开发超氧化物(O2•-)和一氧化氮(NO)阴离子的定量生物传感器对于病理学研究至关重要。截至今天,电化学检测的主要挑战是开发高选择性的纳米模拟材料来替代天然酶。在这项研究中,通过溶剂热策略成功合成了银有机骨架(Ag-MOF)的树枝状形态结构。由于聚合物复合材料的引入导致改善的导电性和催化活性,这促进了传质并导致更快的电子效率。为了监测O2•-和NO的电化学信号,Ag-MOF电极基板是通过滴涂生产的,和复合材料是通过循环伏安电位循环设计的。设计的电极基板显示出高灵敏度,宽线性浓度为1nM-1000μM和1nM-850μM,和对O2·-和NO的低检测限为0.27nM和0.34nM(S/N=3)。除此之外,传感器成功监测了细胞释放的O2·-,和NO来自HepG2和RAW264.7活细胞,并有潜力监测外源性NO从二乙胺(DEA)-NONO和硝普钠(SNP)的供体释放。此外,所开发的系统用于实际生物流体样品中的O2·-和NO的分析,结果令人满意(94.10-99.57±1.23%)。所设计的系统提供了一种新颖的方法来获得具有高度选择性的良好电化学生物传感器平台,稳定,灵活。最后,所提出的方法提供了一种定量的方法来跟踪生物系统中O2·-和NO的动态变化。
Developing quantitative biosensors of superoxide (O2•-) and nitric oxide (NO) anion is crucial for pathological research. As of today, the main challenge for electrochemical detection is to develop high-selectivity nano-mimetic materials to replace natural enzymes. In this study, the dendritic-like morphological structure of silver organic framework (Ag-MOF) was successfully synthesized via a solvothermal strategy. Owing to the introduction of polymeric composites results in improved electrical conductivity and catalytic activity, which promotes mass transfer and leads to faster electron efficiency. For monitoring the electrochemical signals of O2•- and NO, the Ag-MOF electrode substrate was produced by drop-coating, and composites were designed by cyclic voltammetric potential cycles. The designed electrode substrates demonstrate high sensitivity, wide linear concentrations of 1 nM-1000 μM and 1 nM-850 μM, and low detection limits of 0.27 nM and 0.34 nM (S/N = 3) against O2•- and NO. Aside from that, the sensor successfully monitored the cellular release of O2•-, and NO from HepG2 and RAW 264.7 living cells and has the potential to monitor exogenous NO release from donors of Diethylamine (DEA)-NONOate and sodium nitroprusside (SNP). Additionally, the developed system was applied to the analysis of O2•- and NO in real biological fluid samples, and the results were good satisfactory (94.10-99.57 ± 1.23%). The designed system provides a novel approach to obtaining a good electrochemical biosensor platform that is highly selective, stable, and flexible. Finally, the proposed method provides a quantitative way to follow the dynamic changes in O2•- and NO in biological systems.