Ken Kingery, senior science communications specialist, Duke University
Machine learning (ML) approach opens insights into an entire class of materials being pursued for solid-state batteries by Duke University researchers.
Researchers from Duke University are applying lessons learned from decades of perfecting eye-imaging technologies to tomorrow’s autonomous systems sensor technologies.
Duke University researchers have developed insights into the atomistic dynamics of emerging solid-state batteries to speed their evolution and move beyond lithium.
An atomic mechanism that makes some thermoelectric materials efficient near high-temperature phase transition could help unlock better options for technologies reliant on transforming heat into electricity.
Duke University researchers have developed an approach to designing motion plans for multiple robots grows "trees" in the search space to solve complex problems in a fraction of the time.
Duke University electrical engineers are using machine learning to design dielectric metamaterials that absorb and emit specific frequencies of terahertz radiation, which could create new, sustainable types of thermal energy harvesters and lighting.
The Duke-led GUIde Consortium develops faster, more accurate simulations of turbine blade vibrations to help aeronautical engineers develop safer jet turbines with lower maintenance costs.