Hydrophobicity of Highly Ordered Nanorod Polycrystalline Nickel and Silver Surfaces
Bibliographic record
Abstract
Highly ordered nickel and silver nanorods arrays prepared by alumina template assisted electrodeposition were investigated to determine the effect of the array geometry on metal surface hydrophobicity and adhesion forces. The nanorod geometry, clustering and pinning were used to characterize surface hydrophobicity and its modulation. A contribution of metal crystallographic orientation to the surface energy was calculated. To characterize nanorod array surface properties and elucidate the source of the particle adhesion effects has been calculated. The dispersive components of surface tension γSD and surface polarizability ks, as surface features that markedly characterize hydrophobicity and adhesion, were calculated. The highest hydrophobicity was found for Ag nanorods with aspect ratio of 10 then Ni nanorods with aspect ratio 10. The same geometry of nanorods particles resulted in different surface hydrophobicity and it was ascribed to the orientation of Ag and Ni crystals formed on the top of nanorods. Due to high hydrophobicity nanorod array surfaces could be used as an antifouling surface in medicine to select areas on implant surface not to be colonized by cells and tissues.
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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".