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Media Contacts
Oak Ridge National Laboratory researchers have created a technology that more realistically emulates user activities to improve cyber testbeds and ultimately prevent cyberattacks.
A team of researchers at Oak Ridge National Laboratory demonstrated the ability to additively manufacture power poles from bioderived and recycled materials, which could more quickly restore electricity after natural disasters.
Oak Ridge National Laboratory researchers demonstrated their wireless charging technology on an autonomous electric vehicle for the first time in a project with Local Motors.
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.
Three technologies developed by ORNL researchers have won National Technology Transfer Awards from the Federal Laboratory Consortium. One of the awards went to a team that adapted melt-blowing capabilities at DOE’s Carbon Fiber Technology Facility to enable the production of filter material for N95 masks in the fight against COVID-19.
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.
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
Two staff members at the Department of Energy’s Oak Ridge National Laboratory have received prestigious HENAAC and Luminary Awards from Great Minds in STEM, a nonprofit organization that focuses on promoting STEM careers in underserved
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.