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Media Contacts
Scientists at the Department of Energy’s Oak Ridge National Laboratory are using a new modeling framework in conjunction with data collected from marshes in the Mississippi Delta to improve predictions of climate-warming methane and nitrous oxide.
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.
Corning uses neutron scattering to study the stability of different types of glass. Recently, researchers for the company have found that understanding the stability of the rings of atoms in glass materials can help predict the performance of glass products.
In summer 2023, ORNL's Prasanna Balaprakash was invited to speak at a roundtable discussion focused on the importance of academic artificial intelligence research and development hosted by the White House Office of Science and Technology Policy and the U.S. National Science Foundation.
The 21st Symposium on Separation Science and Technology for Energy Applications, Oct. 23-26 at the Embassy Suites by Hilton West in Knoxville, attracted 109 researchers, including some from Austria and the Czech Republic. Besides attending many technical sessions, they had the opportunity to tour the Graphite Reactor, High Flux Isotope Reactor and both supercomputers at ORNL.
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%.