Fluorocarbon Plasma-Polymerized Layer Increases the Release Time of Silver Ions and the Antibacterial Activity of Silver-Based Coatings
Bibliographic record
Abstract
Silver-based antibacterial coatings limit the spread of hospital-acquired infections. Indeed, the use of silver and silver oxide nanoparticles (Ag and AgO NPs) incorporated in amorphous hydrogenated carbon (a-C:H) as a matrix demonstrates a promising approach to reduce microbial contamination on environmental surfaces. However, its success as an antibacterial coating hinges on the control of Ag+ release. In this sense, if a continuous release is required, an additional barrier is needed to extend the release time of Ag+. Thus, this research investigated the use of a plasma fluoropolymer (CFx) as an additional top layer to elongate Ag+ release and increase the antibacterial activity due to its high hydrophobic nature. Herein, a porous CFx film was deposited on a-C:H containing Ag and AgO NPs using pulsed afterglow low pressure plasma polymerization. The chemical composition, surface wettability and morphology, release profile, and antibacterial activity were analyzed. Overall, the combination of a-C:H:Ag (12.1 at. % of Ag) and CFx film (120.0°, F/C = 0.8) successfully inactivated 88% of E. coli and delayed biofilm formation after 12 h. Thus, using a hybrid approach composed of Ag NPs and a hydrophobic polymeric layer, it was possible to increase the overall antibacterial activity of the coating.
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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) | 0.001 | 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".