Shear Assisted Processing and Extrusion (ShAPE) imparts significantly more deformation compared to conventional extrusion. The latest ShAPE system at PNNL, ShAPEshifter, is a purpose-built machine designed for maximum configurability.
Continued studies will deepen scientists’ understanding of virus-host interactions at the molecular level and also pave the way for developing better drugs to fight emerging viruses.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
The Grid Storage Launchpad dedication event was attended by leaders in grid and transportation energy storage, battery innovation, and industry stakeholders working to transform America’s energy system.
Early life exposure to polycyclic aromatic hydrocarbons (PAHs), found in smoke, has been linked to developmental problems. To study the impacts of these pollutants, PAH metabolism in infants and adults were compared.
Erich Hsieh, Deputy Assistant Secretary for OE’s Energy Storage Division, shared insights about the Grid Storage Launchpad and energy storage innovations .
Discovering and measuring the spatial organization of proteins within cells allows scientists to map complex proteoforms across tissues with near-cellular resolution.
Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.
Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.
COVID-19 infections at PNNL early in the pandemic were caused by a wide variety of viral sequences, according to a new analysis by Laboratory researchers.
A new policy database containing energy equity-related actions could serve as a useful starting point for state policymakers and stakeholders who want to enact similar energy equity measures or adapt policies to their local circumstances.
A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.