Construction of Er-doped ZnO/SiO2 composites with enhanced antimicrobial properties and analysis of antibacterial mechanism
Source abstract
Abstract
Abstract Herein, silica carrier was used as underlying structure to prepare composite material loaded with rare earth element Er and Zn. Rare earth elements can improve antimicrobial effects of ZnO due to their specific electronic structure. Er–ZnO/SiO2 hybrid antibacterial material was prepared through sol-gel method and its structure and morphology were characterized by scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, inductively coupled plasma emission spectroscopy and Brunauer-Emmett-Teller measurements. E. coli and S. aureus were selected as model bacteria to assess antibacterial activity of prepared hybrid material by plate coating method. Er–ZnO/SiO2 exhibited good antibacterial activity towards E. coli and S. aureus. Increase in Er3+ concentration from 0.12% to 1.10% led to increase in antibacterial performance followed by subsequent decrease. Improving effect of Er relied on the molar ratio of Er doped in ZnO/SiO2 hybrid material. The optimal sample was found to be 0.60%Er–ZnO/SiO2, with antibacterial rates of 93.71% and 70.46% against E. coli and S. aureus, respectively. Antibacterial mechanism was assessed by fluorescence detection of reactive oxygen species. In addition, flame atomic absorption spectrometry was used to measure the amount of released Zn2+. Results also showed that 0.60%Er–ZnO/SiO2 hybrid material generated more reactive oxygen species, released more Zn2+ ions, and had the largest surface area, which improved its antibacterial rate. Thus, Er enhanced antibacterial properties of ZnO/SiO2, providing these composite materials with great potential as antibacterial products.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanoparticles: synthesis and applications · Bone Tissue Engineering Materials · Graphene and Nanomaterials Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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