The Role of Geometry on the Ease of Solidification Inside and Out of Cylindrical Nanopores
Bibliographic record
Abstract
We investigated the role of a nanoporous particle on the formation of macroscopic solid in the framework of equilibrium thermodynamics and from the free-energy perspective. The model particle has cylindrical pores with equidistant circular openings on the particle surface. We focused on two potentially limiting steps: (i) the solid nucleation from liquid inside a single pore and (ii) the bridging of multiple pores on the particle surface. We examined the nucleation near the liquid-vapor meniscus inside a pore by considering different solid-vapor and solid-pore wall contact angles, as well as the liquid-vapor meniscus angles. For bridging, we quantified the effects of the proximity of neighboring pores and the number of participating pores where we considered two or three pores, placed two different distances apart, and three contact angles of the solid with the particle surface. Except in special cases where an analytical solution could be developed, we determined the equilibrium nucleus and bridge shapes numerically using the Surface Evolver code. The geometry of these equilibrium shapes was the key for correctly calculating the energy barriers. Our results indicate that the meniscus angle can be an important factor in reducing the barrier for nucleation if the internal angles of the solid nucleus satisfy a certain criterion. For the solid growth out of the pores, we found that the barriers were significantly lower in the presence of multiple, closely packed pores compared to the growth from a single pore. This paper is deliberately written with no reference to material properties or a specific process to highlight the generality of geometry-controlled barriers. A direct application where our findings can be particularly valuable is the ice formation in clouds, which is the subject of intensive research in atmospheric sciences for its role in influencing precipitation patterns and hence the climate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".