This research focuses on the development of next-generation non-enzymatic continuous glucose monitoring technologies that enable stable, reversible, and real-time tracking of glucose concentration dynamics. The sensing platform utilizes fluorescence-based molecular recognition mechanisms driven by competitive binding interactions between glucose and engineered carbohydrate ligands within a Concanavalin A–based sensing architecture. These interactions produce glucose-dependent modulation of fluorescence signal behavior, enabling quantitative monitoring across physiologically relevant concentration ranges.
By eliminating reliance on enzymatic reactions, this approach reduces signal drift, enhances long-term sensing stability, and improves measurement reliability compared to conventional enzyme-based continuous glucose monitoring systems. The sensing chemistry is designed for integration into minimally invasive implantable or wearable device formats, supporting continuous physiological monitoring with strong biochemical specificity and rapid responsiveness to metabolic fluctuations.
Advanced optical readout strategies combined with signal processing and machine-learning-enabled analytics allow robust interpretation of dynamic glucose trends in ambulatory environments. This technology establishes a scalable framework for durable, high-accuracy metabolic monitoring platforms intended for next-generation digital health applications, chronic disease management, and precision medicine.