Design and Experimental Validation of a Bioelectronic Sensor Platform to Improve Accurate Detection of Heavy Metals in Water Using Mathematical Modeling

Heavy metal contamination, including arsenic, lead, mercury, and cadmium, poses a critical risk to public health and ecosystems. Traditional monitoring techniques are expensive and require specialized personnel, limiting large-scale deployment. This project proposes an innovative platform based on bioelectronic sensors, which combine biological elements with electrochemical transducers for rapid, low-cost detection. The methodology consists of two phases: first, the development of a dynamic mathematical model (digital twin) to predict and optimize sensor behavior under various conditions; second, experimental laboratory validation to refine model parameters. This multidisciplinary approach integrates electronic engineering, biochemistry, and automation. Expected outcomes include an optimized prototype, high-impact scientific publications, and strengthened R&D capabilities. The project directly contributes to SDG 6 (Clean Water and Sanitation), offering a sustainable technological solution for environmental monitoring and the protection of water resources.<br/><br/><b>Goal</b>: <br/>Develop a comprehensive platform for creating bioelectronic sensors to detect heavy metals in water, through dynamic mathematical modeling and experimental validation. The project aims to optimize the design and performance of these devices to improve their sensitivity, selectivity, and stability.<br/><br/><b>Research lines</b>: <br/>Organization of production and technological innovation