Enhancing Mechanical and Physical Properties of Epoxy Composites with Eco-Friendly Metakaolin Filler: An Experimental Study
Bibliographic record
Abstract
This paper focuses on the preparation and characterization of the reinforcement of an epoxy with metakaolin clay of grain size (45 ≤ d ≤60) μm as an eco-friendly inorganic material to improve its mechanical and physical properties with low-cost filler.Various proportions of (0, 15, 30, 45, and 60) wt.% of metakaolin were intrinsically mixed with epoxy to invistigate their physical properties represented by moisture absorption.Tensile test was employed to determine their mechanical properties such as ultimate tensile strength, yield strength, modulus of elasticity, as well as compressive strength, flexural strength and Vickers hardness test which was used to specify hardness number.The obtained experimental results showed improvement in the modulus of elasticity, compressive strength, and hardness with the addition of metakaolin.In contrast, the yield and tensile strength of the composite specimens which decrease with the increase in metakaolin content.Also, absorption of moisture test at different immersion times was carried out with different filler loadings of the prepared composite.The results of the test showed that a decreasing in water uptake for specimens with higher metakaolin content.From optical surface investigation using an optical microscope, it was found a reduction in porosity with an increase in the concentration of metakaolin and the specimen with weight percent of 60% is the highest homogeneity and less porosity and voids as compared to the other specimens.The prepared composite may find potential practical applications such as anti-corrosion coating, automotive components and medical devices.
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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".