Performance enhancement of hybrid kenaf/bamboo fibre-reinforced bio-epoxy composites for sustainable structural applications
Bibliographic record
Abstract
In this study, bamboo (B) and kenaf (K) fibres were employed as reinforcements in bio-epoxy matrices to develop biocomposites, aiming to evaluate their mechanical, thermal, and morphological properties. Biocomposites fabricated by Hand lay-up techniques by using different formulations. TGA results indicated that the biocomposites maintained thermal stability up to 230 °C before decomposition, with complete degradation occurring at 600 °C. The findings demonstrated that hybrid biocomposites exhibited superior mechanical performance; however, the sample (K50) exhibited the greatest tensile strength among all the biocomposites, which was approximately 43 MPa. On the other hand, the 5K/5B biocomposite exhibits the maximum elongation at break, approximately 2 %. The K50 biocomposite showed the highest flexural strength among all samples. DMA analysis revealed an increase in the storage modulus (É) for hybrid biocomposites, indicating improved stiffness and reduced damping. According to the DMA results, the storage modulus values were enhanced compared to those of the single biocomposites, while the loss modulus exhibited lower values with the hybrid biocomposites. Following, the storage modulus increased from 3566.92 MPa (K50) to 3764.14 MPa for the optimised hybrid composite (5K/5B), indicating improved stiffness under load. Likewise, the loss modulus increased from 300.48 MPa (K50) to 360.65 MPa (3K/7B), which confirms improved energy dissipation capability and stronger fibre–matrix interlocking. SEM analysis provided insights into fibre-matrix adhesion and bonding, confirming that the integration of hybrid natural fibres enhances the overall properties of bio-epoxy composites. In contrast, the WA and TS percentages exhibited higher values. Thus, incorporating the kenaf fibres with bamboo as hybrid biocomposites in this study would meet the demand for sustainable packaging, serving as a viable alternative to conventional plastics and enhancing environmental sustainability. From the TMA findings, the results indicate that the 3K/7B biocomposite has the highest CTE value, indicating the most significant thermal expansion among the samples. These combined improvements demonstrate strong potential for use in lightweight automotive structures, building panels, and eco-friendly consumer products where greater strength and thermal resistance are needed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".