journal article·2020-10

Preparation and characterization of silver mesoporous silica nanoshells with promising antibacterial activity

Noha Mohamed

Journal of Porous Materials· Volume 27· Issue 5
Publisher page

Abstract

Silver nanoparticles are highly efficient antibacterial agents. The applications of silver nanoparticles in the biological environment are always obstructed by oxidation that causes aggregation of nanoparticles which lowers its antibacterial activity. In this research, silver nanoparticles were coated on the surface of mesoporous silica forming mesoporous silver nanoshells (Ag@MSNs). The prepared nanoparticles were characterized by their zeta potential, particle size, UV absorption spectroscopy, and transmission electron microscopy. Additionally, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and X-ray diffraction (XRD) were also performed. The antimicrobial activity of the prepared Ag@MSNs was evaluated against gram-positive and gram-negative pathogens. The results showed that Ag@MSNs are monodispersed with an average size distribution 272 ± 61 nm. Ag@MSNs are highly stable with zeta potential value of 43.3 ± 6.5 mV and an absorption peak at 235 nm. Small-angle X-ray diffraction demonstrated a narrow and strong peak at 2ϴ = 1.71ο indicating that the Ag@MSNs have an ordered porous structure. Surface coating of mesoporous silica nanoparticles with silver shells increased the contact surface area with the bacterial membrane. Thus, Ag@MSNs significantly inhibited the growth of Staphylococcus aureus and E-coli. In conclusion the present study gives new insights into the synthesis of antimicrobial nano-formulation that can offer a variety of uses in industry and biomedical applications.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Nanoparticles: synthesis and applications · Gold and Silver Nanoparticles Synthesis and Applications · Quantum Dots Synthesis And Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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