Evaluating the Efficacy of 2D Material Biosensors Coupled with AI for Water Quality Monitoring

Abstract Clean and safe water is very crucial for public health and environmental sustainability. The traditional methods are not beneficial due to delayed results, high operational costs and less sensitivity to identify the contaminants present in water. Recent developments in nanotechnology and artificial intelligence (AI) have introduced intelligent and responsive water quality monitoring systems. 2D materials exhibits exceptional properties that can be used in ultra sensible detection of heavy metals and other organic pollutants. They also offer excellent biocompatibility and are used to create efficient biosensors. This study explores the efficacy of biosensors based on 2D materials like graphene, MXene, TMDs, MoS2, 2D phosphorous and boron nitride for real time detection of organic pollutants in water. These biosensors when combined with AI models including machine learning and deep learning, results in real time processing, predictive analysis and enhancing their utility in complex water systems. Different techniques of machine learning are suggested that how these approaches are enhancing the accuracy and scalability of water quality assessment.