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Media Contacts
Neutron scattering at ORNL has shown that cholesterol stiffens simple lipid membranes, a finding that may help us better understand the functioning of human cells.
ORNL and Department of Energy officials dedicated the launch of two clean energy research initiatives that focus on the recycling and recovery of advanced manufacturing materials and on connected and
Through a one-of-a-kind experiment at ORNL, nuclear physicists have precisely measured the weak interaction between protons and neutrons. The result quantifies the weak force theory as predicted by the Standard Model of Particle Physics.
Scientists at Oak Ridge National Laboratory used new techniques to create a composite that increases the electrical current capacity of copper wires, providing a new material that can be scaled for use in ultra-efficient, power-dense electric vehicle traction motors.
Scientists at ORNL and the University of Nebraska have developed an easier way to generate electrons for nanoscale imaging and sensing, providing a useful new tool for material science, bioimaging and fundamental quantum research.
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.
A team led by ORNL created a computational model of the proteins responsible for the transformation of mercury to toxic methylmercury, marking a step forward in understanding how the reaction occurs and how mercury cycles through the environment.
Horizon31, LLC has exclusively licensed a novel communication system that allows users to reliably operate unmanned vehicles such as drones from anywhere in the world using only an internet connection.
Pick your poison. It can be deadly for good reasons such as protecting crops from harmful insects or fighting parasite infection as medicine — or for evil as a weapon for bioterrorism. Or, in extremely diluted amounts, it can be used to enhance beauty.
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.