Cytotoxicity and Antibacterial Efficacy of AgCu and AgFe NanoAlloys: A Comparative Study
Bibliographic record
Abstract
Although Ag nanoparticles (NPs) have been widely applied in daily life and in biomedical and industrial fields, there is a demand for Ag-based bimetallic nanoalloys (NAs), such as AgCu and AgFe, due to their enhanced antibacterial efficacy and reduced Ag consumption. In this work, we present a comparison study on the antibacterial efficacy and cytotoxicity rates of Ag NPs and AgCu and AgFe NAs to L929 mouse fibroblast cells using the CCK-8 technique based on the relative cell viability. The concept of the minimum death concentration (MDC) is introduced to estimate the cytotoxicity to the cells. It is found that the minimum inhibitory concentrations (MICs) of the NPs against E. coli and S. aureus decrease with the addition of both Cu and Fe. There is a strong correlation between the MDC and MIC, implying that the mechanisms of both antibacterial efficacy and cytotoxicity are similar. The enhanced antibacterial efficacy to bacteria and cytotoxicity toward the cell are attributed to Ag+ release. The following order is found for both the MIC and MDC: AgFe < AgCu < Ag NPs. However, there is no cytotoxicity to the L929 cells for AgFe and AgCu NAs at their MIC Ag concentrations against S. aureus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".