By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.
Anika Halappanavar’s research into COVID-19 misinformation earned her recognition by the Washington State Academy of Sciences as one of the state’s top high school researchers.
Rotational Hammer Riveting, developed by PNNL, joins dissimilar materials quickly without preheating rivets. The friction-based riveting enables use of lightweight magnesium rivets and also works on aluminum and speeds manufacturing.
PNNL data scientists Svitlana Volkova and Emily Saldanha, along with former PNNL intern Pamela Bilo Thomas, will publish their research on online information spread in Nature's Scientific Reports.
PNNL teamed with academia and industry to develop a novel zero-emission methane pyrolysis process that produces both hydrogen and high-value carbon solids suitable for an array of manufacturing applications.
PNNL’s newest solvent captures carbon dioxide from power plants for as little as $47.10 per metric ton, marking a significant milestone in the journey to lower the cost of carbon capture.
As a member of the NAM board of directors, Brett Jefferson, PNNL data scientist, will help lead the professional association’s mission to advance mathematical excellence of underrepresented minorities.
In a new review, PNNL researchers outline how to convert stranded biomass to sustainable fuel using electrochemical reduction reactions in mini-refineries powered by renewable energy.