Armed with some of the world’s most advanced instrumentation, researchers at PNNL are working to analyze huge amounts of data and uncover hidden biological connections.
PNNL scientists developed a new method to map exactly how a fungus works with leafcutter ants in a complex microbial community to degrade plant material at the molecular level. The team’s insights are important for biofuels development.
A new discovery by PNNL researchers has illuminated a previously unknown key mechanism that could inform the development of new, more effective catalysts for abating NOx emissions from combustion-engines burning diesel or low carbon fuel.
Tiffany Kaspar’s work has advanced the discovery and understanding of oxide materials, helping develop electronics, quantum computing, and energy production. She strives to communicate her science to the public.
Researchers from the Environmental Molecular Sciences Laboratory are collecting soil cores as part of the 1000 Soils Research Pilot to develop a database of molecular-level data from belowground ecosystems.
Scott Chambers creates layered structures of thin metal oxide films and studies their properties, creating materials not found in nature. He will soon move his instrumentation and research to the new Energy Sciences Center.
The DOE Early Career Research Program supports exceptional researchers during the crucial early years of their careers and helps advance scientific discovery in fundamental sciences
As he prepares to enter PNNL's Energy Sciences Center later this year, Vijayakumar 'Vijay' Murugesan is among DOE leaders exploring solutions to design and build transformative materials for batteries of the future.
New 140,000-square-foot facility will advance fundamental chemistry and materials science for higher-performing, cost-effective catalysts and batteries, and other energy efficiency technologies.
PNNL has three small-scale spectroscopy devices that are speeding up the testing and analysis of candidate novel materials used in energy storage research and environmental remediation.
Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.