Structure and adsorption of water in nonuniform cylindrical nanopores
Bibliographic record
Abstract
Grand canonical Monte Carlo simulations are used to examine the adsorption and structure of water in the interior of cylindrical nanopores in which the axial symmetry is broken either by varying the radius as a function of position along the pore axis or by introducing regions where the characteristic strength of the water-nanopore interaction is reduced. Using the extended simple point charge (SPC∕E) model for water, nanopores with a uniform radius of 6.0 Å are found to fill with water at chemical potentials approximately 0.5 kJ∕mol higher than the chemical potential of the saturated vapor. The water in these filled pores exists in either a weakly structured fluidlike state or a highly structured uniformly polarized state composed of a series of stacked water clusters with pentagonal cross sections. This highly structured state can be disrupted by creating hydrophobic regions on the surface of the nanopore, and the degree of disruption can be systematically controlled by adjusting the size of the hydrophobic regions. In particular, hydrophobic banded regions with lengths larger than 9.2 Å result in a complete loss of structure and the formation of a liquid-vapor coexistence in the tube interior. Similarly, the introduction of spatial variation in the nanopore radius can produce two condensation transitions at distinct points along the filling isotherm.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".