Impact of Protecting Ligands on Surface Structure and Antibacterial Activity of Silver Nanoparticles
Bibliographic record
Abstract
Silver nanoparticles (Ag NPs) have attracted much attention in the past decade because of their unique physicochemical properties and notable antibacterial activities. In particular, thiol-protected Ag NPs have come to the forefront of metal nanoparticle studies, as they have been shown to possess high stability and interesting structure-property relationships. However, a clear correlation between thiol-protecting ligands, the resulting Ag NP surface structure, and their antibacterial properties has yet to be determined. Here, a multielement (Ag and S), multi-edge (Ag K-edge, Ag L3-edge, S K-edge) X-ray absorption spectroscopy (XAS) methodology was used to identify the structure and composition of Ag NPs protected with cysteine. XAS characterization was carried out on similar-sized Ag NPs protected with poly(vinylpyrrolidone) (PVP), in order to provide a valid comparison of the ligand effect on surface structure. The PVP-Ag NPs showed a metallic Ag surface and composition, consistent with metal NPs protected by weak protecting ligands. On the other hand, the Cys-Ag NPs exhibited a distinct surface shell of silver sulfide, which is remarkably different than previously studied Cys-Ag NPs. The minimum inhibitory concentration (MIC) of both types of Ag NPs against Gram-positive (+) and Gram-negative (-) bacteria were tested, including Staphylococcus aureus (+), Escherichia coli (-), and Pseudomonas aeruginosa (-). It was found that the MICs of the Cys-Ag NPs were significantly lower than the PVP-Ag NPs for each bacteria, implicating the influence of the sulfidized surface structure. Overall, this work shows the effect of ligand on the surface structure of Ag NPs, as well as the importance of surface structure in controlling antibacterial activity.
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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".