The Electrochemistry of Hydrogen Peroxide on Uranium Dioxide and the Modelling of Used Nuclear Fuel Corrosion under Permanent Disposal Conditions
Bibliographic record
Abstract
This thesis reports a series of investigations examining the corrosion process of used nuclear fuel under permanent disposal conditions. The motivation of the project is that the safety assessment of deep geological disposal of spent nuclear fuel requires a fundamental understanding of the processes controlling fuel corrosion which could lead to the release of radionuclides to the geosphere from a failed container.\nOne primary objective of this project was to develop a computational model in order to simulate fuel corrosion under the disposal conditions. A series of simulations based on COMSOL were designed and developed to determine the influence of redox conditions, with the emphasis on α-radiolysis and steel vessel corrosion products, on the corrosion rate of spent fuel.\nA second objective of this project was to develop a more detailed understanding of the H2O2 decomposition process and its influence on UO2 (nuclear fuel) corrosion. The radiolytically produced H2O2 is the primary driving force for nuclear fuel corrosion under disposal conditions. The influence of several variables (potential, pH, carbonate/bicarbonate, and fission products) on the reactivity of H2O2 has been evaluated. Their influence on the surface composition and electrical conductivity of UO2 was found to significantly affect the surface redox reaction rates and alter the overall fuel corrosion rate. The analytical techniques include electrochemical measurements (CV, CSV, ECORR, LPR, EIS) and surface/solution analyses (SEM, XPS, ICP-AES).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".