Advancements in the detection of emerging chemical contaminants using paper microfluidics enhanced by nanomaterials and artificial intelligence

Abstract

Recent advancements in nanomaterials (NMs) and microfluidic technologies are crucial for improving the detection of chemical contaminants in food and environmental samples. Integrating NMs into microfluidic systems significantly enhances the sensitivity and selectivity of these detection methods, effectively meeting the urgent need for efficient monitoring of harmful pollutants resulting from unsustainable growth practices. This review focuses on the development of NM-based microfluidic systems specifically designed for the rapid detection of chemical pollutants. It emphasizes the use of various nanomaterials with zero-, one-, two-, and three-dimensional structures (0D, 1D, 2D, and 3D). In addition to summarizing recent research, this review presents a comparative analysis of various sensing mechanisms, namely fluorescence, colorimetric, SERS, electrochemical, and chemiluminescence, evaluating them in terms of stability, sensitivity, scalability, reproducibility, and real-world applicability. Moreover, the integration of NM-based microfluidic systems with modern technologies like artificial intelligence (AI) and the Internet of Medical Things (IoMT) has led to the progress of 5th generation smart biosensors featuring lab-on-a-chip, point-of-care, and wearable capabilities. This review highlights the critical role of these advanced systems in safeguarding food safety and environmental health by describing their unique characteristics and functionalities. Additionally, it discusses the challenges and future research directions in this important field.

Keywords: Nanomaterials, Microfluidics, Chemical contaminants, Food safety, Environmental samples, Point-of-care test