Application of Co3O4 nanocrystal/rGO for simultaneous electrochemical detection of cadmium and lead in environmental waters

纳米晶 氧化物 石墨烯 水溶液中的金属离子 电化学 材料科学 电化学气体传感器 检出限 金属 氧化钴 纳米技术 化学 电极 冶金 色谱法 物理化学
作者
Junhua You,Jingjing Li,Zhiwei Wang,Mehdi Baghayeri,Hangzhou Zhang
出处
期刊:Chemosphere [Elsevier BV]
卷期号:335: 139133-139133 被引量:81
标识
DOI:10.1016/j.chemosphere.2023.139133
摘要

Sensing of cadmium (Cd) and lead (Pb) in environmental samples is crucial for identifying potential health risks associated with exposure to these heavy metals as well as understanding the extent of heavy metal contamination in different environments and its impact on the ecosystem. The present study elucidates the development of a novel electrochemical sensor that can detect Cd (II) and Pb (II) ions simultaneously. This sensor is fabricated using reduced graphene oxide (rGO) and cobalt oxide nanocrystals (Co3O4 nanocrystals/rGO). The characterization of Co3O4 nanocrystals/rGO was done by using various analytical techniques. The incorporation of cobalt oxide nanocrystals with intense absorption properties results in an amplification of the electrochemical current generated on the surface of the sensor by heavy metals. This, when coupled with the unique properties of the GO layer, enables the identification of trace levels of Cd (II) and Pb (II) in the surrounding environment. The electrochemical testing parameters were meticulously optimized to obtain high sensitivity and selectivity. The Co3O4 nanocrystals/rGO sensor exhibited exceptional performance in detecting Cd (II) and Pb (II) within a concentration range of 0.1-450 ppb. Notably, the limits of detection (LOD) for Pb (II) and Cd (II) were found to be highly impressive at 0.034 ppb and 0.062 ppb, respectively. The Co3O4 nanocrystals/rGO sensor integrated with the SWASV method displayed notable resistance to interference and exhibited consistent reproducibility and stability. Therefore, the suggested sensor has the potential to serve as a technique for detecting both ions in aqueous samples using SWASV analysis.
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