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Media Contacts
An all-in-one experimental platform developed at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences accelerates research on promising materials for future technologies.
Joe Hagerman, ORNL research lead for buildings integration and controls, understands the impact building technology innovations can have during times of crisis. Over a decade ago, he found himself in the middle of one of the most devastating natural disasters of the century, Hurricane Katrina.
Scientists seeking ways to improve a battery’s ability to hold a charge longer, using advanced materials that are safe, stable and efficient, have determined that the materials themselves are only part of the solution.
The 75th anniversary of the final voyage of the USS Indianapolis and her brave crew is Thursday, July 30. The US Navy warship was on a top-secret mission across the Pacific Ocean to deliver war materials that marked the conclusion of the Manhattan Project.
A team led by Dan Jacobson of Oak Ridge National Laboratory used the Summit supercomputer at ORNL to analyze genes from cells in the lung fluid of nine COVID-19 patients compared with 40 control patients.
From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.
Scientists at Oak Ridge National Laboratory have demonstrated a direct relationship between climate warming and carbon loss in a peatland ecosystem.
Five researchers at the Department of Energy’s Oak Ridge National Laboratory have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
Scientists at ORNL used neutron scattering and supercomputing to better understand how an organic solvent and water work together to break down plant biomass, creating a pathway to significantly improve the production of renewable
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.