Seven entrepreneurs will embark on a two-year fellowship as the seventh cohort of Innovation Crossroads kicks off this month at ORNL. Representing a range of transformative energy technologies, Cohort 7 is a diverse class of innovators with promising new companies.
Wildfires are an ancient force shaping the environment, but they have grown in frequency, range and intensity in response to a changing climate. At ORNL, scientists are working on several fronts to better understand and predict these events and what they mean for the carbon cycle and biodiversity.
Innovations in artificial intelligence are rapidly shaping our world, from virtual assistants and chatbots to self-driving cars and automated manufacturing.
Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.
ORNL researchers, in collaboration with Enginuity Power Systems, demonstrated that a micro combined heat and power prototype, or mCHP, with a piston engine can achieve an overall energy efficiency greater than 93%.
In a discovery aimed at accelerating the development of process-advantaged crops for jet biofuels, scientists at ORNL developed a capability to insert multiple genes into plants in a single step.
Rigoberto Advincula, a renowned scientist at ORNL and professor of Chemical and Biomolecular Engineering at the University of Tennessee, has won the Netzsch North American Thermal Analysis Society Fellows Award for 2023.
Oak Ridge National Laboratory scientists led the development of a supply chain model revealing the optimal places to site farms, biorefineries, pipelines and other infrastructure for sustainable aviation fuel production.
A method using augmented reality to create accurate visual representations of ionizing radiation, developed at ORNL, has been licensed by Teletrix, a firm that creates advanced simulation tools to train the nation’s radiation control workforce.
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.