Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

Tailoring hydrogen-based energy storage solutions to specific real-world applications.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

Across the United States, water moving between the river and riverbed sediments does not overcome localized processes that govern organic matter chemistry.

A process developed at PNNL that converts biomass and waste into a chemical intermediate or into gasoline, diesel, and jet fuel is available for commercial licensing.

The Co-Optimization of Fuels & Engines initiative recently outlined six years of research in a findings and impact report.

Scientists from PNNL and the U.S. Department of Agriculture-Forest Services’ Pacific Northwest Research Station have partnered to evaluate potential climate and wildfire adaptation scenarios and resulting benefits from restoration forestry.

Researchers found that certain oxide interface configurations remain stable in extreme environments, suggesting ways to build better performing, more reliable devices for fuel cells, space-based electronics, and nuclear energy.