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The combination of bioenergy with carbon capture and storage could cost-effectively sequester hundreds of millions of metric tons per year of carbon dioxide in the United States, making it a competitive solution for carbon management, according to a new analysis by ORNL scientists.
Prometheus Fuels has licensed an ethanol-to-jet-fuel conversion process developed by researchers at Oak Ridge National Laboratory. The ORNL technology will enable cost-competitive production of jet fuel and co-production of butadiene for use in renewable polymer synthesis.
ORNL scientists have modified a single microbe to simultaneously digest five of the most abundant components of lignocellulosic biomass, a big step forward in the development of a cost-effective biochemical conversion process to turn plants into
Oak Ridge National Laboratory researchers used additive manufacturing to build a first-of-its kind smart wall called EMPOWER.
Oak Ridge National Laboratory researchers have designed and additively manufactured a first-of-its-kind aluminum device that enhances the capture of carbon dioxide emitted from fossil fuel plants and other industrial processes.
Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.
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.
Five researchers at the Department of Energy’s Oak Ridge National Laboratory have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
Scientists at ORNL used neutron scattering and supercomputing to better understand how an organic solvent and water work together to break down plant biomass, creating a pathway to significantly improve the production of renewable
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.