Oak Ridge National Laboratory scientists led the development of a supply chain model revealing the optimal places to site farms, biorefineries, pipelines and other infrastructure for sustainable aviation fuel production.
Oak Ridge National Laboratory scientists exploring bioenergy plant genetics have made a surprising discovery: a protein domain that could lead to new COVID-19 treatments.
A new report published by ORNL assessed how advanced manufacturing and materials, such as 3D printing and novel component coatings, could offer solutions to modernize the existing fleet and design new approaches to hydropower.
Researchers at Oak Ridge National Laboratory have identified a key need for future hydropower innovations – full-scale testing – to better inform developers and operators before making major investments.
Researchers at Oak Ridge National Laboratory are using a novel approach in determining environmental impacts to aquatic species near hydropower facilities, potentially leading to smarter facility designs that can support electrical grid reliability.
Oak Ridge National Laboratory and collaborators have discovered that signaling molecules known to trigger symbiosis between plants and soil bacteria are also used by almost all fungi as chemical signals to communicate with each other.
Researchers at Oak Ridge National Laboratory were part of an international team that collected a treasure trove of data measuring precipitation, air particles, cloud patterns and the exchange of energy between the atmosphere and the sea ice.
Scientists from Oak Ridge National Laboratory used high-performance computing to create protein models that helped reveal how the outer membrane is tethered to the cell membrane in certain bacteria.
To better determine the potential energy cost savings among connected homes, researchers at Oak Ridge National Laboratory developed a computer simulation to more accurately compare energy use on similar weather days.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.