Dynamic organic matter traits tied to reactions can surpass static descriptors in explaining decomposition rates and responses to environmental change.
Using an AI approach to combine outputs from microbial models and apply them as inputs to reactive-transport models reduces computational time by several orders of magnitude.
A team from PNNL joined the Department of Homeland Security Science and Technology Directorate and first responders from across the country to evaluate commercially available unmanned ground vehicles for emergency response.
PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.
Replacing commercial acid with acidic waste enables researchers to improve nickel extraction efficiency, lower projected costs, and improve process economics.
RemPlex 2025 Global Summit on Environmental Remediation attendees share knowledge about cleanup and monitoring of complex sites worldwide; more than 100 presentations are posted online.
Connecting energy generation, electricity storage, and using sensors and control software to track load, including precommercial market marine energy generation technologies.
From developing new energy storage materials to revealing patterns of Earth’s complex systems, studies led by PNNL researchers are recognized for their innovation and influence.
The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.
The first direct molecular-scale evidence of the temperature-driven transformation of the coordination environment of ytterbium at geologically relevant conditions.
Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.
