Stanford inventors have developed a rechargeable, fluid-based shock absorber material for use in space constrained environments. Foam is the most common form of shock absorption material, but its force exerted is proportional to the degree of displacement.
Scientists in the Zhenan Bao Research Group at Stanford developed a process for direct photo-patterning of electronic polymers that improves device density of elastic circuits over 100x.
The Zhenan Bao Research Group at Stanford University developed and manufactured a photo-curable, directly patternable, stretchable, and highly conductive polymer that is ideal for bioelectronic applications, and stretchable electronic devices.
The Zhenan Bao Research Group at Stanford University has designed an intrinsically stretchable polymeric matrix that allows seamless integration with physically crosslinked PEDOT:PSS, while stabilizing its high stretchability, and high conductivity after all necessary fabricat
Stanford engineers have developed an optical modulator to enable low-cost and high spatial-resolution time-of-flight imaging and LiDAR with low-cost standard image sensors.
Researchers at Stanford have developed a highly efficient (>90%) holographic beam steering method for obtaining distance information of objects nearby, with applications from autonomous vehicles to home appliances.
A team of Stanford computer scientists have developed software that can serve as a key enabling technology for location-aware services indoors. Location-aware services are an important emerging technology for mobile devices.
Optimizing battery performance currently relies on empirical testing using arbitrary parameters, under-validated physiochemical models, and limited data analysis of summary trends.
Stephen Tsai and researchers at Stanford University's Structures and Composite Laboratory have designed a composite grid-stiffened skin structure, which is ultra-lightweight, stiff, strong, and easier and less expensive to manufacture.
Stanford researchers have developed a time efficient and safer algorithm for autonomous cars that combines game theory and risk awareness. This algorithm computes approximate feedback Nash equilibria where all agents are risk aware, a novel approach.
Stanford researchers at the Camarillo Lab have developed a neural-network based model that can provide real-time calculation of brain strain based on instrumented mouthguard kinematics signals.