PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
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.
The Department of Energy Office of Nuclear Energy acting assistant secretary makes his first visit to a national laboratory in his new role, touring PNNL's Radiochemical Processing Laboratory.
Resolving how nanoparticles come together is important for industry and environmental remediation. New work predicts nanoparticle aggregation behavior across a wide range of scales for the first time.
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.
A poem inspired by radioactive tank waste—“Can a Scientist Dream it Alone?”—was awarded first place in the Department of Energy’s Poetry of Science Art Contest.
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 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.
Gosline works to develop computational algorithms that are uniquely targeted for rare disease work by doing foundational research in model system development. This work can be expanded to all model systems in human disease.
