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.
Using multiple specialized techniques, IDREAM researchers gained a better understanding of how trace impurities within gibbsite affect the hydrogen yield.
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.
A perspective paper recently published by PNNL researchers underscores how the Lab’s research on electrochemical energy storage is preparing the electric grid to meet the energy needs of the future.
Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.
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.
Researchers discovered that a polymer additive promotes smooth, layer-by-layer deposition on metal electrodes by tuning interactions with the substrate.
Matteo Muratori, director of transportation and industry programs at PNNL, has been named to the 2026–2028 cohort of the National Academies of Sciences, Engineering, and Medicine’s New Voices Program.
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.