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Effective antimicrobial activity of ZnO and Yb-doped ZnO nanoparticles against Staphylococcus aureus and Escherichia coli
Biomaterials Advances ( IF 7.9 ) Pub Date : 2021-03-02 , DOI: 10.1016/j.msec.2021.112004
Diego Eloyr Navarro-López , Rebeca Garcia-Varela , O. Ceballos-Sanchez , A. Sanchez-Martinez , Gildardo Sanchez-Ante , Kaled Corona-Romero , D.A. Buentello-Montoya , Alex Elías-Zuñiga , Edgar R. López-Mena

Nanostructured Zn1-xYbxO (0.0 ≤ x ≤ 0.1) powders were prepared by the solution method using polyvinyl alcohol (PVA) and sucrose. The effect of the ytterbium doping content on the structural, morphological, optical and antimicrobial properties was analyzed. X-ray diffraction (XRD) analysis revealed that the hexagonal wurtzite structure was retained, and no secondary phases due to doping were observed. The crystallite size was under 20 nm for all the Zn1-xYbxO (0.0 ≤ x ≤ 0.1) powders. The optical band gap was calculated, and the results revealed that this value increased with the ytterbium content, and the Eg values varied from 3.06 to 3.10 eV. The surface chemistry of the powders was analyzed using X-ray photoelectron spectroscopy (XPS), and the results confirmed the oxidation state of ytterbium as 3+ for all the samples. Zn1-xYbxO (0.0 ≤ x ≤ 0.1) nanoparticles were tested as antimicrobial agents against Staphylococcus aureus and Escherichia coli, resulting in a potential antimicrobial effect at most of the tested concentrations. These results were used in an artificial neural network (ANN). The results showed that it is possible to generate a model capable of forecasting the absorbance with good precision (error of 1–2%).



中文翻译:

ZnO和掺Yb的ZnO纳米颗粒对金黄色葡萄球菌大肠杆菌的有效抗菌活性

使用聚乙烯醇(PVA)和蔗糖通过溶液法制备纳米结构的Zn 1-x Yb x O(0.0≤x≤0.1)粉末。分析了do掺杂量对结构,形态,光学和抗菌性能的影响。X射线衍射(XRD)分析显示六方纤锌矿结构得以保留,并且未观察到由于掺杂而引起的第二相。所有Zn 1-x Yb x O(0.0≤x≤0.1)粉末的微晶尺寸均在20 nm以下。计算了光学带隙,结果表明该值随y含量和E g的增加而增加。值从3.06到3.10 eV不等。使用X射线光电子能谱(XPS)分析了粉末的表面化学性质,结果证实所有样品的of的氧化态均为3+。测试了Zn 1-x Yb x O(0.0≤x≤0.1)纳米颗粒作为抗金黄色葡萄球菌大肠杆菌的抗菌剂,在大多数测试浓度下均具有潜在的抗菌作用。这些结果被用于人工神经网络(ANN)。结果表明,可以生成一个能够精确预测吸光度的模型(误差为1-2%)。

更新日期:2021-03-09
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