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Media Contacts
If air taxis become a viable mode of transportation, Oak Ridge National Laboratory researchers have estimated they could reduce fuel consumption significantly while alleviating traffic congestion.
As ORNL’s fuel properties technical lead for the U.S. Department of Energy’s Co-Optimization of Fuel and Engines, or Co-Optima, initiative, Jim Szybist has been on a quest for the past few years to identify the most significant indicators for predicting how a fuel will perform in engines designed for light-duty vehicles such as passenger cars and pickup trucks.
Scientists from Oak Ridge National Laboratory used high-performance computing to create protein models that helped reveal how the outer membrane is tethered to the cell membrane in certain bacteria.
Popular wisdom holds tall, fast-growing trees are best for biomass, but new research by two U.S. Department of Energy national laboratories reveals that is only part of the equation.
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.
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.
Oak Ridge National Laboratory researchers used additive manufacturing to build a first-of-its kind smart wall called EMPOWER.
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.
Scientists at the Department of Energy’s Oak Ridge National Laboratory have a powerful new tool in the quest to produce better plants for biofuels, bioproducts and agriculture.
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.