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Media Contacts
Stor4Build is a new consortium focused on energy storage for buildings that will accelerate the growth, optimization and deployment of storage technologies.
Materials scientist Denise Antunes da Silva researches ways to reduce concrete’s embodied carbon in the Sustainable Building Materials Laboratory at ORNL, a research space dedicated to studying environmentally friendly building materials. Credit: ORNL, U.S. Dept. of Energy
A crowd of investors and supporters turned out for last week’s Innovation Crossroads Showcase at the Knoxville Chamber as part of Innov865 Week. Sponsored by ORNL and the Tennessee Advanced Energy Business Council, the event celebrated deep-tech entrepreneurs and the Oak Ridge Corridor as a growing energy innovation hub for the nation.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.
A study led by researchers at ORNL could help make materials design as customizable as point-and-click.
Every day, hundreds of thousands of commuters across the country travel from houses, apartments and other residential spaces to commercial buildings — from offices and schools to gyms and grocery stores.
To explore the inner workings of severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, researchers from ORNL developed a novel technique.
When Brian Fricke walks into a supermarket, evidence of his scientific achievement is all around in the refrigerated cases housing the fresh fruits and vegetables. As an Oak Ridge National Laboratory building equipment researcher, Fricke has a long history of making sure that produce is kept fresh in an energy efficient and environmentally sound manner.
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.