Ten scientists from the Department of Energy’s Oak Ridge National Laboratory are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.
Improved data, models and analyses from ORNL scientists and many other researchers in the latest global climate assessment report provide new levels of certainty about what the future holds for the planet
Four first-of-a-kind 3D-printed fuel assembly brackets, produced at the Department of Energy’s Manufacturing Demonstration Facility at Oak Ridge National Laboratory, have been installed and are now under routine operating
A research team led by Oak Ridge National Laboratory bioengineered a microbe to efficiently turn waste into itaconic acid, an industrial chemical used in plastics and paints.
Researchers at Oak Ridge National Laboratory have identified a statistical relationship between the growth of cities and the spread of paved surfaces like roads and sidewalks. These impervious surfaces impede the flow of water into the ground, affecting the water cycle and, by extension, the climate.
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.
Planning for a digitized, sustainable smart power grid is a challenge to which Suman Debnath is using not only his own applied mathematics expertise, but also the wider communal knowledge made possible by his revival of a local chapter of the IEEE professional society.
The Transformational Challenge Reactor, or TCR, a microreactor built using 3D printing and other new advanced technologies, could be operational by 2024.
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.