An Evaluation of the Mechanical Properties and Microstructure in Uranium Dioxide Doped with Oxide Additives
Bibliographic record
Abstract
abstract: The United States Department of Energy (DOE) has always held the safety and reliability of the nation's nuclear reactor fleet as a top priority. Continual improvements and advancements in nuclear fuels have been instrumental in maximizing energy generation from nuclear power plants and minimizing waste. One aspect of the DOE Fuel Cycle Research and Development Advanced Fuels Campaign is to improve the mechanical properties of uranium dioxide (UO2) for nuclear fuel applications.\n\nIn an effort to improve the performance of UO2, by increasing the fracture toughness and ductility, small quantities of oxide materials have been added to samples to act as dopants. The different dopants used in this study are: titanium dioxide, yttrium oxide, aluminum oxide, silicon dioxide, and chromium oxide. The effects of the individual dopants and some dopant combinations on the microstructure and mechanical properties are determined using indentation fracture experiments in tandem with scanning electron microscopy. Indentation fracture experiments are carried out at room temperature and at temperatures between 450 °C and 1160 °C. \n\n The results of this work find that doping with aluminosilicate produces the largest favorable change in the mechanical properties of UO2. This sample exhibits an increase in fracture toughness at room temperature without showing a change in yield strength at elevated temperatures. The results also show that doping with Al2O3 and TiO2 produce stronger samples and it is hypothesized that this is a result of the sample containing dopant-rich secondary phase particles.
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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) | 0.001 | 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".