A modeling analysis led by ORNL gives the first detailed look at how geothermal energy can relieve the electric power system and reduce carbon emissions if widely implemented across the United States within the next few decades.
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.
Researchers at the Statewide California Earthquake Center are unraveling the mysteries of earthquakes by using physics-based computational models running on high-performance computing systems at ORNL. The team’s findings will provide a better understanding of seismic hazards in the Golden State.
ORNL researchers have developed a novel way to encapsulate salt hydrate phase-change materials within polymer fibers through a coaxial pulling process. The discovery could lead to the widespread use of the low-carbon materials as a source of insulation for a building’s envelope.
Four ORNL teams and one researcher were recognized for excellence in technology transfer and technology transfer innovation.
Rigoberto “Gobet” Advincula, a scientist at the Department of Energy’s Oak Ridge National Laboratory, has been appointed a Fellow of the Institute of Materials, Minerals and Mining.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
Rigoberto “Gobet” Advincula, a scientist at the Department of Energy’s Oak Ridge National Laboratory, has been named a 2023 Fellow of the National Academy of Inventors. Advincula has been recognized for his 14 patents and 21 published filings related to nanomaterials, smart coatings and films, solid-state device fabrication and chemical additives.
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%.