On the long term antibacterial features of silver-doped diamondlike carbon coatings deposited via a hybrid plasma process
Bibliographic record
Abstract
Environmental surfaces are increasingly recognized as important sources of transmission of hospital-acquired infections. The use of antibacterial surface coatings may constitute an effective solution to reduce the spread of contamination in healthcare settings, provided that they exhibit sufficient stability and a long-term antibacterial effect. In this study, silver-incorporated diamondlike carbon films (Ag-DLC) were prepared in a continuous, single-step plasma process using a hybrid, inductively coupled plasma reactor combined with a very-low-frequency sputtering setup. The average Ag concentration in the films, ranging from 0 to 2.4 at. %, was controlled by varying the sputtering bias on the silver target. The authors found that the activity of Escherichia coli was reduced by 2.5 orders of magnitude, compared with the control surface, after a 4-h contact with a 2.4 at. % Ag-DLC coating. The coatings displayed slow release kinetics, with a total silver ion release in the sub-ppb range after 4 h in solution, as measured by graphite furnace-atomic absorption spectroscopy. This was confirmed by Kirby-Bauer diffusion tests, which showed limited diffusion of biocidal silver with a localized antibacterial effect. As a slow and continuous release is mandatory to ensure a lasting antibacterial effect, the newly developed Ag-DLC coatings appears as promising materials for environmental hospital surfaces.
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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) | 0.000 | 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".