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Development of neural network based numerical platform for nanoplastics discrimination in water
Summary
This study developed a neural network-based electrochemical sensor platform for detecting and discriminating nanoplastics in water. Silver nanoparticles bound to a glassy carbon electrode served as the sensing element, with machine learning used to interpret the electrochemical signals.
Electrochemical sensors could be employed as digital equipment and systems to measure samples. As a result, computational modeling is necessary as well as experimental results. In the current work, our electrochemical sensor is fabricated by the binding of silver nanoparticles (AgNps) to glassy carbon electrode (GCE) combined with the multilayer perceptron (MLP) neural network. The sensitivity of our sensor, also the repeatability, the reproducibility, and the stability were all demonstrated, with minimal preparation cost. The developed electrochemical sensor was also applied to determine phenolic compounds in real samples of mineral and tap water. The MLP model was created using the findings of the experimental studies. The current and the concentration of each species, were the input and output parameters, respectively. The results of MLP modeling were consistent with the studies, indicating that it could be effective in electrochemical sensor technology.