Updated software improves slicing for large-format 3D printing
A team of researchers at Oak Ridge National Laboratory and the University of Tennessee have developed a concrete mix that demonstrated high early strength within six hours of mixing, potentially doubling the production capacity for the precast industry.
A team of Oak Ridge National Laboratory researchers demonstrated that an additively manufactured hot stamping die – a tool used to create car body components – cooled faster than those produced by conventional manufacturing methods.
A method developed at Oak Ridge National Laboratory to print high-fidelity, passive sensors for energy applications can reduce the cost of monitoring critical power grid assets.
A new Department of Energy report produced by Oak Ridge National Laboratory details national and international trends in hydropower, including the role waterpower plays in enhancing the flexibility and resilience of the power grid.
Researchers at Oak Ridge National Laboratory developed self-healing elastomers that demonstrated unprecedented adhesion strength and the ability to adhere to many surfaces, which could broaden their potential use
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.
The annual Director's Awards recognized four individuals and teams including awards for leadership in quantum simulation development and application on high-performance computing platforms, and revolutionary advancements in the area of microbial
Four research teams from the Department of Energy’s Oak Ridge National Laboratory and their technologies have received 2020 R&D 100 Awards.
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.