Nanomaterial-enabled sensing of paraquat in food and environmental matrices: mechanisms, materials, and analytical challenges.
Abstract
Paraquat (PQ), a highly toxic herbicide banned in several countries yet still widely used globally, poses severe threats to human health and ecosystems through acute poisoning and chronic exposure via contaminated food and water. Despite numerous analytical methods, rapid, sensitive, and field-deployable detection remains essential for effective monitoring and regulatory enforcement. Unlike existing reviews focusing narrowly on specific nanomaterials or detection techniques, this review represents a systematic cross-platform comparison evaluating metallic nanoparticles (Au, Ag, Pt), carbon-based materials (graphene, carbon nanotubes, quantum dots), metal-organic frameworks, and hybrid nanocomposites across electrochemical detection mechanisms. Critically, this review examines how food and environmental matrix effects influence sensor performance and discusses the analytical validation requirements necessary for reliable paraquat determination, thereby addressing a major gap in studies that predominantly report performance under idealized buffer conditions. Novel enhancement strategies are evaluated, including molecularly imprinted polymers, aptamer functionalization, and disposable electrode integration for point-of-use testing. This review uniquely addresses the translational research gap by identifying why promising laboratory sensors fail in real-world applications, including challenges associated with matrix interference, selectivity, long-term stability, reproducibility, and practical validation based on reports over the past decade. Furthermore, economic feasibility and lifecycle aspects of nanomaterial-enabled sensing platforms are critically discussed to evaluate their potential for deployment in resource-limited settings. Thus, the review provides clear guidance for developing next-generation sensors capable of protecting public health through effective paraquat monitoring.