Ampcera has an exclusive licensing agreement with PNNL to commercially develop and license a new battery material for applications such as vehicles and personal electronics.
PNNL's ASSORT model will help airports balance passenger screening and security risks with throughput. It also quantifies risks for different traveler types and optimizes checkpoint operations, improving efficiency while enhancing safety.
A team from PNNL contributed several articles to the Domestic Preparedness Journal showcasing recent efforts to explore the emergency management and artificial intelligence research and development landscape.
Seawater threatens to intrude into coastal freshwater aquifers that millions of people depend on for drinking water and irrigation. This study investigates sea-level rise impacts on the global coastal groundwater table.
New datasets delineating global urban land support scientific research, application, and policy, but they can produce different results when applied to the same problem making it difficult for researchers to decide which to use.
Sergei Kalinin, a joint appointee at the University of Tennessee, Knoxville and PNNL, and Ji-Guang (Jason) Zhang, a PNNL Lab Fellow, are part of the 2024 class of National Academy of Inventors Fellows.
A recent paper published in Science sheds light on how aerosols—tiny particles in the air—released by industrial activities can trigger downstream snowfall events.
PNNL Earth scientist Alison Delgado will serve as an author for the “Science of Response Management” chapter of the Sixth National Climate Assessment (NCA6.)
Through a detailed examination of historical data supported by mechanistic analysis and model experiments, researchers unveil that a large-scale climate system intensifies heat extremes and wildfire risks in the PNW.
Energy storage is increasingly critical to building a resilient electric grid in the United States—a trend embodied by the Grid Storage Launchpad, a newly inaugurated, 93,000-square-foot facility at PNNL.
This study shows that dry dynamics alone is not enough to understand jet stream persistence. Instead, clouds and precipitation are more important contributors than internal “dry” mechanisms to this memory of the Southern Hemisphere jet.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.