The American Society of Heating, Refrigeration and Air-Conditioning Engineers, or ASHRAE, selected Jason DeGraw, a researcher with ORNL, as one of 23 members elevated to Fellow during its 2024 winter conference.
Four ORNL teams and one researcher were recognized for excellence in technology transfer and technology transfer innovation.
Gina Tourassi, associate laboratory director for computing and computational sciences at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory, has been named a fellow of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.
A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%.
Magnesium oxide is a promising material for capturing carbon dioxide directly from the atmosphere and injecting it deep underground to limit the effects of climate change. ORNL scientists are exploring ways to overcome an obstacle to making the technology economical.
Scientists from Stanford University and the Department of Energy’s Oak Ridge National Laboratory are turning air into fertilizer without leaving a carbon footprint. Their discovery could deliver a much-needed solution to help meet worldwide carbon-neutral goals by 2050.
Four 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.
Recent research by ORNL scientists focused on the foundational steps of carbon dioxide sequestration using aqueous glycine, an amino acid known for its absorbent qualities.
Effective Dec. 4, Gina Tourassi will assume responsibilities as associate laboratory director for the Computing and Computational Sciences Directorate at the Department of Energy’s Oak Ridge National Laboratory.
Researchers from institutions including ORNL have created a new method for statistically analyzing climate models that projects future conditions with more fidelity.