Size optimisation of silver nanoparticles synthesised by gallic acid using the response surface methodology
Size optimisation of silver nanoparticles synthesised by gallic acid using the response surface methodology
复制标题
使用响应面法优化没食子酸合成的银纳米粒子的尺寸
DOI：
10.1049/mnl.2019.0780
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发表时间：
2020
影响因子：
1.3
通讯作者：
M. Khatibzadeh
中科院分区：
文献类型：
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作者：
M. Ahani;M. Khatibzadeh
这项工作报告了使用没食子酸（GA）从硝酸银（AgNO3）合成银纳米粒子（AgNP）的绿色方法，不使用危险化学品。通过基于中心复合设计的响应面法，在三个水平上优化AgNO3浓度、GA浓度和pH等变量对AgPs平均粒径的影响，以获得所需的响应，即最小平均粒径。每个实验中 AgNP 的形成均通过紫外可见 (UV-vis) 光谱进行表征，并通过动态光散射 (DLS) 测量平均粒径。为了评估因素对响应的显着性及其定量影响，进行了方差分析。结果表明，pH 值是影响响应的最有效因素。在优化条件（5.42 mM AgNO3、6.25 mM GA、pH = 9.02）下合成的 AgNP 通过 X 射线衍射、DLS、场发射扫描电子显微镜、能量色散 X 射线、透射电子显微镜、紫外可见光谱和傅里叶变换红外光谱进行表征。在DLS的基础上，得到AgNPs的平均粒径为8 nm，与模型预测值（7.51 nm）吻合良好。
This work reports the synthesis of silver nanoparticles (AgNPs) from Ag nitrate (AgNO3) using the gallic acid (GA) as a green methodology without utilisation of hazardous chemicals. The effects of variables such as AgNO3 concentration, GA concentration, and pH on the average particle size of AgPs were optimised through response surface methodology based on the central composite design at three levels to obtain the desired response, i.e. minimum average particle size. The formation of AgNPs at each experiment was characterised by ultraviolet–visible (UV–vis) spectroscopy and the average particle size was measured by dynamic light scattering (DLS). To evaluate the significance of factors on the response and their quantitative effects, analysis of variance was carried out. The results indicated that the pH was the most effective factor on the response. The AgNPs synthesised at optimised conditions (5.42 mM of AgNO3, 6.25 mM of GA, and pH = 9.02) were characterised by X-ray diffraction, DLS, field-emission scanning electron microscope, energy dispersive X-ray, transmission electron microscopy, UV–vis spectroscopy, and Fourier-transform infrared spectroscopy. On the basis of the DLS, the average particle size of AgNPs obtained 8 nm, which was in satisfactory agreement with the predicted value (7.51 nm) by model.