ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.
Ilenne Del Valle is merging her expertise in synthetic biology and environmental science to develop new technologies to help scientists better understand and engineer ecosystems for climate resilience.
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
Four ORNL teams and one researcher were recognized for excellence in technology transfer and technology transfer innovation.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
Technology Transfer staff from Department of Energy’s Oak Ridge National Laboratory attended the 2024 Consumer Electronics Show, or CES, in Las Vegas, Jan. 8–12.
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.
Scientists at ORNL have developed a technique for recovering and recycling critical materials that has garnered special recognition from a peer-reviewed materials journal and received a new phase of funding for research and development.
Ateios Systems licensed an ORNL technology for solvent-free battery component production using electron curing. Through Innovation Crossroads, Ateios continues to work with ORNL to enable readiness for production-quality battery components.
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%.