Influence of chloride and <scp>pH</scp> on the pitting mechanism of <scp>Zn‐Ni</scp> alloy coating in sodium chloride solutions
Bibliographic record
Abstract
Abstract This paper presents the pitting corrosion behaviour of Zn‐Ni alloy coatings in NaCl solutions with different chloride concentrations and pH. The pitting‐behaviour investigation is done using an optical microscope, potentiodynamic polarization (Tafel slopes), electrochemical impedance spectroscopy (EIS), and scanning electron microscopy (SEM) integrated with energy dispersive spectroscopy (EDS). The design of the experiment with three‐level fractional factorial design (FFD) is used to analyze the behaviour of pitting corrosion. The pitting behaviour in acidic solution with low chloride concentration was found to be significantly different from that in the neutral solution with high chloride concentration. Electrochemical analysis indicates that the corrosion behaviour of samples immersed at 0.35 moL/L NaCl and pH 3.0 at different exposure times have low impedance values compared to the 0.35 moL/L NaCl and pH 7.0 samples. SEM images show that the pH and chloride concentration in the electrolyte has a significant influence on the pitting morphology. Exclusive large pit morphology in an acidic solution (pH 3.0) with low chloride concentration (0.35 moL/L) was also observed. This provides new insight into pitting behaviour on a coated material. The study will serve a valuable tool toward designing or selecting metal coatings for marine or corrosive environments.
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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 | 0.001 | 0.003 |
| 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".