DFT +<i>U</i>Study of the Adsorption and Dissociation of Water on Clean, Defective, and Oxygen-Covered U<sub>3</sub>Si<sub>2</sub>{001}, {110}, and {111} Surfaces
Bibliographic record
Abstract
Abstract The interfacial interaction of U3Si2 with water leads to corrosion of nuclear fuels, which affects various processes in the nuclear fuel cycle. However, the mechanism and molecular-level insights into the early oxidation process of U3Si2 surfaces in the presence of water and oxygen are not fully understood. In this work, we present Hubbard-corrected density functional theory (DFT + U) calculations of the adsorption behavior of water on the low Miller indices of the pristine and defective surfaces as well as water dissociation and accompanied H2 formation mechanisms. The adsorption strength decreases in the order U3Si2{001} > U3Si2{110} > U3Si2{111} for both molecular and dissociative H2O adsorption. Consistent with the superior reactivity, dissociative water adsorption is most stable. We also explored the adsorption of H2O on the oxygen-covered U3Si2 surface and showed that the preadsorbed oxygen could activate the OH bond and speed up the dissociation of H2O. Generally, we found that during adsorption on the oxygen-covered, defective surface, multiple water molecules are thermodynamically more stable on the surface than the water monomer on the pristine surface. Mixed molecular and dissociative water adsorption modes are also noted to be stable on the {111} surface, whereas fully dissociative water adsorption is most stable on the {110} and {001} surfaces.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".