Use of Multielectrode Arrays and Statistical Analysis to Investigate the Pitting Probability of Copper: Part II. The Effect of Sulfate and Bicarbonate
Bibliographic record
Abstract
Copper and copper alloys have found applications in various industries. One of the main reasons Cu and its alloys are utilized widely is that they have sufficient corrosion resistance in key environments, such as seawater and anoxic solutions; however, localized corrosion processes might occur in the presence of aggressive anions, oxygen, or an increase in solution pH. In critical applications of Cu, the susceptibility of Cu to localized corrosion, specifically pitting, must be carefully considered, as it could lead to material failure. In this study, the pitting probability of Cu in unary (sulfate) and binary (sulfate + bicarbonate) solutions was investigated using electrochemical techniques in conjunction with statistical analysis. We determined pitting probabilities based on two different defining criteria for pitting susceptibility, one based on the probability that the corrosion potential, Ecorr, could exceed the passivity breakdown potential, Eb, and the other, a more conservative approach, based on the likelihood that Ecorr would be greater than the repassivation potential, Erp. The pitting probability of Cu did not change significantly with sulfate concentration at pH 8 but was found to increase with increasing [ <?CDATA ${{{\rm{SO}}}_{4}}^{2-}$?> SO 4 2 − ] up to 0.005 M at pH 9 and then to decrease with a further increase in [ <?CDATA ${{{\rm{SO}}}_{4}}^{2-}$?> SO 4 2 − ].
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".