Ultrahydrophobic Surface for Water Treatment by Membrane Processes-Prediction of Water Contact Angle on Air/Solid Composite Surface by Solving Young-Laplace Equation
Bibliographic record
Abstract
The evaluation of contact angle (CA) of air-solid composite surface is growing in its importance in membrane separation technology. The reason is that the super-hydrophobic property of the surface allows self-cleaning of membrane surface in various membrane separation processes and also mitigates pore wetting, which is considered the serious disadvantage of membrane distillation. The Cassie-Baxter equation is currently considered one of the best tools to evaluate CA of the air-solid composite surface. However, most of the experimental works of CA measurement were carried out by the sessile drop method, in which the size of the droplet is limited to micro- or submicrometer range, and it is not known how CA is affected by the air content of the air-solid composite surface especially when the droplet size is in a range of millimeter. In this work, the meniscus shape of a large water droplet with a size greater than the capillary length (2.713 mm) was calculated for different air contents at the air-solid surface by solving the Young-Laplace differential equation. It was concluded that the effect of fs (fraction of solid surface) on CA does not depend significantly on the droplet size, even though the droplet flattens considerably as the droplet size increases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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 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".