Effect of Ni and Zn Elements on the Microstructure and Antibacterial Properties of Cu Coatings
Bibliographic record
Abstract
In this work, 316L stainless steel samples were coated with copper (Cu) and German silver (Cu 17%Ni 10%Zn) to investigate the relation between their mechanical and antibacterial behaviors. The mechanical and material characteristics of the samples were studied by looking into the microstructure of the surface and the cross-section of the coatings, the surface roughness, and the adhesion strength between the coating layer and the substrate. The antibacterial behavior is then studied against gram-negative Escherichia coli and gram-positive Staphylococcus aureus. Two experiments were conducted to examine the antibacterial behavior. In the first experiment, the coated samples were covered with distilled water, whereas in the second experiment, the samples were tested without being covered with distilled water. The results show that German silver (Cu 17%Ni 10%Zn) had a higher antibacterial rate than copper (Cu) by around 10% for both gram-negative E. coli and gram-positive S. aureus. The reason is because a smoother surface is expected to limit the bacterial adhesion in most cases, and the German silver samples have a lower surface roughness (Ra) due to the higher thermal expansion value of zinc (Zn) compared with copper (Cu). A more in-depth look into the effect of various thickness of the coating with alloying elements (in this case nickel and zinc) on the antibacterial rate would be of great interest.
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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 | 0.001 |
| 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) | 0.000 | 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".