Katy Bradford: Cassette approach offers compelling construction solution
Scientists at Oak Ridge National Laboratory have devised a method to identify the unique chemical makeup of every lithium-ion battery around the world, information that could accelerate recycling, recover critical materials and resolve a growing waste stream.
Thirty-two Oak Ridge National Laboratory employees were named among teams recognized by former DOE Secretary Dan Brouillette with Secretary’s Honor Awards as he completed his term. Four teams received new awards that reflect DOE responses to the coronavirus pandemic.
As Hurricane Dorian raged through the Bahamas, researchers at Oak Ridge National Laboratory worked around the clock to aid recovery efforts for one of the Caribbean’s worst storms ever.
Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.
Jeff Johnson, nonproliferation research and development integration manager for ORNL’s National Security Sciences Directorate, has been honored by the American Nuclear Society
Six ORNL scientists have been elected as fellows to the American Association for the Advancement of Science, or AAAS.
ORNL has added 10 virtual tours to its campus map, each with multiple views to show floor plans, rotating dollhouse views and 360-degree navigation. As a user travels through a map, pop-out informational windows deliver facts, videos, graphics and links to other related content.
Momentum Technologies Inc., a Dallas, Texas-based materials science company that is focused on extracting critical metals from electronic waste, has licensed an Oak Ridge National Laboratory process for recovering cobalt and other metals from spent
Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.