Effect of Surfactants on Wetting of Super-Hydrophobic Surfaces
Bibliographic record
Abstract
The effect of surfactants on wetting behavior of super-hydrophobic surfaces was investigated. Super-hydrophobic surfaces were prepared of alkylketene dimer (AKD) by casting the AKD melt in a specially designed mold. Time-dependent studies were carried out, using the axisymmetric drop shape analysis method for contact angle measurement of pure water on AKD surfaces. The results show that both advancing and receding contact angles of water on the AKD surfaces increase over time ( approximately 3 days) and reach the values of about 164 and 147 degrees , respectively. The increase of contact angles is due to the development of a prickly structure on the surface (verified by scanning electron microscopy), which is responsible for its super-hydrophobicity. Aqueous solutions of sodium acetate, sodium dodecyl sulfate, hexadecyltrimethylammonium bromide, and n-decanoyl-n-methylglucamine were used to investigate the wetting of AKD surfaces. Advancing and receding contact angles for various concentrations of different surfactant solutions were measured. The contact angle results were compared to those of a number of pure liquids with surface tensions similar to those of surfactant solutions. It was found that although the surface tensions of pure liquids and surfactant solutions at high concentrations are similar, the contact angles are very different. Furthermore, the usual behavior of super-hydrophobic surfaces that turn super-hydrophilic when the intrinsic contact angle of liquid on a smooth surface (of identical material) is below 90 degrees was not observed in the presence of surfactants. The difference in the results for pure liquids and surfactant solutions is explained using an adsorption hypothesis.
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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 | 0.000 | 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) | 0.001 | 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".