Evaluation of Mechanical, Physical, and Morphological Properties of Epoxy Composites Reinforced with Different Date Palm Fillers
Bibliographic record
Abstract
The present study deals with the fabrication of epoxy composites reinforced with 50 wt% of date palm leaf sheath (G), palm tree trunk (L), fruit bunch stalk (AA), and leaf stalk (A) as filler by the hand lay-up technique. The developed composites were characterized and compared in terms of mechanical, physical and morphological properties. Mechanical tests revealed that the addition of AA improves tensile (20.60–40.12 MPa), impact strength (45.71–99.45 J/m), flexural strength (32.11–110.16 MPa) and density (1.13–1.90 g/cm3). The water absorption and thickness swelling values observed in this study were higher for AA/epoxy composite, revealing its higher cellulosic content, compared to the other composite materials. The examination of fiber pull-out, matrix cracks, and fiber dislocations in the microstructure and fractured surface morphology of the developed materials confirmed the trends for mechanical properties. Overall, from results analysis it can be concluded that reinforcing epoxy matrix with AA filler effectively improves the properties of the developed composite materials. Thus, date palm fruit bunch stalk filler might be considered as a sustainable and green promising reinforcing material similarly to other natural fibers and can be used for diverse commercial, structural, and nonstructural applications requiring high mechanical resistance.
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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".