Little of the mixed consumer plastics thrown away or placed in recycle bins actually ends up being recycled. Nearly 90% is buried in landfills or incinerated at commercial facilities that generate greenhouse gases and airborne toxins. Neither outcome is ideal for the environment.
Creating energy the way the sun and stars do — through nuclear fusion — is one of the grand challenges facing science and technology. What’s easy for the sun and its billions of relatives turns out to be particularly difficult on Earth.
ORNL will team up with six of eight companies that are advancing designs and research and development for fusion power plants with the mission to achieve a pilot-scale demonstration of fusion within a decade.
Researchers at ORNL are tackling a global water challenge with a unique material designed to target not one, but two toxic, heavy metal pollutants for simultaneous removal.
Systems biologist Paul Abraham uses his fascination with proteins, the molecular machines of nature, to explore new ways to engineer more productive ecosystems and hardier bioenergy crops.
From materials science and earth system modeling to quantum information science and cybersecurity, experts in many fields run simulations and conduct experiments to collect the abundance of data necessary for scientific progress.
In the race to identify solutions to the COVID-19 pandemic, researchers at the Department of Energy’s Oak Ridge National Laboratory are joining the fight by applying expertise in computational science, advanced manufacturing, data science and neutron science.
ORNL computer scientist Catherine Schuman returned to her alma mater, Harriman High School, to lead Hour of Code activities and talk to students about her job as a researcher.
Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.