Thermodynamic Investigation of the Barrier for Heterogeneous Nucleation on a Fluid Surface in Comparison with a Rigid Surface
Bibliographic record
Abstract
When a vapor phase is in contact with a solid or nonvolatile fluid, under conditions where the vapor is thermodynamically metastable to condensation, a droplet may nucleate from the vapor either homogenously within the vapor phase, or heterogeneously at the solid or fluid substrate interface. The case where the droplet is thermodynamically favored to nucleate heterogeneously is the subject of this article. The heterogeneous nucleation of a sessile drop on a soft surface has been studied many times experimentally and theoretically. It has been observed experimentally that heterogeneous nucleation happens faster on a soft surface in comparison with a rigid surface. Here we use Gibbsian surface thermodynamics to provide a physical understanding for this observation. Due to the difficulties of considering soft-elastic surfaces, we demonstrate that by considering only the fluidity of a surface (i.e., by considering a fluid surface as an infinitely soft material and comparing a fluid surface with a rigid surface), thermodynamics will predict that heterogeneous nucleation is easier on soft surfaces compared with rigid surfaces. We first investigate the effect of contact angle on the barrier for heterogeneous nucleation on rigid substrates at constant vapor phase pressure. Then we find a lower energy barrier for heterogeneous nucleation at a fluid surface in comparison with heterogeneous nucleation at a rigid surface which explains the faster nucleation on soft surfaces compared with rigid surfaces. Finally we inspect the role of each contribution to the energy barrier.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".