Quantum precision reached in modeling molten salt behavior
Planning for a digitized, sustainable smart power grid is a challenge to which Suman Debnath is using not only his own applied mathematics expertise, but also the wider communal knowledge made possible by his revival of a local chapter of the IEEE professional society.
There are more than 17 million veterans in the United States, and approximately half rely on the Department of Veterans Affairs for their healthcare.
A collaboration between the ORNL and a Florida-based medical device manufacturer has led to the addition of 500 jobs in the Miami area to support the mass production of N95 respirator masks.
The Transformational Challenge Reactor, or TCR, a microreactor built using 3D printing and other new advanced technologies, could be operational by 2024.
Soteria Battery Innovation Group has exclusively licensed and optioned a technology developed by Oak Ridge National Laboratory designed to eliminate thermal runaway in lithium ion batteries due to mechanical damage.
Oak Ridge National Laboratory scientists have discovered a cost-effective way to significantly improve the mechanical performance of common polymer nanocomposite materials.
Oak Ridge National Laboratory scientists evaluating northern peatland responses to environmental change recorded extraordinary fine-root growth with increasing temperatures, indicating that this previously hidden belowground mechanism may play an important role in how carbon-rich peatlands respond to warming.
ORNL researchers have developed an intelligent power electronic inverter platform that can connect locally sited energy resources such as solar panels, energy storage and electric vehicles and smoothly interact with the utility power grid.
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.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.