Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
An international team using neutrons set the first benchmark (one nanosecond) for a polymer-electrolyte and lithium-salt mixture. Findings could produce safer, more powerful lithium batteries.
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.
Scientists at the Department of Energy’s Oak Ridge National Laboratory are using a new modeling framework in conjunction with data collected from marshes in the Mississippi Delta to improve predictions of climate-warming methane and nitrous oxide
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 at ORNL and the National Renewable Energy Laboratory took inspiration from flying insects to demonstrate a miniaturized gyroscope, a special sensor used in navigation technologies.
A team of scientists found that critical interactions between microbes and peat moss break down under warming temperatures, impacting moss health and ultimately carbon stored in soil.
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
Researchers used neutron scattering at Oak Ridge National Laboratory’s Spallation Neutron Source and High Flux Isotope Reactor to better understand how certain cells in human tissue bond together.
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.