Optimization of zinc‐nickel film electrodeposition for better corrosion resistant characteristics
Bibliographic record
Abstract
Corrosion is one of the main causes of structural deterioration in offshore and marine structures. One way to mitigate the effect of corrosion is with Zn‐Ni electroplated coatings. An experimental design and optimization procedures for Zn‐Ni alloy electroplating was an explored. This study analyzed a five‐variable experimental plan comprised of four steps: (1) a two‐level fractional factorial design (FFD); (2) a response surface design the steepest ascent analysis; (3) a central composite design (CCD); and (4) a corrosion behaviour test to optimize the factors in Zn‐Ni deposition. The critical plating variables in step 1 were zinc/nickel molar concentration ratio, current density, citrate concentrations, plating temperature, and plating time, used to determine their influence on the polarization resistance and corrosion resistance. In steps 2 and 3 the significant variables were studied using the steepest ascent method and the central composite design (CCD) to find the most optimal conditions for zinc‐nickel electroplating. These conditions were found to be a Zn/Ni molar concentration ratio of 0.66, a plating temperature of 28 °C, an electroplating current density of 60 mA/cm 2 , an electroplating time of 13 min, and a citrate concentration of 0.062 mol/L. The corrosion behaviour test of step 4 showed that the films with a higher intensity of the γ‐NiZn 3 , γ‐Ni 2 Zn 11 , and γ‐Ni 3 Zn 22 phases exhibited better corrosion resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".