An integrated study of uranyl mineral dissolution processes: etch pit formation, effects of cations in solution, and secondary precipitation
Bibliographic record
Abstract
Abstract Understanding the mechanism(s) of uranium-mineral dissolution is crucial for predictive modeling of U mobility in the subsurface. In order to understand how pH and type of cation in solution may affect dissolution, experiments were performed on mainly single crystals of curite, Pb2+ 3(H2O)2[(UO2)4O4(OH)3]2, becquerelite, Ca(H2O)8[(UO2)6O4(OH)6], billietite, Ba(H2O)7[(UO2)6O4(OH)6], fourmarierite Pb2+ 1−x(H2O)4[(UO2)4O3−2x(OH)4+2x] (x= 0.00–0.50), uranophane, Ca(H2O)5[(UO2)(SiO3OH)]2, zippeite, K3(H2O)3[(UO2)4(SO4)2O3(OH)], and Na-substituted metaschoepite, Na1−x[(UO2)4O2−x(OH)5+x] (H2O)n. Solutions included: deionized water; aqueous HCl solutions at pH 3.5 and 2; 0.5 mol L−1 Pb(II)-, Ba-, Sr-, Ca-, Mg-, HCl solutions at pH 2; 1.0 mol L−1 Na- and K-HCl solutions at pH 2; and a 0.1 mol L−1 Na2CO3 solution at pH 10.5. Uranyl mineral basal surface microtopography, micromorphology, and composition were examined prior to, and after dissolution experiments on micrometer scale specimens using atomic force microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy. Evolution of etch pit depth at different pH values and experimental durations can be explained using a stepwave dissolution model. Effects of the cation in solution on etch pit symmetry and morphology can be explained using an adsorption model involving specific surface sites. Surface precipitation of the following phases was observed: (a) a highly-hydrated uranyl-hydroxy-hydrate in ultrapure water (on all minerals), (b) a Na-uranyl-hydroxy-hydrate in Na2CO3 solution of pH 10.5 (on uranyl-hydroxy-hydrate minerals), (c) a Na-uranyl-carbonate on zippeite, (d) Ba- and Pb-uranyl-hydroxy-hydrates in Ba-HCl and Pb-HCl solutions of pH 2 (on uranophane), (e) a (SiOx(OH)4−2x) phase in solutions of pH 2 (uranophane), and (f) sulfate-bearing phases in solutions of pH 2 and 3.5 (on zippeite).
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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 | 0.000 | 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".