Gibbsian Thermodynamics of Wenzel Wetting (Was Wenzel Wrong? Revisited)
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide When a drop is in contact with a rough surface, it can rest on top of the rough features (the Cassie–Baxter state) or it can completely fill the rough structure (the Wenzel state). The contact angle (θ) of a drop in these states is commonly predicted by the Cassie–Baxter or Wenzel equations, respectively, but the accuracy of these equations has been debated. Previously, we used fundamental Gibbsian composite-system thermodynamics to rigorously derive the Cassie–Baxter equation, and we found that the contact line determined the macroscopic contact angle, not the contact area that was originally proposed. Herein, to address the various perspectives on the Wenzel equation, we apply Gibbsian composite-system thermodynamics to derive the complete set of equilibrium conditions (thermal, chemical, and mechanical) for a liquid drop resting on a homogeneous rough solid substrate in the Wenzel mode of wetting. Through this derivation, we show that the roughness must be evaluated at the contact line, not over the whole interfacial area, and we propose a new Wenzel equation for a surface with pillars of equal height. We define a new dimensionless number H = h (1 – λ solid )/ R to quantify when the drop’s radius of curvature ( R ) is large enough compared to the size of the pillars for the new Wenzel equation to be simplified ( h is the pillar height; λ solid is the line fraction of the spherical cap’s circumference that is on the pillars). Our new line-roughness Wenzel equation can be simplified to cos θ W = ρ cos θ Y when H ≪ 1, where ρ is the line roughness. We also perform a thermodynamic free-energy analysis to determine the stability of the equilibrium states that are predicted by our new Wenzel equation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".