Influence of Chloride Concentration on the Pitting Probability of Copper Using Coupled Multielectrode Arrays
Bibliographic record
Abstract
The preferred disposal method for used nuclear fuel is to seal it in containers emplaced in a deep geologic repository (DGR) in a suitable rock formation. In Canada, the container is a robust steel vessel coated with 3 mm of Cu as a corrosion barrier. Eventually, the DGR environment will become anoxic, and in the absence of oxygen copper is thermodynamically stable in water. However, since exposure conditions will initially be oxidizing due to the presence of O2 trapped on sealing the DGR, there is a possibility of localized corrosion. Mass balance calculations show that uniform corrosion during this period will be within the designed corrosion allowance, however, since the Cu coating is relatively thin and the required lifetime is long, the risk of pitting corrosion during this period must be carefully evaluated. Pitting corrosion susceptibility is determined by the corrosion potential of the material (Ecorr, the potential at which the rates of all anodic and cathodic reactions are equal), and the breakdown potential (Eb, the potential at which the copper oxidation rate on the surface increases abruptly due to the breakdown of the protective film), both of which are distributed parameters, due to the stochastic nature of passive film rupture and any uncontrollable variations in the structure and local environment at the metal surface. It is important to note that the analysis is based on the concept that pitting is only possible if the Ecorr is equal to, or more positive than the Eb. In this work, a multielectrode array was used to obtain a statistically meaningful set of potential measurements through the simultaneous monitoring of 30 electrodes to determine their corrosion and breakdown potentials. From the extensive database thus acquired, we defined the Ecorr and Eb distributions for copper electrodes exposed to solutions containing various chloride concentrations. Our previous studies showed passive behavior of copper surface exposed to high pH solutions. Therefore, a more thorough evaluation of the relative values of Ecorr and Eb is required to determine the susceptibility to pitting. Experiments were conducted by using multielectrode array at 25 C and pH 11. In alkaline solutions, copper oxide is more stable and can form a passive film, whereas chloride promotes the breakdown of the passive film. As the chloride concentration increased, values of Ecorr and Eb decreased, suggesting that the solubility of copper increases with increasing chloride concentration. Therefore, the stability of the passive film is decreased in the presence of chloride. In general, the possibility of pitting increases with growing overlap between the distribution curves of Ecorr and Eb. To evaluate the possibility of pitting, histograms and distribution curves of Ecorr and Eb in the solutions what have different chloride concentrations were compared. In lower chloride concentrations, there was a very small overlap between Ecorr and Eb; consequently, the possibility of pitting under these conditions is very low. On the other hand, a high chloride concentration at high pH contributed to a higher probability of pitting, as the overlap between Ecorr and Eb distributions increased.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".