
Guided by machine learning, chemists at ORNL designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material.
Guided by machine learning, chemists at ORNL designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material.
Using neutrons to see the additive manufacturing process at the atomic level, scientists have shown that they can measure strain in a material as it evolves and track how atoms move in response to stress.
When the second collaborative ORNL-Vanderbilt University workshop took place on Sept. 18-19 at ORNL, about 70 researchers and students assembled to share thoughts concerning a broad spectrum of topics.
As current courses through a battery, its materials erode over time. Mechanical influences such as stress and strain affect this trajectory, although their impacts on battery efficacy and longevity are not fully understood.
ORNL has been selected to lead an Energy Earthshot Research Center, or EERC, focused on developing chemical processes that use sustainable methods instead of burning fossil fuels to radically reduce industrial greenhouse gas emissions to stem climate ch
Since its inception in 2010, the program bolsters national scientific discovery by supporting early career researchers in fields pertaining to the Office of Science.
Researchers at the Department of Energy’s Oak Ridge National Laboratory were the first to use neutron reflectometry to peer inside a working solid-state battery and monitor its electrochemistry.
Andrew Ullman, Distinguished Staff Fellow at Oak Ridge National Laboratory, is using chemistry to devise a better battery
Critical Materials Institute researchers at Oak Ridge National Laboratory and Arizona State University studied the mineral monazite, an important source of rare-earth elements, to enhance methods of recovering critical materials for energy, defense
While studying how bio-inspired materials might inform the design of next-generation computers, scientists at ORNL achieved a first-of-its-kind result that could have big implications for both edge computing and human health.