Sustainable and smart nano-biosensors: Integrated solutions for healthcare, environmental monitoring, agriculture, and food safety

The demand for highly sensitive and specific biosensors is growing rapidly across multiple sectors, including medical diagnostics, environmental monitoring, pathogen detection, and food safety. This review critically explores the advancements in nano-enabled biosensor technologies, focusing on bio-based nanosensors synthesized using plant-derived phytochemicals, cellulose, and lignin. These naturally derived materials serve as eco-friendly stabilizing and reducing agents for the formation of functional nanoparticles (NPs). This review (i) discusses the synthesis and characterization techniques of bio-nanosensors; (ii) examines their physical, chemical, and biological interactions; and (iii) evaluates their applications in early disease screening, diagnostics (e.g., cancer and diabetes), and the detection of pathogens, pollutants, and toxins. In addition, we address key challenges associated with bio-based nanomaterials, including NP toxicity, biocompatibility, and the need for appropriate regulatory frameworks. Furthermore, we highlight the growing role of artificial intelligence, machine learning, and the Internet of Things in enhancing biosensor performance and commercialization. This review concludes by emphasizing the potential of bio-based nanosensors for clinical, environmental, and food safety applications, while advocating the development of micro- and disposable sensor formats. By highlighting the potential and limitations of bionanosensors, this review provides a foundation for future research and the development of clinical and commercial applications. • Phytochemical-based nanosensors are eco-friendly tools for sustainable biosensor designs and applications. • Improved synthesis of plant nanobiosensors increases their sensitivity, specificity, and detection efficiency. • Bio-nanosensors enable early disease diagnosis and detection of pathogens and environmental toxins. • AI, IoT, and machine learning integration improve the biosensor accuracy and real-time data analysis. • Challenges include toxicity, biocompatibility, and lack of clear regulations for commercialization.