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.

The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.

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.

PMedIC, the PNNL and OHSU joint research collaboration, contributes to student success and impact in biomedicine.

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.

Scientists screen for nanobodies that recognize wild type and mutant functional proteins to develop a framework to disrupt protein interactions that can cause disease.

Bradley Crowell with the U.S. Nuclear Regulatory Commission sees advanced materials integrity, radiological measurement, and environmental capabilities on his first visit to PNNL.

Recovery Simulator and Analysis tool further strengthens PNNL’s short-term resilience planning efforts for Puerto Rico.

PNNL’s ARENA test bed analyzes how electrical cables degrade in extreme environments and how nondestructive examination inspection technologies can detect and locate damage.

ExaGO, a power grid simulation and optimization platform developed at PNNL is the first of its kind to run on Frontier.

Silicone wristbands could be more convenient option for collecting exposure data.

PNNL scientists developed new capabilities to study materials under extreme conditions.

Study highlights microbiome’s role in regulating xenobiotic metabolism.

COVID-19 had multiple impacts on the electric sector and the nature of those impacts varied on the scale analyzed.

Simulations reveal advantages of using new model vs. allometric scaling for risk assessment.

Exposure to polycyclic aromatic hydrocarbons found at Superfund Sites induces enzymes in dose- and time-dependent patterns in mice.

Geophysical modeling and inversion software meets DOE and nuclear industry standards for use in decision-making for radiologically contaminated sites.