In experiment after experiment, the synthetic radioisotope actinium-225 has shown promise for targeting and attacking certain types of cancer cells.
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.
A team of collaborators from ORNL, Google Inc., Snowflake Inc. and Ververica GmbH has tested a computing concept that could help speed up real-time processing of data that stream on mobile and other electronic devices.
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.
Research teams from the Department of Energy’s Oak Ridge National Laboratory and their technologies have received seven 2021 R&D 100 Awards, plus special recognition for a COVID-19-related project.
Analytical chemists at ORNL have developed a rapid way to measure isotopic ratios of uranium and plutonium collected on environmental swipes, which could help International Atomic Energy Agency analysts detect the presence of undeclared nuclear
ASM International recently elected three researchers from ORNL as 2021 fellows. Selected were Beth Armstrong and Govindarajan Muralidharan, both from ORNL’s Material Sciences and Technology Division, and Andrew Payzant from the Neutron Scattering Division.
Scientists at ORNL and the University of Tennessee, Knoxville, have found a way to simultaneously increase the strength and ductility of an alloy by introducing tiny precipitates into its matrix and tuning their size and spacing.
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.