Effect of Mercury(II) Ions on the Corrosion Resistance of Aluminium Alloy Coatings in 3.5% wt. NaCl solution
Bibliographic record
Abstract
In order to investigate the influence of mercury ions (Hg 2+ ) on the corrosion behavior of typical materials used in open rack vaporizer (ORV) in solutions containing chloride ions, 5052 aluminum alloy substrate flame sprayed with aluminum-zinc coating was used as a research object. The electrochemical corrosion behavior of the coating in 3.5% wt. NaCl solutions containing different concentrations of Hg 2+ was investigated by potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques, and the surface microstructure and physical phases of the coating were characterized by scanning electron microscopy (SEM) and XRD. It was found that a relatively dense corrosion product film was generated on the surface of the coating in the low concentration of Hg 2+ solution during immersion, which slightly inhibited the corrosion of the coating, at which point the self-corrosion current density was maintained at less than 10 μA·cm -2 . While the concentration exceeded 0.5 ug/L, the corrosion rate of the coating was accelerated as the concentration increased, and the self-corrosion current density reached 23.3 μA·cm -2 at the Hg 2+ concentration of 30 ug/L. After 32 days of immersion, a higher R t value of the sample in the low concentration of mercury ion solution can reach 13205.8 Ω·cm 2 , which is only 2627.3 Ω·cm 2 at a high concentration of 30 ug/L mercury ion, indicating that the high concentration of mercury ion under prolonged immersion produces significant damage to the corrosion resistance of the specimens. During this period, pitting holes and local coating flaking appeared on the coating surface, and the corrosion morphology changed from uniform corrosion to local corrosion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".