Improving the thermal properties of olive/bamboo fiber‐based epoxy hybrid composites
Bibliographic record
Abstract
Abstract In this work, thermal analysis of olive/bamboo fiber‐based epoxy hybrid composites was carried out. Three types of olive fibers, which are olive tree small branch (OTS), olive tree big branch (OTB), and olive tree leaves (OTL), along with bamboo fibers (B), were used to fabricate the composites. Thermal properties of hybrid composites were examined by the thermogravimetric analyzer (TGA), dynamic mechanical analyzer (DMA), and thermomechanical analyzer (TMA). It was found that the thermal stability improved with the incorporation of hybrid fibers in epoxy composites compared to pure fiber composites. Hybrid composite (OTS‐B) exhibited a lower residue (15.82%) whereas hybrid composites (OTB‐B and OTS‐B) show 54.65% and 54.53% weight loss at the maximum decomposition temperature. DMA results showed that the storage modulus and loss modulus reduced with hybrid fiber composites while the damping factor (tan delta) was increased. The storage modulus values of the pure composite sample (B) exhibited a higher increased (3150 MPa). In contrast, the pure composite sample (B) exhibited the highest loss modulus (337 MPa). From TMA analysis, OTL‐B hybrid composite presented a higher T g and lower coefficient of thermal expansion. We concluded that finding from this work will strengthen attracting interpretation of utilization of two different fibers to fabricate hybrid composites for various lightweight purposes in a wide‐ranging choice of industrial applications such as biomedical tools, automobile, and construction fields. Additionally, a novel method can be used to develop hybrid biocomposites materials, which have potential applications in biomaterials and engineering areas.
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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".