by weight). An analysis of variance (ANOVA) was conducted to understand the impact of these treatment conditions on the composite properties. To optimize the flexural and hardness attributes, the study employed the desirability function (DF) and response surface methodology (RSM). Experimental results closely matched predicted values, affirming the model's accuracy. The study found that silane concentration had a significant effect on the flexural and hardness properties. Through RSM, the ideal treatment was identified as 5 % nanoparticle content, 10 % silane concentration, and a 20-minute silane immersion. These conditions led to a 26 % improvement in flexural strength and a 28 % increase in hardness with microanalysis done by SEM as well as Dynamic mechanical anaylsis (DMA) showcasing the potential of treated natural fiber composites in sustainable material applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".