Investigating of the Effect of Air-Formed Surface Films on Copper Corrosion Properties
Bibliographic record
Abstract
Canada’s plan for the safe and long-term management of used nuclear fuel involves utilizing an engineered multi-barrier system that will be deposited in a deep geological repository (DGR).1 Developed and proposed by the Nuclear Waste Management Organization (NWMO), the used fuel bundles will be placed in carbon-steel containers that will be coated with a about 3-mm thick corrosion resistant copper (Cu) coating. To date, Cu corrosion studies have been performed on freshly polished metal coupons that have undergone a rigorous cleaning procedure to eliminate air-formed organic and oxide films from the material’s surface. However, prior to emplacement in a DGR, the containers will be fabricated and stored for periods of time where such surface films can deposit and accumulate.2 Furthermore, a standardized surface preparation/finish procedure has yet to be determined. The influence of air-formed surface films on the Cu coating’s atmospheric and aqueous corrosion behaviour is important to understand the likelihood and extent of localized corrosion under anticipated DGR conditions. This work aims to investigate the effect of aging period, surface roughness, and storage environment on the surface film’s chemical composition, morphology, and influence on Cu corrosion properties using a suite of surface analytical and electrochemical methods. The results will assist in the future development of a surface preparation strategy of the Cu coatings prior to used fuel confinement and DGR deposition. Hall, D. S., Behazin, M., Jeffrey Binns, W. & Keech, P. G. An evaluation of corrosion processes affecting copper-coated nuclear waste containers in a deep geological repository. Prog. Mater. Sci. 118, 100766 (2021). Gateman, S. M. et al. Corrosion of One-Step Superhydrophobic Stainless-Steel Thermal Spray Coatings. ACS Appl. Mater. Interfaces 12, 1523–1532 (2020).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".