PDF(5481 KB)
B、N和F共掺杂的新型碳点对硝唑类抗生素的检测(英文)
贾燕春, 胡军辉, 宋胜梅, 双少敏, 董川
PDF(5481 KB)
PDF(5481 KB)
B、N和F共掺杂的新型碳点对硝唑类抗生素的检测(英文)
以氟硼酸钠、柠檬酸、乙胺和乙腈为前驱体,通过一步溶剂热法制备了硼、氮、氟共掺杂的蓝色荧光碳点(Blue Fluorescent Carbon Dots, BNF-CDs),分别利用透射电子显微镜、傅里叶变换红外光谱、X射线光电子能谱、紫外可见吸收和荧光光谱等对合成的BNF-CDs进行了结构、形貌及光学性能表征。结果表明,BNF-CDs具有稳定优异的光学性能,荧光量子产率高达65.9%。基于内滤作用,硝唑类抗生素可有效猝灭BNF-CDs的荧光,为甲硝唑类抗生素(Metronidazole, MTR)、奥硝唑类抗生素(Ornidazole, ORN)和替硝唑类抗生素(Tinidazole, TIN)的检测提供了一种新的荧光检测方法,并建立了相应的硝基咪唑类抗生素的检测曲线,线性范围分别为6 ~250 μmol/L、3.6 ~189 μmol/L和3.6 ~295 μmol/L,检出限分别为16 nmol/L、35 nmol/L、71 nmol/L。此外,对实际样品中MTR、ORN和TIN的测定也取得了满意的回收率(96.4%~105.8%),表明了该方法潜在的应用前景。
Blue fluorescent carbon dots (BNF-CDs) co-doped with boron, nitrogen and fluorine were prepared by a one-step solvothermal method using sodium fluoroborate, citric acid, ethylamine and acetonitrile as precursors. Herein, the structure, morphology and optical properties of BNF-CDs were characterized by transmission electron microscopy, Fourier transform infrared spectrometer, X-ray photoelectron spectroscopy, UV-Vis absorption and fluorescence spectroscopy, respectively. The results illustrated that the BNF-CDs have stable and excellent optical properties with a high fluorescence quantum yield of 65.9%. Based on the internal filtration effect between BNF-CDs and nitroimidazole antibiotics, the fluorescence of BNF-CDs can be effectively quenched. This phenomenon provides a novel fluorescence method for the determination of metronidazole (MTR), ornidazole (ORN) and tinidazole (TIN), and the corresponding detection curves for these nitroimidazole antibiotics were established, with linear ranges of 6-250 μmol/L for MTR, 3.6-189 μmol/L for ORN and 3.6-295 μmol/L for TIN. The detection limits were found to be 16 nmol/L, 35 nmol/L and 71 nmol/L, respectively. In addition, satisfactory recovery rates (rangihg from 96.4% to 105.8%) have been achieved for determination of MTR, ORN and TIN in actual samples,which indicates its potential application in the future.
nitroimidazole antibiotics / carbon dots / internal filtration effect / multifunctional detection
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