Generative Adversarial Nets Boosted Multi-Layer Perceptron for Detection of Toxic Heavy Metal Ions Based on Visual Fluorescence Response from Carbon Nanoparticles Array

Factual failure in decimating global concerns related to toxic heavy metal ions even after a decent amount of research, calls forth an inexpensive, intelligent sensing platform for fast, accurate, and automated detection of As(III), Cd(II), Hg(II), Cr(VI), and Pb(II). The same is addressed herein through the development of a fluorescent sensor array with nine Alizarin Red S (ARS)-based fluorescent carbon nanoparticles (CNPs) through an economical and facile synthesis approach. The fluorescent response of the array with the heavy metal ions under UV-light irradiation was digitally captured to use as features in finding out an intelligent machine learning-based computer model for toxic heavy metal detection without human mentation. Seven supervised classification algorithms were used to canvass the array data set, where augmented Multi-layer Perceptron (Aug-MLP) outdid the other algorithms with the help of Generative Adversarial Nets (GANs) by artificially amplifying the data sets. Accuracy of Aug-MLP in identifying toxic heavy metal ions in laboratory samples as well as spiked river and sewage water defines the success of our approach that points towards possible automation of on-site identification. Overall, this work is a unique proof of concept of Artificial Intelligence (AI) integration with the visual sensor array extendable to other chemical entities