Mechanoelectric Metamaterials for High-Sensitivity, Broad-Range Flexible Sensors

Flexible pressure sensors are critical components of electronic skins (E-skins) that emulate the human somatosensory system by transducing physiological stimuli into electrical signals. To effectively mimic the pressure-sensing capabilities of human skin, these sensors must achieve high sensitivity across a wide pressure range. However, the tradeoff between sensitivity and operational range in current sensors has hindered the practical design of flexible pressure sensors in healthcare and wearable electronics, posing a significant barrier to advancing E-skin technology. This proposal seeks to overcome this fundamental limitation by converging computational modeling, 3D printing, and artificial intelligence/machine learning to establish a close-loop framework for sensor innovation: physics-based models guide metamaterial design, additive manufacturing enables precise fabrication, and data-driven algorithms decode complex electromechanical signatures for real-time intelligence. If successful, this project will generate transformative knowledge at the intersection of soft active materials, programmable metastructures, and adaptive sensing systems, and inspire new methodologies for developing intelligent devices. This project will develop a new class of 3D-printable metamaterials for piezoelectric pressure sensors with high sensitivity, broad detection range, and self-powered characteristics, combined with mechanical flexibility and durability. Central to this innovation are gradient lattice structures with programmable stiffness and grafted molecular ferroelectrics. To realize this goal, the work has three aims. Aim 1 will develop multiscale electro-mechanical models to design and predict the piezoelectric responses of the metamaterial sensors. Aim 2 will fabricate the metamaterial sensors by 3D printing and characterize the sensitivity, detection range, dependence of piezoelectric voltage on stress, mechanical flexibility, and durability of the metamaterial sensors. Aim 3 will integrate the multi-mode sensing capacities into a robotic finger and enhance its sensing capacity and intelligence through advanced machine-learning algorithms. The expected outcome is to create a versatile sensor design paradigm by the synergistic integration of molecular engineering and metamaterials to reconcile conflicting performance demands. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.