Superhydrophobic Stability of Nanotube Array Surfaces under Impact and Static Forces
Bibliographic record
Abstract
The surfaces of nanotube arrays were coated with poly(methyl methacrylate) (PMMA) using an imprinting method with an anodized alumina membrane as the template. The prepared nanotube array surfaces then either remained untreated or were coated with NH2(CH2)3Si(OCH3)3(PDNS) or CF3(CF2)7CH2CH2Si(OC2H5)3 (PFO). Thus, nanotube arrays with three different surfaces, PDNS, PMMA (without coating), and PFO, were obtained. All three surfaces (PDNS, PMMA, and PFO) exhibited superhydrophobic properties with contact angles (CA) of 155, 166, and 168°, respectively, and their intrinsic water contact angles were 30, 79, and 118°, respectively. The superhydrophobic stabilities of these three surfaces were examined under dynamic impact and static pressures in terms of the transition from the Cassie-Baxter mode to the Wenzel mode. This transition was determined by the maximum pressure (p(max)), which is dependent on the intrinsic contact angle and the nanotube density of the surface. A p(max) greater than 10 kPa, which is sufficiently large to maintain stable superhydrophobicity under extreme weather conditions, such as in heavy rain, was expected from the PFO surface. Interestingly, the PDNS surface, with an intrinsic CA of only 30°, also displayed superhydrophobicity, with a CA of 155°. This property was partially maintained under the dynamic impact and static pressure tests. However, under an extremely high pressure (0.5 MPa), all three surfaces transitioned from the Cassie-Baxter mode to the Wenzel mode. Furthermore, the lost superhydrophobicity could not be recovered by simply relieving the pressure. This result indicates that the best way to maintain superhydrophobicity is to increase the p(max) of the surface to a value higher than the applied external pressure by using low surface energy materials and having high-density binary nano-/microstructures on the surface.
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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 | 0.000 |
| 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".