循环伏安法
离子液体
电极
电化学气体传感器
电化学
热重分析
安培法
材料科学
微分脉冲伏安法
扫描电子显微镜
碳糊电极
分析化学(期刊)
核化学
无机化学
化学
化学工程
有机化学
催化作用
物理化学
复合材料
工程类
作者
Fatemeh Zaeifi,Fatemeh Sedaghati,Fayezeh Samari
标识
DOI:10.1016/j.microc.2022.107969
摘要
In this study, we introduced a new carbon composite electrode modified with green synthesized CuO nanostructures (CuO-NSs) for sensitive l-cysteine determination. We applied a cost-effective and green method to synthesize CuO-NSs using Terminalia catappa leaf extract as a reducer and stabilizer without other additives. The CuO nanostructures were characterized by Ultraviolet–visible spectroscopy (UV–vis), X-ray diffraction (XRD), field emission scanning electron microscopy with energy dispersive X-ray spectroscopy (FE-SEM-EDX) and elemental mapping, transmission electron microscopy (TEM), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Electrochemical techniques such as cyclic voltammetry and amperometry were applied to investigate the electrochemical performance of the composite electrode for cysteine (l-Cys) electrooxidation. The composite electrode showed a noticeable shift in the electrooxidation peak potential of l-Cys over the conventional electrodes and a stable and sensitive electrochemical response by providing active sites and effective electron transfer pathways. Effects of various parameters such as pH of the solution, the amount of modifier, and scan rate were investigated on the electrochemical behavior of l-Cys. Under the optimal experimental conditions, the fabricated l-Cys sensor showed a linear relationship over an extensive concentration range of 10.0 µM to 5000.0 µM (R2 = 0.998) with a limit of detection (LOD) of 0.51 µM at physiological conditions (pH = 7.0). In addition, the composite electrode displayed high selectivity, good average reproducibility (RSD = 2.45 %), and applicability in the real sample on the l-Cys detection. The green, fast, simple, and cost-effective synthesis of CuO-NSs and good electrocatalytic activity of the modified electrode reveal good use of the prepared sensor to detect l-Cys.
科研通智能强力驱动
Strongly Powered by AbleSci AI