How Does Uranium Adsorb on (010) Pyrophyllite Under Alkaline Conditions? An In Silico Study
Bibliographic record
Abstract
Deep geological waste repositories must ensure that radionuclides from high-level waste are contained safely, despite the evolution of extreme geochemical conditions of (hyper)alkaline pH (>10) and salinity over geological time scales. Over time, the chemistries of clay minerals (used as geotechnical barriers) and uranium are altered, potentially leaching harmful radionuclides to the environment. However, Ca 2+ was reported to aid in U(VI) retention at pH > 10. Herein, two-dimensional periodic density functional theory calculations in combination with COSMO implicit solvation are carried out to elucidate the retention mechanisms of [UO 2 (OH) 3 (H 2 O)] − and [UO 2 (OH) 4 ] 2– on (010) pyrophyllite under (hyper)alkaline conditions. By considering the protonation equilibria of surface sites and environmental speciation of uranium, the structure and properties of (010) pyrophyllite and the uranyl retention mechanisms have been investigated. Representative of increasing interface pH, three edges of (010) are proposed: 010_H, 010_1Ca, and 010_2Ca . Ca 2+ –bound surfaces ( 010_1Ca, 010_2Ca ) are consistent with protonation equilibria of SiOH and Al(OH 2 ) 2 sites above pH 8, whereas 010_H fails to describe alkaline conditions. The Ca 2+ ions bridge the adsorbed U(VI) species on 010_1Ca or 010_2Ca, and their geometries agree with EXAFS structures from the literature, exhibiting a similar ν stretch for the uranyl bonds. A correlation of energetics and U(VI) adsorption to surface speciation and batch-sorption experiments from the literature is presented to quantitatively distinguish the pH ranges of the proposed edge models. This study highlights the importance of surface and solute chemistries at the interface in building computational models.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".