Development of a Platform for Bioelectronic Sensors in Heavy Metal Detection in Water: From Mathematical Modeling to Experimental Validation

Heavy metal contamination, including arsenic, lead, mercury, and cadmium, poses a critical risk to public health and ecosystems. Traditional monitoring techniques are costly and limit real-time detection. This project proposes an innovative solution by developing a bioelectronic sensor platform, integrating a digital twin based on dynamic mathematical models to predict and optimize sensor behavior before physical construction. The methodology spans from literature review and first-principles modeling to computational simulation and experimental laboratory validation. This multidisciplinary approach allows for the identification of critical factors affecting sensitivity and selectivity, reducing development costs and accelerating technological implementation. Expected results include an optimized prototype, high-impact scientific publications, and knowledge transfer between international institutions. The project not only contributes to SDG 6 (Clean Water and Sanitation) but also strengthens research capabilities in bioelectronic sensors, promoting sustainable and scalable solutions for environmental monitoring and the protection of water resources.<br/><br/><b>Goal</b>: <br/>Develop a comprehensive platform for bioelectronic sensors through dynamic mathematical modeling and experimental validation for the precise detection of heavy metals in water. 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/>Educational entertainment (Edutainment)