Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.
PNNL’s Center for the Remediation of Complex Sites convened attendees from around the world to discuss challenges associated with environmental contamination.
Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.
A team of scientists at PNNL developed new computational models to predict the behavior of these impurities and reduce the expense and risk related to actinide metal production.
High fidelity simulations enabled by high-performance computing will allow for unprecedented predictive power of molecular level processes that are not amenable to experimental measurement.
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.
Report for the Oregon Public Utility Commission highlights innovations and best practices for resilience and utility planning could be helpful to other states as well.
Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.