| Abstract Scope |
Interfacing electronic or robotic components with biological systems is extremely challenging due to their fundamentally contradictory properties. At MSU Lin Lab, we leverage theory-guided and AI-accelerated polymer-network design, exploiting high-performing soft materials as an ideal material candidate to form long-term, high-efficacy, multi-modal interfaces between electronic or robotic components and biological systems. In this talk, I will first present our computational and experimental efforts to understand the mechanics of polymer networks with slipping cross-links, which redefines the mechanical properties of soft materials. I will then introduce a new class of vision-based tactile robots that integrate molecular design of fatigue-resistant photoelastic gels, mechanical design of stress-interpreting photometry systems, and algorithm design of physics-informed machine learning, to enable perception, visualization, and interpretation of robot-tissue interactions. Finally, I will highlight the development of an electrical tissue adhesive that enables rapid and robust integration of electrochemical biosensors with biological tissues, facilitating strain-insensitive physiological monitoring. |