Pore-scale study of droplet settling on a heterogenous surface structure
Bibliographic record
Abstract
Equilibrium contact angle of a droplet is influenced by surface characteristics and fluid properties. In addition to increasing the solid–liquid contact line, surface roughness also alters the surface free energy, which has a significant influence on contact angle values. Droplets are more likely to impinge on vertices as surface roughness increases. Anisotropic wetting of chemically heterogeneous surfaces further controls the total surface free energy. The free energy Lattice Boltzmann method is utilized to study the effects of wettability heterogeneity and roughened surfaces. Initial model comparisons with experiments showed excellent agreement. The rough surface is modeled with different pillar shapes on a smooth wall, with surface wettability ranging from hydrophilic to neutral conditions. The length scale of surface patterns matches the droplet size, making the Cassie–Baxter and Wenzel equations inapplicable. Results indicate that droplets pin on the vertices of rectangular pillars, while frustum shapes facilitate movement. Studies cover nearly neutral wet, moderately wet, and strongly wet conditions. The effects of relative surface roughness, roughness distribution, mixed wetting surfaces, and body force on equilibrium contact angle are examined. Additionally, the interaction between fluid flow and surface roughness elements shows that smaller spacing and greater height of roughness elements enhance thermal performance, with Nusselt numbers fluctuating significantly. Findings suggest that the ratio of droplet size to pillar surface area is crucial for minimizing surface free energy. On superhydrophilic surfaces, droplet pinning at pillar edges causes the surface to behave hydrophobically. In mixed-wet rough surfaces, pillar wettability significantly influences the equilibrium contact angle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".