Preparation of Nanostructured Superhydrophobic Copper and Aluminum Surfaces
Bibliographic record
Abstract
Preparation of nanostructured superhydrophobic surfaces requires both an optimum roughness and low surface energy. Application of a direct voltage between two copper plates immersed in a dilute ethanolic stearic acid solution transforms the surface of the anodic copper electrode to superhydrophobic due to the formation of micro-nanofibrous low surface energy flower-like copper stearate as confirmed by scanning electron microscope (SEM). Nanostructured superhydrophobic aluminum surfaces have also been prepared by electrodeposition of copper films on aluminum surfaces followed by electrochemical modification by ethanolic stearic acid. The X-ray diffraction (XRD) analyses confirmed the formation of copper films on aluminum substrates. The electrodeposited copper films are composed of microdots of copper whose density increases with the decrease of deposition potential as observed by SEM. The deposited copper microdots on aluminum substrates were electrochemically modified to low surface energy copper stearate nanofibres to obtain superhydrophobicity. The copper films deposited at potentials above-0.6 V did not exhibit superhydrophobic properties. However, the copper films deposited at potential-0.6 V and below exhibited superhydrophobic properties with water drop rolling-off those surfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".