Fabrication of Superhydrophobic Surfaces on Aluminum Alloy Via Electrodeposition of Copper Followed by Electrochemical Modification
Bibliographic record
Abstract
Abstract Superhydrophobic aluminum surfaces have been prepared by means of electrodeposition of copper on aluminum surfaces, followed by electrochemical modification using stearic acid organic molecules. Scanning electron microscopy (SEM) images show that the electrodeposited copper films follow “island growth mode” in the form of microdots and their number densities increase with the rise of the negative deposition potentials. At an electrodeposition potential of −0.2 V the number density of the copper microdots are found to be 4.5×104 cm−2 that are increased to 2.9×105 cm−2 at a potential of −0.8 V. Systematically, the distances between the microdots are found to be reduced from 26.6 μm to 11.03 μm with the increase of negative electrochemical potential from −0.2V to −0.8V. X-ray diffraction (XRD) analyses have confirmed the formation of copper stearate on the stearic acid modified copper films. The roughness of the stearic acid modified electrodeposited copper films is found to increase with the increase in the density of the copper microdots. A critical copper deposition potential of −0.6V in conjunction with the stearic acid modification provides a surface roughness of 6.2 μm with a water contact angle of 157°, resulting in superhydrophobic properties on the aluminum substrates.
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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 | 0.001 | 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".