Correlating the Atomic Structure of Bimetallic Silver–Gold Nanoparticles to Their Antibacterial and Cytotoxic Activities
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) have gained much attention in biomedical research because of their antibacterial properties. However, they have also exhibited cytotoxicity toward certain mammalian cells. In order to improve therapeutic efficacy, the incorporation of gold (Au) and Ag into bimetallic Ag–Au NPs is a promising strategy, as it has the potential to increase biocompatibility and maintain antibacterial activity. Toward this end, we prepared a series of bimetallic Ag–Au NPs and studied them with X-ray absorption spectroscopy (XAS) in order to elucidate the correlation of atomic structure to their bioactivities. The addition of Au was found to drastically change the atomic structure of the Ag NPs; namely, the Ag core of the NPs was gradually replaced with Au, while Ag was found mostly on the surface. Next, NP antibacterial activity toward S. aureus and cytotoxicity toward NIH-3T3 fibroblast cells were assessed. It was found that the antibacterial activity of the bimetallic NPs was lower than pure Ag NPs and dependent on the Ag location within the NPs. On the other hand, the cytotoxicity of bimetallic NPs was much lower than the pure Ag NPs and dependent on the overall Au concentration. Using the structural information garnered from XAS, we were able to rationalize the bioactivity results of the NPs based on their atomic structure and provide guiding principles to design Au–Ag NPs with balanced antibacterial and cytotoxic activities. This work represents an important step toward engineering the atomic structure of bimetallic Au–Ag NPs for biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".