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.
Researchers at ORNL designed a novel polymer to bind and strengthen silica sand for binder jet additive manufacturing, a 3D-printing method used by industries for prototyping and part production.
A team including researchers from the Department of Energy’s Oak Ridge National Laboratory has developed a digital tool to better monitor a condition known as Barrett’s esophagus, which affects more than 3 million people in the United States.
ORNL's Larry Baylor and Andrew Lupini have been elected fellows of the American Physical Society.
Diego Del-Castillo-Negrete, a distinguished staff member in the Fusion Energy Division, was cited for Outstanding Technical Achievement – National Laboratory. He will be recognized during the GMiS annual conference, which will be held virtually Oct. 11-22. The HENAAC awards program is in its 33rd year.
Pengfei Cao, a polymer chemist at ORNL, has been chosen to receive a 2021 Young Investigator Award from the Polymeric Materials: Science and Engineering Division of the American Chemical Society, or ACS PMSE.
Joseph Pickel has been elected a 2021 fellow of the American Chemical Society, or ACS. Pickel supports the Fusion and Fission Energy and Sciences Directorate as environment, safety and health
Kathy McCarthy, associate laboratory director for Fusion and Fission Energy and Science at the Department of Energy’s Oak Ridge National Laboratory, has been elected fellow of the American Nuclear Society for her nationally and internationally recognized leadership in nuclear energy and fusion.
The Department of Energy’s Office of Science has selected five Oak Ridge National Laboratory scientists for Early Career Research Program awards.
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.