Antibacterial Evaluation of Ag/Cu Doped in DLC Coatings
Bibliographic record
Abstract
The development of antimicrobial coatings is an emerging technology to reduce infections acquired from high-touch surfaces in hospitals. Among these, metal-doped coatings have gained attention for their antimicrobial effects. Diamond-like carbon (DLC) coatings, known for their excellent tribological properties, are also being explored for biomedical applications. This research on the antibacterial activity of metal-doped DLC coatings, particularly those synthesized via magnetron sputtering, has the potential to significantly impact the field of biomedical materials and contribute to the development of safer healthcare environments. Due to their potent antimicrobial properties, silver (Ag) and copper (Cu) have become increasingly crucial in hospital surface coatings. Despite this, studies explicitly focusing on the antibacterial activity of Ag/Cu-doped DLC are scarce. In this study, Ag/Cu-DLC coatings were synthesized using the direct current (DC) magnetron sputtering method, with varying Ag target power between 0-20 W and Cu target power between 0-36 W. The coatings' chemical composition and structural characteristics were analyzed using XRD, XPS, Raman spectroscopy, and SEM. The antibacterial activities of the coatings against pathogens Klebsiella pneumoniae and Staphylococcus aureus were evaluated using a modified disk diffusion assay, Minimum Inhibitory Concentration (MIC), and time-course antimicrobial assays.The results demonstrated that Ag/Cu-doped DLC coatings exhibited superior antibacterial properties compared to undoped DLC coatings, mainly when tested in an LB medium. The doped DLC coatings effectively inhibited bacterial growth, making them suitable for application on material surfaces to prevent bacterial spread. The study also highlighted the initial effectiveness of both coatings in inhibiting bacterial proliferation. However, the longevity of this effect differed, with Ag-DLC displaying more prolonged antibacterial action due to the continuous release of silver. At the same time, Cu-DLC showed a rapid decline in effectiveness over time.
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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 | 0.001 | 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".