Understanding the Corrosion of Copper in Nitric Acid By Tuning the Solution Chemistry
Bibliographic record
Abstract
The used fuel container (UFC) is a key engineered barrier to permanently contain and isolate used nuclear fuels underground in deep geological repositories (DGR) to be implemented in Canada, Sweden and Finland.1,2 Copper is used for corrosion protection in the current design of the UFC. Within a DGR, radiolysis of humid air will result in the formation of nitric acid.3 Recent studies showed that copper corrodes faster in nitric acid when oxygen is present, which will be the case for a short period after the UFC’s emplacement.4 When the oxygen becomes depleted in a DGR, the corrosion of copper will be minimal but can be affected by corrosion products such as Cu+, Cu2+, NO2 - and NO. Previous results suggest that the presence of copper ions in the solution surrounding the copper can activate nitrate as a cathodic reagent.5 A possible mechanism is that Cu+ reacts with nitrate through its oxidation to Cu2+, which then reacts with metallic copper through its comproportionation reaction, accelerating the corrosion process.5 Yet, a precise mechanistic understanding of the role of Cu+, such as the degree to which oxidation of Cu+ occurs in solution, or if Cu+ simply acts as a catalyst for nitrate reduction or decomposition, is missing. As a result, an improved understanding of the influence of solution parameters on the corrosion mechanisms of copper in nitric acid is needed, including the presence of oxidants and Cu+-chelating species such as chloride. Herein, the interplay between the dissolved redox couples, Cu+/Cu2+ and NO3 -/NO2 - and Cl- is explored, and their effect on the corrosion of copper is studied by linear sweep voltammetry and polarization resistance and Tafel analysis. The interpretation of linear polarization resistance measurements in the presence of multivalent ions and the role of corrosion products are discussed. 1 P. G. Keech, P. Vo, S. Ramamurthy, J. Chen, R. Jacklin and D. W. Shoesmith, Corrosion Engineering, Science and Technology, 49, 2014, 425-430. 2 F. King, L. Ahonen, C. Taxen, U. Vuorinen and L. Werme, Copper corrosion under expected conditions in a deep geologic repository (SKB-TR--01-23), 2001. Sweden. 3 R. P. Morco, J. M. Joseph, D. S. Hall, C. Medri, D. W. Shoesmith and J. C. Wren, Corrosion Engineering, Science and Technology, 52, 2017, 141-147. 4 J. Turnbull, R. Szukalo, M. Behazin, D. Hall, D. Zagidulin, S. Ramamurthy, J. Wren and D. Shoesmith, Corrosion, 74, 2017, 326-336. 5 J. Turnbull, R. Szukalo, D. Zagidulin, M. Biesinger and D. W. Shoesmith, Materials and Corrosion, 72, 2021, 348–360.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".