Effect of Ni Concentration on the Surface Morphology and Corrosion Behavior of Zn-Ni Alloy Coatings
Bibliographic record
Abstract
This research work aims to develop electrodeposited Zn-Ni alloy coatings with controlled dissolution tendencies on a mild steel substrate. The varying Ni concentration in the electroplating bath, i.e., 10, 15, 20 and 25 g·L−1, affected the surface morphology and electrochemical properties of the deposited Zn-Ni alloy coatings. SEM and EDS analysis revealed the resulting variation in surface morphology and composition. The electrochemical behavior of different coatings was evaluated by measuring the open circuit potential and cyclic polarization trends in 3.5 wt.% NaCl solution. The degradation behavior of the electrodeposited Zn-Ni coatings was estimated by conducting a salt spray test for 96 h. The addition of Ni in the coating influenced the coating thickness and surface morphology of the coatings. The coating thickness decreased from 38.2 ± 0.5 μm to 20.7 ± 0.5 μm with the increase in Ni concentration. Relatively negative corrosion potential (<−1074 ± 10 mV) of the Zn-Ni alloy coatings compared to the steel substrate (−969 mV) indicated the sacrificial dissolution behavior of the Zn-rich coatings. On the other hand, compared to the pure Zn (26.12 mpy), ~4 times lower corrosion rate of the Zn-Ni coating (7.85 mpy) was observed by the addition of 25 g·L−1 Ni+2 in the bath solution. These results highlighted that the dissolution rate of the sacrificial Zn-Ni alloy coatings can effectively be tuned by the addition of Ni in the alloy coating during the electrodeposition process.
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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".