Use of Multielectrode Arrays and Statistical Analysis to Investigate the Pitting Probability of Copper. Part I: The Effect of Chloride
Bibliographic record
Abstract
Under some conditions, copper and copper alloys are either immune from corrosion or undergo slow uniform corrosion, generally considered a favourable situation, since predicting the damage incurred by the metal during a period of uniform corrosion is relatively straightforward. However, under conditions leading to surface passivation of Cu, localized corrosion might occur in the presence of aggressive oxidants. Therefore, the susceptibility of Cu to localized corrosion must be considered carefully to avoid unpredictable failures in Cu-based structures. Understanding the pitting probability of Cu is important for various applications, including the use of Cu-coated containers for the permanent disposal of used nuclear fuel. In this study, the pitting probability of Cu in chloride-containing solutions crudely representing the groundwater that might be found in a deep geologic repository (DGR) was investigated using electrochemical techniques and statistical analysis. The probabilities of both pitting and repassivation of Cu were found to increase with increasing [Cl−]. The surface morphologies of copper electrodes in the same solution were also evaluated using scanning electron microscopy (SEM). The passive film on the surface of the copper electrode with the highest breakdown potential (Eb) was found to be more protective than that on the electrode with the lowest Eb.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".