Quantifying the effects of dissolved nitrogen and carbon dioxide on drying pressure of hydrophobic nanopores
Bibliographic record
Abstract
The effects of a dissolved gas on the behavior of liquid in cylindrical nanopores are investigated in the framework of Gibbsian composite system thermodynamics and classical nucleation theory. An equation is derived relating the phase equilibrium of a mixture of a subcritical solvent and a supercritical gas to the curvature of the liquid-vapor interface. Both the liquid and the vapor phases are treated nonideally, which is shown to be important for the accuracy of the predictions in the case of water with dissolved nitrogen or carbon dioxide. The behavior of water in nanoconfinement is found to be only affected when the gas amount is significantly more than the saturation concentration of these gases at atmospheric conditions. However, such concentrations can be easily reached at high pressures during intrusion if there is sufficient gas present in the system, especially considering gas oversolubility in confinement. By including an adjustable line tension term in the free energy equation (-44 pJ/m for all points), the theory can make predictions in line with the few data points available from recent experimental work. However, we note that such a fitted value empirically accounts for multiple effects and should not be interpreted as the energy of the three-phase contact line. Compared to molecular dynamics simulations, our method is easy to implement, requires minimal computational resources, and is not limited to small pore sizes and/or short simulation times. It provides an efficient path for first-order estimation of the metastability limit of water-gas solutions in nanopores.
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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 | 0.000 | 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 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".