Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.
Alex Roschli is no stranger to finding himself in unique situations. After all, the early career researcher in ORNL’s Manufacturing Systems Research group bears a last name that only 29 other people share in the United States, and he’s certain he’s the only Roschli (a moniker that hails from Switzerland) with the first name Alex.
A residential and commercial tower under development in Brooklyn that is changing the New York City skyline has its roots in research at the Department of Energy’s Oak Ridge National Laboratory.
Oak Ridge National Laboratory’s latest Transportation Energy Data Book: Edition 37 reports that the number of vehicles nationwide is growing faster than the population, with sales more than 17 million since 2015, and the average household vehicle travels more than 11,000 miles per year.
Scientists at ORNL, Drexel University and their partners have discovered a way to improve the energy density of promising energy-storage materials, conductive two-dimensional ceramics called MXenes.
The use of lithium-ion batteries has surged in recent years, starting with electronics and expanding into many applications, including the growing electric and hybrid vehicle industry. But the technologies to optimize recycling of these batteries have not kept pace.
Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.
A University of South Carolina research team is investigating the oxygen reduction performance of energy conversion materials called perovskites by using neutron diffraction at Oak Ridge National Laboratory’s Spallation Neutron Source.
Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.