Currently, the biggest hurdle for electric vehicles, or EVs, is the development of advanced battery technology to extend driving range, safety and reliability.
Dean Pierce of ORNL and a research team led by ORNL’s Alex Plotkowski were honored by DOE’s Vehicle Technologies Office for development of novel high-performance alloys that can withstand extreme environments.
ORNL scientists found that a small tweak created big performance improvements in a type of solid-state battery, a technology considered vital to broader electric vehicle adoption.
Nonfood, plant-based biofuels have potential as a green alternative to fossil fuels, but the enzymes required for production are too inefficient and costly to produce. However, new research is shining a light on enzymes from fungi that could make biofuels economically viable.
A partnership of ORNL, the Tennessee Department of Economic and Community Development, the Community Reuse Organization of East Tennessee and TVA that aims to attract nuclear energy-related firms to Oak Ridge has been recognized with a state and local economic development award from the Federal Laboratory Consortium.
Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.
Oak Ridge National Laboratory researchers have developed a thin film, highly conductive solid-state electrolyte made of a polymer and ceramic-based composite for lithium metal batteries.
A novel approach developed by scientists at ORNL can scan massive datasets of large-scale satellite images to more accurately map infrastructure – such as buildings and roads – in hours versus days.
Researchers at the Department of Energy’s Oak Ridge National Laboratory have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.
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.