Investigation of hardness, microstructure and anti-corrosion properties of Zn-ZnO composite coating doped unripe plantain peel particles
Bibliographic record
Abstract
The use of mild steel for several engineering applications despite its vulnerability to degradation on exposure to environmental contaminants has called for the incessant search for durable materials that can reliably protect its surface. This paper examined the hardness, microstructure and anti-corrosion properties of Zn-ZnO composite coating doped with unripe plantain peel (UPP) particles. The hardness of the coatings was examined using the Brinell hardness technique, while the anti-corrosion properties were studied employing potentiodynamic polarization technique, using 3.65% NaCl solution (simulated seawater) as the test medium. The microstructure properties were investigated using SEM/EDS and XRD. The results of the experiment show that the as-received mild steel exhibited the hardness and corrosion rate of 136.8 kgf/mm2 and 8.6272 mm/year, respectively, while the Zn-ZnO coated mild steel exhibited hardness and corrosion rate of 254.6 kgf/mm2 and 3.0954 mm/year, respectively. The optimal performing Zn-ZnO-UPP coated mild steel exhibited a hardness and corrosion rate of 260.3 kgf/mm2 and 1.5290 mm/year, respectively. This indicated that the UPP particles further enhanced the strengthening (binding force at the steel/coating interface) and the passivating tendency of Zn-ZnO coating. More so, the SEM images revealed that the Zn-ZnO-UPP coating exhibited a more refined microstructure than the Zn-ZnO coating, indicating the grain refining ability of the UPP nanoparticles. The XRD profile of the coatings exhibited high intensities, indicating good texture, high stability, chemical and microstructural homogeneity. These improvements in properties indicated that the Zn-ZnO-UPP coating can be used for the protection of mild steel components in marine environments.
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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) | 0.000 | 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".