DFT study of Se(-II) sorption on biotite in reducing conditions
Bibliographic record
Abstract
Abstract Crystalline rock is considered a potential host rock for a deep geological repository (DGR) for storing nuclear waste. Biotite is a common accessory mineral found in crystalline rocks such as granite and tonalite that plays an important role in the absorption of selenium (Se). In particular, the radioisotope Se-79 is widely present in spent nuclear fuel, and it is crucial for evaluating the suitability of a DGR for storing nuclear waste. In the anticipated reducing conditions of a DGR, the prevalent oxidation state would be Se (-II). However, the low solubility of Se under reducing conditions means that traditional spectrometry methods are not effective at studying the sorption mechanisms of Se (-II). Therefore, this study investigated the sorption mechanisms of Se (-II) on biotite under reducing conditions by using density functional theory (DFT) calculations to investigate the inner-sphere complex and outer-sphere complex reactions. Both complexes were found to contribute to sorption of Se (-II) on biotite with the outer-sphere complexes being the primary sorption mechanism under the naturally neutral groundwater conditions. The results of the DFT calculations were consistent with the sorption experimental data and the results of the surface complexation modeling, which further confirmed the significance of the inner-sphere and outer-sphere complexes to the sorption of Se (-II) on biotite under a wider range of pH conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".