Katy Bradford: Cassette approach offers compelling construction solution
A digital construction platform in development at Oak Ridge National Laboratory is boosting the retrofitting of building envelopes and giving builders the tools to automate the process from design to installation with the assistance of a cable-driven robotic crane.
Oak Ridge National Laboratory scientists have developed a method leveraging artificial intelligence to accelerate the identification of environmentally friendly solvents for industrial carbon capture, biomass processing, rechargeable batteries and other applications.
Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
Oak Ridge National Laboratory scientists studied hot springs on different continents and found similarities in how some microbes adapted despite their geographic diversity.
Oak Ridge National Laboratory scientists exploring bioenergy plant genetics have made a surprising discovery: a protein domain that could lead to new COVID-19 treatments.
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.
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.
A research team led by Oak Ridge National Laboratory bioengineered a microbe to efficiently turn waste into itaconic acid, an industrial chemical used in plastics and paints.
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.