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91 - 100 of 135 Results

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

University of Pennsylvania researchers called on computational systems biology expertise at Oak Ridge National Laboratory to analyze large datasets of single-cell RNA sequencing from skin samples afflicted with atopic dermatitis.

A team of scientists from LanzaTech, Northwestern University and ORNL have developed carbon capture technology that harnesses emissions from industrial processes to produce acetone and isopropanol

A new fusion record was announced February 9 in the United Kingdom: At the Joint European Torus, or JET, the team documented the generation of 59 megajoules of sustained fusion energy, more than doubling the

Scientists have developed a novel approach to computationally infer previously undetected behaviors within complex biological environments by analyzing live, time-lapsed images that show the positioning of embryonic cells in C. elegans, or roundworms. Their published methods could be used to reveal hidden biological activity.

Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.

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.