Surface Tensions in NaCl−Water−Air Systems from MD Simulations
Bibliographic record
Abstract
Surface tensions for liquid-vapor (lv), solid-liquid (sl), and solid-vapor (sv) interfaces are calculated from molecular dynamics simulations of the NaCl-water-air system. Three distinct calculation techniques based on thermodynamic properties are used to describe the multicomponent mixtures. Simulations of each bulk phase (including a liquid saturated solution) and various interfaces are carried out at both NPT and NVT conditions. The thermodynamic relation for energy difference between interface and bulk phases provides an upper bound to the surface tension, while the energy-integral and test area methods provide direct estimates. At 1 atm and 300 K, the best predictions for surface tensions are sigmasv (NaCl-air) of 114 mN m(-1), sigmasl (NaCl- soln) of 63 mN m(-1), sigmalv (soln-air) of 82 mN m(-1), and sigmalv (water-air) of 66 mN m(-1). The calculated surface tensions from simulations have uncertainties between 5 and 10%, which are higher than measurements for the liquid interfaces and lower than the measurement uncertainty for the solid interfaces. The calculated upper bounds for surface tensions of liquid interfaces compare well with experimental results but provide no improvement over existing measurements. However, the bounding values for solid interfaces lower uncertainty by as much as a factor of 10 as compared to the indirect experimental measurements currently available. The energy-integral and test area methods appear to underestimate the surface tension of water by 10%, which is consistent with previous studies using similar model potentials. The calculated upper bounds of surface tension show a weakly positive correlation with pressure in the 0.1-100 atm range for liquid-solid, liquid-vapor, and solid-vapor interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".