Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: Recent advances and prospects

Abstract

Environmental pollution and its drastic effects on human and animal health have urged governments to implement strict policies to minimize damage. The first step in applying such policies is to find reliable methods to detect pollution in various media, including water, food, soil, and air. In this regard, various approaches such as spectrophotometric, chromatographic, and electrochemical techniques have been proposed. To overcome the limitations associated with conventional analytical methods, microfluidic devices have emerged as sensitive technologies capable of generating high content information during the past few years. The passage of contaminant samples through the microfluidic channels provides essential details about the whole environment after detection by the detector. In the meantime, artificial intelligence is an ideal means to identify, classify, characterize, and even predict the data obtained from microfluidic systems. The development of microfluidic devices with integrated machine learning and artificial intelligence is promising for the development of next-generation monitoring systems. Combination of the two systems ensures time efficient setups with easy operation. This review article is dedicated to the recent developments in microfluidic chips coupled with artificial intelligence technology for the evolution of more convenient pollution monitoring systems.