A DNA editing tool adapted by Oak Ridge National Laboratory scientists makes engineering microbes for everything from bioenergy production to plastics recycling easier and faster.
A study by Oak Ridge National Laboratory researchers has demonstrated how satellites could enable more efficient, secure quantum networks.
Critical Materials Institute researchers at Oak Ridge National Laboratory and Arizona State University studied the mineral monazite, an important source of rare-earth elements, to enhance methods of recovering critical materials for energy, defense and manufacturing applications.
ORNL researchers have identified a mechanism in a 3D-printed alloy – termed “load shuffling” — that could enable the design of better-performing lightweight materials for vehicles.
A team of scientists led by ORNL discovered the gene in agave that governs when the plant goes dormant and used it to create poplar trees that nearly doubled in size, increasing biomass yield for biofuels production
Researchers at Oak Ridge National Laboratory demonstrated that an additively manufactured polymer layer, when applied to carbon fiber reinforced plastic, or CFRP, can serve as an effective protector against aircraft lightning strikes.
As scientists study approaches to best sustain a fusion reactor, a team led by Oak Ridge National Laboratory investigated injecting shattered argon pellets into a super-hot plasma, when needed, to protect the reactor’s interior wall from high-energy runaway electrons.
Researchers demonstrated that an additively manufactured hot stamping die can withstand up to 25,000 usage cycles, proving that this technique is a viable solution for production.
In a recent study, researchers at Oak Ridge National Laboratory performed experiments in a prototype fusion reactor materials testing facility to develop a method that uses microwaves to raise the plasma’s temperature closer to the extreme values
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.