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Media Contacts
To optimize biomaterials for reliable, cost-effective paper production, building construction, and biofuel development, researchers often study the structure of plant cells using techniques such as freezing plant samples or placing them in a vacuum.
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
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.
Tackling the climate crisis and achieving an equitable clean energy future are among the biggest challenges of our time.
ORNL scientists had a problem mapping the genomes of bacteria to better understand the origins of their physical traits and improve their function for bioenergy production.
The rapid pace of global climate change has added urgency to developing technologies that reduce the carbon footprint of transportation technologies, especially in sectors that are difficult to electrify.
Bryan Piatkowski, a Liane Russell Distinguished Fellow in the Biosciences Division at ORNL, is exploring the genetic pathways for traits such as stress tolerance in several plant species important for carbon sequestration
A team of researchers working within the Center for Bioenergy Innovation at ORNL has discovered a pathway to encourage a type of lignin formation in plants that could make the processing of crops grown for products such as sustainable jet fuels easier and less costly.
Spanning no less than three disciplines, Marie Kurz’s title — hydrogeochemist — already gives you a sense of the collaborative, interdisciplinary nature of her research at ORNL.
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.