Scientists at ORNL have developed 3D-printed collimator techniques that can be used to custom design collimators that better filter out noise during different types of neutron scattering experiments
Jack Orebaugh, a forensic anthropology major at the University of Tennessee, Knoxville, has a big heart for families with missing loved ones. When someone disappears in an area of dense vegetation, search and recovery efforts can be difficult, especially when a missing person’s last location is unknown. Recognizing the agony of not knowing what happened to a family or friend, Orebaugh decided to use his internship at the Department of Energy’s Oak Ridge National Laboratory to find better ways to search for lost and deceased people using cameras and drones.
Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.
How do you get water to float in midair? With a WAND2, of course. But it’s hardly magic. In fact, it’s a scientific device used by scientists to study matter.
The Autonomous Systems group at ORNL is in high demand as it incorporates remote sensing into projects needing a bird’s-eye perspective.
A scientific instrument at ORNL could help create a noninvasive cancer treatment derived from a common tropical plant.
Natural gas furnaces not only heat your home, they also produce a lot of pollution. Even modern high-efficiency condensing furnaces produce significant amounts of corrosive acidic condensation and unhealthy levels of nitrogen oxides
Scientists develop environmental justice lens to identify neighborhoods vulnerable to climate change
A new capability to identify urban neighborhoods, down to the block and building level, that are most vulnerable to climate change could help ensure that mitigation and resilience programs reach the people who need them the most.
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.