The DOI matches its Crossref record.
Ag Nanoparticles Cluster with pH‐Triggered Reassembly in Targeting Antimicrobial Applications
Abstract
Antibacterial efficiency can be effectively improved by applying targeting antibacterial materials and strategies. Herein, the successful synthesis of uniform pH‐responsive Ag nanoparticle clusters (AgNCs) is demonstrated, which can collapse and reassemble into nonuniform Ag NPs upon exposure to the acidic microenvironment of bacterial infections. This pH triggered reassembly contributes greatly to the improved antibacterial activities of AgNCs against both methicillin‐resistant Staphylococcus aureus (MRSA) and Escherichia coli (E. coli). The minimum inhibitory concentration and minimum bactericidal concentration against MRSA are as low as 4 and 32 µg mL−1 (which are 8 and 32 µg mL−1 for E. coli), respectively. In vivo skin wound healing experiments confirm AgNCs can serve as an effective wound dressing to accelerate the healing of MRSA infection. The development of responsive AgNCs offers new materials and strategies in targeting antibacterial applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Nanoparticle protein corona: from structure and function to therapeutic targeting
Bashiri, G., Padilla, M. S., Swingle, K. L., Shepherd, S. J., Mitchell, M. J., & Wang, K. (2023) · Lab on a Chip · doi:10.1039/d2lc00799a · Cited by 296
Infection microenvironment-related antibacterial nanotherapeutic strategies
Wang, Z., Liu, X., Duan, Y., & Huang, Y. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2021.121249 · Cited by 268
Cui, H., Liu, M., Yu, W., Cao, Y., Zhou, H., Yin, J., Liu, H., Que, S., Wang, J., Huang, C., et al. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c07409 · Cited by 116
Metadata
- Authors
- Xie, X., Sun, T., Xue, J., Miao, Z., Yan, X., Fang, W., Li, Q., Tang, R., Lu, Y., Tang, L., Zha, Z., & He, T.
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2020-02-24
- Volume
- 30
- Issue
- 17
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W3007419295
- Cited by
- 197
- References
- 0
- FWCI
- 11.65
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Ag Nanoparticles Cluster with pH‐Triggered Reassembly in Targeting Antimicrobial Applications. https://app.cochat.ai/papers/10.1002/adfm.202000511
- The paper
- Xie, X., Sun, T., Xue, J., Miao, Z., Yan, X., Fang, W., Li, Q., Tang, R., Lu, Y., Tang, L., Zha, Z., & He, T. (2020). Ag Nanoparticles Cluster with pH‐Triggered Reassembly in Targeting Antimicrobial Applications. Advanced Functional Materials. https://doi.org/10.1002/adfm.202000511
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.