Use of Electrochemical Techniques and Statistical Analysis to Investigate The Pitting Probability of Copper
Bibliographic record
Abstract
The development of a safe permanent disposal plan is essential for the long-term disposal of used fuel bundles. Nuclear Waste Management Organization (NWMO) has been investigating the deep geologic disposal of nuclear waste which offers the optimum passive safety system with a negligible probability of release of radionuclides into the environment.\nThe proposed used fuel containers (UFC) for the permanent disposal of high-level nuclear waste in Canada is comprised of a carbon steel vessel coated with a 3 mm corrosion-resistant copper layer deposited using a combination of electrodeposition and cold spray deposition. Although copper is often considered to be thermodynamically stable in anoxic conditions, different corrosion processes might occur upon exposure to aggressive anions, such as chloride, sulfate, and sulfide. The presence of oxygen could increase the possibility of passive film formation on Cu which might result in localized corrosion (specifically pitting) and unexpected failure of UFC; so, the pitting corrosion probability of Cu in these conditions should be evaluated carefully.\nThis research has focused on the development of a new technique to investigate the pitting probability of copper (Cu) in Deep Geological Repository (DGR) conditions using multielectrode arrays and statistical models. Investigation of pitting probability of Cu required the combination of different types of electrochemical experiments including potentiodynamic and potentiostatic polarizations since potentiodynamic polarization test gives us information about the Eb and Erp, however, potentiosatic polarizations simulates the natural condition in which a constant potential is applied to the Cu electrode. As a result, two different applied potentials were chosen based on histograms of Ecorr and Eb which had been collected using potentiodynamic polarization experiments to simulate the natural conditions. Ecorr+ 20 mV and Eb-20 mV vs SCE were selected as the highest and lowest applied potentials, respectively. Also, the morphology and composition of various types of passive films were investigated using SEM and XPS techniques. These information gave us useful information to evaluate the pitting possibility of Cu in the presence of different types of passive films such as Cu2O, CuO, and Cu(OH)2 as well as which film was more resistant to pitting corrosion.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".