Development of Cost-Effective Sensor for Simultaneous Determination of Nanoplastics Using Artificial Neural Network

Given the significance of water in our lives, it has become imperative to protect its quantity and, especially, its quality. One of the substances in this area that causes the most worry is nanoplastics because of its propensity to linger in the environment for a long time. As they build up, they exert hazardous effects, and even at low concentrations, they are detrimental to both human and animal health. Even more recently, they were acknowledged to be carcinogenic agents. Therefore, it has become crucial for water monitoring to create a novel electrochemical sensor that can simultaneously discriminate nanoplastics in water, primarily bisphenol A (BPA), phenol (Phe), and catechol (CC). In this work, a new electrochemical sensor for BPA, Phe, and CC detection consisting of AgNPs modified glassy carbon electrode (GCE) has been developed. Under the best experimental conditions, the green AgNPs/GCE sensor exhibits high sensitivity and selectivity for individual and simultaneous detection of phenolic compounds (PCs), with a detection limit of 0.147, 0.131, and $0.126 ~\mu ext{M}$ , respectively, for BPA, Phe, and CC. The present electrochemical sensor has been approved for testing on real water samples. Starting with the results obtained by our electrochemical study, we have trained the multilayer perceptron (MLP) network based on the back-propagation (BP) algorithm. BPA, Phe, and CC currents were introduced into the network as input parameters and their concentrations as the outputs. The outcomes of the MLP modeling matched the experiments well, which indicates its worthwhile application in electrochemical sensor technology.