Void-induced dissolution in molecular dynamics simulations of NaCl and water
Bibliographic record
Abstract
To gain a better understanding of the interaction of water and NaCl at the surface during dissolution, we have used molecular dynamics to simulate the interface with two equal-sized slabs of solid NaCl and liquid water in contact. The introduction of voids in the bulk of the salt, as well as steps or pits on the surface of the NaCl slab results in a qualitative change of system structure, as defined by radial distribution functions (RDFs). As an example, the characteristic Na-Na RDF for the system changes from regularly spaced narrow peaks (corresponding to an ordered crystalline structure), to a broad primary and smaller secondary peak (corresponding to a disordered structure). The change is observed at computationally short time scales of 100 ps, in contrast with a much longer time scale of 1 mus expected for complete mixing in the absence of defects. The void fraction (which combines both bulk and surface defects) required to trigger dissolution varies between 15%-20% at 300 K and 1 atm, and has distinct characteristics for the physical breakdown of the crystal lattice. The void fraction required decreases with temperature. Sensitivity studies show a strong dependence of the critical void fraction on the quantity and distribution of voids on the surface, with systems containing a balanced number of surface defects and a rough surface showing a maximum tendency to dissolve. There is a moderate dependence on temperature, with a 5% decrease in required void fraction with a 100 K increase in temperature, and a weak dependence on water potential model used, with the SPC, SPC/E, TIP4P, and RPOL models giving qualitatively identical results. The results were insensitive to the total quantity of water available for dissolution and the duration of the simulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".