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A novel additive manufacturing method developed by researchers at Oak Ridge National Laboratory could be a promising alternative for low-cost, high-quality production of large-scale metal parts with less material waste.

Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.

OAK RIDGE, Tenn., March 20, 2019—Direct observations of the structure and catalytic mechanism of a prototypical kinase enzyme—protein kinase A or PKA—will provide researchers and drug developers with significantly enhanced abilities to understand and treat fatal diseases and neurological disorders such as cancer, diabetes, and cystic fibrosis.

OAK RIDGE, Tenn., March 11, 2019—An international collaboration including scientists at the Department of Energy’s Oak Ridge National Laboratory solved a 50-year-old puzzle that explains why beta decays of atomic nuclei


Oak Ridge National Laboratory’s latest Transportation Energy Data Book: Edition 37 reports that the number of vehicles nationwide is growing faster than the population, with sales more than 17 million since 2015, and the average household vehicle travels more than 11,000 miles per year.

OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.

Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.

Oak Ridge National Laboratory scientists analyzed more than 50 years of data showing puzzlingly inconsistent trends about corrosion of structural alloys in molten salts and found one factor mattered most—salt purity.

Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.