Investigating the Surface Oxidation of UO<sub>2</sub> Thin Films Using in-Situ α-Irradiation-Electrochemistry Experiments
Bibliographic record
Abstract
Nuclear power is a clean, safe, and economical energy source and after hydrothermal-powered sources, it is the second largest contributor to low-emitting electricity. However, the continuous usage of nuclear power entails the obligation to deal with the long-term management of used nuclear fuel. To do so, Canada plans to use a deep geological repository (DGR) system that will be built approximately 500-800 meters underground in Wabigoon Lake First Nation and the Township of Ignace, Ontario. The DGR consists of corrosion-resistant used fuel containers (UFCs) and other barriers, and it is expected to provide safe and long-term containment of radioactive waste. However, it is essential to consider the eventual failure of the UFCs which can expose fuel to groundwater and pose potential risks. Most of the radionuclides within the used fuel are trapped inside the fuel matrix and the fuel dissolution rate in groundwater determines how fast they can be released into the environment. The containers are expected to remain intact for a long time and any potential breach is likely to occur after β- and γ-radiation have largely decayed. This makes α-radiation the dominant radiation source at the fuel surface and a primary focus. The α-irradiation can cause accumulated radiation-induced damage to the fuel matrix and a possible scenario in this case is the formation of soluble UVI. This can increase the fuel’s dissolution rate as UVI is more soluble than UV and UIV by several orders of magnitude. Therefore, it is necessary to understand the direct effects of high-energy α-particles on the surface of UO2-based fuels, as these interactions may influence the fuel dissolution rate. This study uses a novel approach to investigate the effects of α-particles on UO2-based fuels via in-situ α-irradiation-electrochemistry experiments. This approach enables studying the surface oxidation state of the fuel in a thin-layer configuration exposed to α-irradiation. The method utilizes UO2 thin film samples and the Tandetron Accelerator’s Rutherford backscattering beamline to deliver the high-energy high-flux α-particles. To prepare UO2 thin films, electrodeposition parameters were first optimized via comprehensive characterization of the films deposited on copper substrates to assess their morphology, elemental composition, phase, and crystal structure. Overall, it was found that using less-negative potentials and current densities is optimal for achieving stable films with smaller cracks, better adherence, and higher crystallinity after annealing. UO2 thin films were then deposited on copper-coated SiN windows using optimized electrodeposition parameters, and the samples were subsequently integrated into a custom-designed in-situ cell for α-irradiation-electrochemistry experiments. Investigations of the effects of high-energy α-particles via in-situ α-irradiation-electrochemistry experiments are currently underway. The surface oxidation states of UO2 films after α-irradiation will be studied and the effects of α-irradiation on dissolution rate, surface morphology, and phase structure of the UO2 samples will be presented.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".