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.
Ampcera has an exclusive licensing agreement with PNNL to commercially develop and license a new battery material for applications such as vehicles and personal electronics.
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.
Discovering and measuring the spatial organization of proteins within cells allows scientists to map complex proteoforms across tissues with near-cellular resolution.
Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.
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 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.
