Dimensional Stability, Mechanical and Thermal Performance of Flax/Carbon/Kevlar Reinforced Bio-Phenolic/Epoxy Hybrid Composites
Bibliographic record
Abstract
This study explores the effects of hybridizing flax fabric (F) with carbon/Kevlar fabric (CK) on key material properties, including density, water absorption, void content, tensile properties, impact resistance, and thermogravimetric stability. The composites were fabricated using compression molding, employing different weight ratios of flax to carbon/Kevlar (F/CK): 100/0, 75/25, 50/50, 25/75, and 0/100, while keeping the fiber loading constant at 50 wt%. Bio-phenolic/epoxy polymer blends were used as the polymer matrix. The results indicate that as the amount of carbon/Kevlar fabric increases, the density of the hybrid composites also increases while the moisture absorption decreases. The analysis of void content in the composites suggests that the fabricated composites are well-prepared, with void content measuring less than 2%. Combining flax fiber with carbon/Kevlar fiber produces a composite with improved mechanical characteristics. Notably, the hybrid composite with a ratio of 25:75 (F: CK) demonstrated superior tensile modulus and impact strength, showing improvements of 25.96% and 16.05%, respectively, compared to the carbon/Kevlar composite. Moreover, the residue at 800°C of the composites increased with an increase in the carbon/Kevlar fabric and the highest was shown by hybrid composites with the ratio of 25:75 (F:CK), where the residue is 39.96%.
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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".