To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.
This PNNL-developed separation system quickly and successfully separates larger particles from smaller ones at various scales, in different solid-liquid mixtures and at different flow rates.
To support federal energy agencies in meeting renewed environmental policies, PNNL is identifying the mechanisms and practices that could enhance agencies’ existing environmental justice programs, policies, and activities.
An analysis of land use in watersheds that supply drinking water to over a hundred United States cities identified a wide range of exposure to potential contamination.
The newly created ICON Science Cooperative is a resource enabling an innovative approach to science to generate transferable knowledge and increase equity.
A Q&A with Lauren Charles, veterinarian and PNNL data scientist, on zoonotic diseases and the role biosurveillance plays in mitigating the growing threat to global health.
Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.