Diffusive transport properties of seawater in calcite nanopores: A molecular dynamics study
Bibliographic record
Abstract
Classical molecular dynamics (MD) simulations were used to explore the diffusive transport properties of seawater through porous media of different calcite structures. Two different calcite surfaces constituted the pore slit models with a size of 1.5 nm; seawater with a 3.5 wt% of salinity occupied the free volume between calcite surfaces at the corresponding average seawater density. The SPC/E water model was employed to describe the water molecules’ interactions, while a Buckingham-type potential modeled the calcite. All cross-interactions were modeled using the Lennard-Jones potential and Ewald sums electrostatics. The simulations demonstrated that calcite surfaces reduce the diffusivity of Na + and Cl - ions, and the magnitude of the diffusivity reduction depends on the structure of the calcite surface. The topology and charge distribution features of the energetically most stable calcite (104) surface led to a slight reduction in the electrolyte diffusivity. On the contrary, density profiles evidenced that the least stable calcite surface (100) favored preferential adsorption of Na + , leading to significant differences in ionic diffusion coefficients. Finally, the ionic-specific diffusion coefficients obtained from molecular dynamics simulations were loaded into an advection–dispersion model to simulate a seawater intrusion into coastal aquifers scenario, with parameters associated with a predominantly diffusive transport through the most stable calcite pore. The concentration profiles showed that minor differences between Na + and Cl - diffusivities result in Na + /Cl - concentration disparities a few meters away from the coastline after years of seawater shifting the dispersion zone.
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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 | 0.001 |
| 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".