Investigating the effect of Cu<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si96.svg" display="inline" id="d1e1694"><mml:msup><mml:mrow/><mml:mrow><mml:mn>2</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math> sorption in montmorillonite using density functional theory and molecular dynamics simulations
Bibliographic record
Abstract
Montmorillonite (MMT) is the main mineral component of bentonite, which is currently proposed as a sealing material in deep geological repositories (DGRs) for used nuclear fuel. In the Canadian program, which will utilize copper-cladded used fuel containers, safety analysis considers the effect of copper corrosion, during which Cu 2 + ions could potentially be adsorbed by the surrounding MMT. In such a scenario, ion exchange between Na + and Cu 2 + is expected. In this study, a multiscale approach that combines electronic density functional theory (DFT) and force-field-based molecular dynamics (MD) simulations was employed to study the effect of introducing Cu 2 + ions to MMT. An extension to the ClayFF force field is parameterized and validated using DFT to model how Cu 2 + interacts with clay systems. MD simulations were performed to calculate the interaction free energies between MMT platelets containing Cu 2 + ions (Cu-MMT) and compared them to inter-platelet interaction energies in Na-MMT and Ca-MMT. Our calculations suggest Cu-MMT develops swelling pressures between those of Ca-MMT and Na-MMT. Furthermore, our MD simulations suggest that Cu 2 + has MMT interlayer mobility that is significantly slower than that of Ca 2＋ .
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".