First-generation graduate Brittany Rodriguez advances manufacturing scie...
A trio of new and improved cosmological simulation codes was unveiled in a series of presentations at the annual April Meeting of the American Physical Society in Minneapolis.
Valentino “Tino” Cooper, a scientist at ORNL, has been appointed to DOE’s Basic Energy Sciences Advisory Committee for a three-year term. Cooper’s research elucidates the fundamental understanding of advanced materials for next-generation energy and information technologies.
Growing up in China, Yue Yuan stood beneath the world’s largest hydroelectric dam, built to harness the world’s third-longest river. Her father brought her to Three Gorges Dam every year as it was being constructed across the Yangtze River so she could witness its progress.
SAE International has awarded ORNL Buildings and Transportation Science Division Director Robert Wagner with the SAE Medal of Honor for his dedication and support of the organization’s mission of advancing mobility solutions.
Climate change often comes down to how it affects water, whether it’s for drinking, electricity generation, or how flooding affects people and infrastructure. To better understand these impacts, ORNL water resources engineer Sudershan Gangrade is integrating knowledge ranging from large-scale climate projections to local meteorology and hydrology and using high-performance computing to create a holistic view of the future.
Andrew Lupini, a scientist and inventor at ORNL, has been elected Fellow of the Microscopy Society of America.
A study led by Oak Ridge National Laboratory researchers identifies a new potential application in quantum computing that could be part of the next computational revolution.
Marm Dixit, a Weinberg Distinguished Staff Fellow at ORNL has received the 2023 Rosalind Franklin Young Investigator Award.
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.
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.