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Abstract The ionic form of Arsenic As3+ is the most toxic, causing severe health problems like cancer, cardiovascular diseases, and developmental disorders. Therefore, researchers have contributed significantly to its removal and detection work. However, there is still a need for accurate, trustworthy, and sensitive methods that may be employed at field sites for detecting Arsenic ions at nano levels. In the present study, we report a novel and robust electrochemical sensing platform for the selective and sensitive detection of As 3+ ions in aqueous samples. The method combines the high sensitivity of stripping voltammetry with the data analytics and power of Chemometric modelling to achieve superior performance. Optimising key instrumental parameters such as deposition potential, deposition time, and scan rate improves the analytical response. To address potential interferences from co-existing ions, a Chemometric modelling approach based on principal component analysis (PCA) is employed to ensure the selection of predictor variables. This multivariate technique effectively discriminates As(III) signals from background noise and interfering species, enhancing selectivity and accurate quantification. The proposed method demonstrates a threshold limit and sensitivity of 0.4nM and 176.34µA/µM, for Arsenic, which is less than the World Health Organization (WHO) permissible limit in drinking water. Furthermore, the method shows good repeatability and stability, making it suitable for real-world water analysis applications. This novel sensing platform offers a promising alternative for sensitive, selective, and robust As(III) detection in water samples, contributing to improved water quality monitoring and public health protection.
Bansod et al. (Thu,) studied this question.