Corrosion and Surface Modification of Hybridized Seashell Composite on AA6063 Alloy for Advance Application
Bibliographic record
Abstract
This study focuses on utilizing abundantly available seashell ash by dispersing it in AA6063 alloy to form composites. A new aluminium metal matrix composite was developed by reinforcing AA 6063 alloy with particles of seashell ash as reinforcement materials in varying percentages (0, 10, 20, 30, 40 wt.%). The samples were prepared by using the stir casting method. The microstructural characterizations were carried out using optical microscopy and the scanning electron microscope (SEM), revealing particle shape and size descriptions for the composite microstructural features. The results indicate that the composite materials exhibit relatively larger and more uniformly distributed grain sizes compared to the base material. The outcomes demonstrate a significant improvement in the tensile strength and hardness of the composites, accompanied by a decrease in corrosion rates. The best samples display a 90.95% increase in tensile strength, a 38.01% increase in hardness and a 71.5% decrease in corrosion rate in an HCl environment, along with a 46.7% decrease in a NaCl environment. Furthermore, there is a 207.7% increase in impact strength in the sample reinforced with 20 wt.% of seashell ash. Overall, the AA-SS composite with 20% wt. seashell ash exhibits the most favourable properties, featuring a 90.95% increase in tensile strength, a 38.01% increase in hardness and a lower corrosion rate when compared to the control sample.
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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".