Bibliographic record
Abstract
Microbes, including bacteria and fungi, easily form stable biofilms on many surfaces. Such biofilms have high resistance to antibiotics, and cause nosocomial and postoperative infections. The antimicrobial and antiviral behaviors of Ag and Cu nanoparticles (NPs) are well known, and possible mechanisms for their actions, such as released ions, reactive oxygen species (ROS), contact killing, the immunostimulatory effect, and others have been proposed. Ag and Cu NPs, and their derivative NPs, have different antimicrobial capacities and cytotoxicities. Factors, such as size, shape and surface treatment, influence their antimicrobial activities. The biomedical application of antimicrobial Ag and Cu NPs involves coating onto substrates, including textiles, polymers, ceramics, and metals. Because Ag and Cu are immiscible, synthetic AgCu nanoalloys have different microstructures, which impact their antimicrobial effects. When mixed, the combination of Ag and Cu NPs act synergistically, offering substantially enhanced antimicrobial behavior. However, when alloyed in Ag-Cu NPs, the antimicrobial behavior is even more enhanced. The reason for this enhancement is unclear. Here, we discuss these results and the possible behavior mechanisms that underlie them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".