Characterization of the influence of fission product doping on the anodic reactivity of uranium dioxide
Bibliographic record
Abstract
The influence of fission product doping on the structure, composition, and electrochemical reactivity of uranium dioxide has been studied using X-ray diffractometry (XRD), scanning electron microscopy (SEM/EDX), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). Experiments were conducted on SIMFUEL specimens with simulated burn-ups (increasing doping levels) of 1.5, 3.0, and 6.0 atom%. As the dopant level increased, the lattice contracted, suggesting the dominant formation of dopant-oxygen vacancy clusters. The smaller than expected lattice contraction can be attributed to the segregation of Zr (one of eleven added dopants) to ABO3 perovskite-type phases that SEM/EDX shows also contain Ba, Ce, and possibly some U. Raman spectroscopy shows that doping leads to a loss of cubic symmetry, possibly associated with tetragonal distortions. Raman mapping confirms this loss of cubic symmetry and suggests the specimen is not uniformly doped. Electrochemical experiments show that these distortions lead to a decrease in the oxidative dissolution rate of the UO2 with increased doping density.Key words: UO2, X-ray diffraction, electrochemistry, Raman spectroscopy, nuclear fission products.
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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 | 0.001 |
| 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".