How solid–liquid adsorption affects the liquid–vapor interface in pores
Bibliographic record
Abstract
Abstract The augmented Young–Laplace (AYL) equation has been widely used to describe the liquid–vapor interfaces affected by capillarity and adsorption, particularly in nanoporous or clay minerals where strong adsorptive interaction prevails. Still, how adsorption ultimately shapes the liquid–vapor interface in pores remains elusive. All current forms of the AYL equation only consider the attractive van der Waals interaction for complete wetting surface and are mostly an extension of the general equation based on thermodynamic principles without rigorous derivation. In this paper, closed forms of the AYL equations are derived by variational calculus method for two typical pore models considering effects of capillarity and adsorption, based on the minimization of free energy for the equilibrium system. The van der Waals, electrical, and structural forces are incorporated as disjoining pressure into the proposed AYL equations to quantify effects of both attractive and repulsive interactions (hence complete and partial wetting conditions) on interfacial geometry and equilibrium matric potential. Numerical calculation algorithms are established to solve the interfacial geometric profiles and characteristics. The proposed model illustrates a transition region on the interface where geometry and physical characteristics are drastically altered under adsorption compared with sole capillarity. Different surface wettabilities are examined for interface profiles and curvature distributions. An apparent contact angle on the partial wetting surface is identified that is approaching complete wetting as the adsorption dominates when pore size reduces. Water retention and interparticle force demonstrate clear distinctions in their evolution, with different particle sizes and varying half‐filling angles under adsorptive conditions as water volume changes.
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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 | 0.001 |
| 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 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".