PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.
Researchers at PNNL are pursuing new approaches to understand, predict and control the phenome—the collection of biological traits within an organism shaped by its genes and interactions with the environment.
The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.
The National Transmission Planning Study presents several transmission expansion scenarios that would reliably support the growing demand for energy across the nation.
PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.
ChatGrid™ is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.
Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.
This study profiled the 24-hour rhythmicity in bile salt hydrolase enzyme activity using simple fluorescence assay and the results showed that this rhythmicity is influenced by feeding patterns of the host.
New research from PNNL and Washington State University collaborators connects the microbiome in the gut to circadian rhythms, suggesting a role for the microbiome as an internal regulator.
PNNL Biomedical Scientist Geremy Clair has taken on new roles as an editor for two journals; Frontiers In Cellular And Infection Microbiology and Frontiers In Molecular Biosciences.
Zhenyu (Henry) Huang acts as guest editor in an upcoming special issue on “Climate Change Mitigation and Adaptation in Power and Energy Systems." The special edition is published by Elsevier.
New research findings published in Science Advances (November 2022), help explain the progression of Alzheimer-related dementia in each patient. The findings outline a biological classification system that predicts disease severity.