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In collaboration with the U.S. Department of Homeland Security’s Science and Technology Directorate, researchers at ORNL are evaluating technology to detect compounds emitted by pathogens and pests in agricultural products at the nation’s border. 

Scientists at ORNL have developed a method that can track chemical changes in molten salt in real time — helping to pave the way for the deployment of molten salt reactors for energy production.

Professionals from government and industry gathered at ORNL for the Nondestructive Assay Holdup Measurements Training Course for Nuclear Criticality Safety, a hands-on training in nondestructive assay, a technique for detecting and quantifying holdup without disturbing operations. 

During his first visit to 91����, Energy Secretary Chris Wright compared the urgency of the Lab’s World War II beginnings to today’s global race to lead in artificial intelligence, calling for a “Manhattan Project 2.”

Ryan Culler is the program manager at 91����, where he oversees the production of actinium-225, a promising treatment for cancer. Driven by a personal connection to cancer through his late brother, Culler is dedicated to advancing medical isotopes to help improve cancer care. 

Researchers at the Department of Energy’s 91���� are using non-weather data from the nationwide weather radar network to understand how to track non-meteorological events moving through the air for better emergency response. 

During Hurricanes Helene and Milton, ORNL deployed drone teams and the Mapster platform to gather and share geospatial data, aiding recovery and damage assessments. ORNL's EAGLE-I platform tracked utility outages, helping prioritize recovery efforts. Drone data will train machine learning models for faster damage detection in future disasters. 

The US focuses on nuclear nonproliferation, and ORNL plays a key role in this mission. The lab conducts advanced research in uranium science, materials analysis and nuclear forensics to detect illicit nuclear activities. Using cutting-edge tools and operational systems, ORNL supports global efforts to reduce nuclear threats by uncovering the history of nuclear materials and providing solutions for uranium removal. 

Researchers are using machine learning to provide a more complete picture of building geometries that include building height to within three meters of accuracy. This model not only provides building height for any building in the world, but it will also feed into LandScan and other large government datasets for planning and response.