MSE Early Career Distinguished Scholar Seminar: Rational Design of Wearable Chemical Sensors for Personalized Healthcare

Abstract: Wearable sweat sensors have the potential to revolutionize precision medicine as they can noninvasively collect molecular information closely associated with an individual's health status. However, the majority of clinically relevant biomarkers cannot be continuously detected in situ using existing wearable approaches. Molecularly imprinted polymers (MIPs) are a promising candidate to address this challenge but haven't yet gained widespread use due to their complex design and optimization process yielding variable selectivity. Here, QuantumDock is introduced, an automated computational framework for universal MIP development toward wearable applications. Using an essential amino acid phenylalanine as the exemplar, we make a QuantumDock-optimized graphene-based wearable device is designed that can perform autonomous sweat induction, sampling and sensing. We further introduce the future of wearable devices in the age of artificial intelligence.

Bio: Daniel Mukasa is a doctoral candidate at the California Institute of Technology. He is working on developing advanced biosensors and medical devices for real-time monitoring of various health parameters including biomolecular analysis and monitoring human emotions. His research interests lie at the intersection of bioelectronics, materials science and machine learning. He is interested in exploring new materials and fabrication methods for the development of wearable devices. In his work, Mukasa is applying advanced computational methods to study the behavior of materials used in medical devices, with the goal of improving their performance and reducing the time to design these complex devices.