PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.
Researchers at PNNL shared advances in artificial intelligence, cybersecurity, advanced imaging, and more at the Department of Homeland Security Research, Development, Test, and Evaluation Summit.
PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.
Continued studies will deepen scientists’ understanding of virus-host interactions at the molecular level and also pave the way for developing better drugs to fight emerging viruses.
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.
Three PNNL-supported projects are at the forefront of developing advanced data analytics technologies to enhance the U.S. power grid’s reliability, resilience, and affordability.
The Grid Storage Launchpad dedication event was attended by leaders in grid and transportation energy storage, battery innovation, and industry stakeholders working to transform America’s energy system.
PNNL staff in the Artificial Intelligence and Data Analytics division were recognized by the TSA’s Innovation Task Force (ITF) for their contributions to cloud capabilities, development strategies, and smart management of cloud resources.
Early life exposure to polycyclic aromatic hydrocarbons (PAHs), found in smoke, has been linked to developmental problems. To study the impacts of these pollutants, PAH metabolism in infants and adults were compared.
Researchers found that in a future where the Great Plains are 4 to 6 degrees Celsius (°C) warmer as projected in a high-emission scenario, these storms could bring three times more intense rainfall.