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Record W4412512041 · doi:10.1149/ma2025-01201332mtgabs

Investigating the Surface Oxidation of UO<sub>2</sub> Thin Films Using in-Situ α-Irradiation-Electrochemistry Experiments

2025· article· en· W4412512041 on OpenAlexaboutno aff
Hossein Amiriyarahmadi, Mehran Behazin, Peter Keech, Lyudmila V. Goncharova, James J. Noël

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsIn situElectrochemistryIrradiationMaterials scienceThin filmNanotechnologyChemical engineeringChemistryElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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 U VI . This can increase the fuel’s dissolution rate as U VI is more soluble than U V and U IV by several orders of magnitude. Therefore, it is necessary to understand the direct effects of high-energy α-particles on the surface of UO 2 -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 UO 2 -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 UO 2 thin film samples and the Tandetron Accelerator’s Rutherford backscattering beamline to deliver the high-energy high-flux α-particles. To prepare UO 2 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. UO 2 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 UO 2 films after α-irradiation will be studied and the effects of α-irradiation on dissolution rate, surface morphology, and phase structure of the UO 2 samples will be presented.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.255
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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