Artificial intelligence tools secure tomorrow’s electric grid
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.
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.
Measuring water quality throughout river networks with precision, speed and at lower cost than traditional methods is now possible with AquaBOT, an aquatic drone developed by Oak Ridge National Laboratory.
Oak Ridge National Laboratory scientists worked with the Colorado School of Mines and Baylor University to develop and test control methods for autonomous water treatment plants that use less energy and generate less waste.
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
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 new analysis from Oak Ridge National Laboratory shows that intensified aridity, or drier atmospheric conditions, is caused by human-driven increases in greenhouse gas emissions. The findings point to an opportunity to address and potentially reverse the trend by reducing emissions.
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.