By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
Battery energy storage systems are being proposed in municipalities across the U.S. PNNL researchers can help community planners guide safe siting and operations.
The diversity and function of organic matter in rivers at a large scale are influenced by factors, such as the types of vegetation covering the land, the energy characteristics, and the breakdown potential of the molecules.
Findings in a new PNNL report show long-duration energy storage will be a necessity in decarbonizing the grid and recommends the planning and procurement process to identify those needs start immediately.
Diefenderfer, Earth scientist who focuses on coastal ecosystems at PNNL, recently published “Ten Years of Gulf Coast Ecosystem Restoration Projects Since the Deepwater Horizon Oil Spill,” a cover article.
ICON science is a Department of Energy-developed framework to enhance scientific outcomes via more intentional design of research efforts across all domains of science.
An analysis of land use in watersheds that supply drinking water to over a hundred United States cities identified a wide range of exposure to potential contamination.
With an eye on renewable, accessible, and resilient power, PNNL researchers show hyper-local microgrids are a viable option, if designed with the right mix of sources.
Integrating hydrogeology and biogeochemistry are required to model the dynamics of geochemical processes occurring in river corridor zones where groundwater and surface water mix.
