Facile fabrication of super-hydrophobic nano-needle arrays via breath figures method
Bibliographic record
Abstract
Super-hydrophobic surfaces which have been fabricated by various methods such as photolithography, chemical treatment, self-assembly, and imprinting have gained enormous attention in recent years. Especially 2D arrays of nano-needles have been shown to have super-hydrophobicity due to their sharp surface roughness. These arrays can be easily generated by removing the top portion of the honeycomb films prepared by the breath figures method. The hydrophilic block of an amphiphilic polymer helps in the fabrication of the nano-needle arrays through the production of well-ordered honeycomb films and good adhesion of the film to a substrate. Anisotropic patterns with water wettability difference can be useful for patterning cells and other materials using their selective growth on the hydrophilic part of the pattern. However, there has not been a simple way to generate patterns with highly different wettability. Mechanical stamping of the nano-needle array with a polyurethane stamp might be the simplest way to fabricate patterns with wettability difference. In this study, super-hydrophobic nano-needle arrays were simply fabricated by removing the top portion of the honeycomb films. The maximum water contact angle obtained with the nano-needle array was 150°. By controlling the pore size and the density of the honeycomb films, the height, width, and density of nano-needle arrays were determined. Anisotropic patterns with different wettability were fabricated by simply pressing the nano-needle array at ambient temperature with polyurethane stamps which were flexible but tough. Mechanical stamping of nano-needle arrays with micron patterns produced hierarchical super-hydrophobic structures.PACS: 05.70.Np, 68.55.am, 68.55.jm.
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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 | 0.001 |
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".