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.

PNNL Chief Diversity Officer and Director for STEM Education and PNNL Battery Materials and System group leader will receive Clean Energy Education and Empowerment (C3E) awards at the 2023 C3E Women in Clean Energy Symposium.

The diversity and function of organic matter in rivers at a large scale are influenced by factors, such as the types of vegetation covering the land, the energy characteristics, and the breakdown potential of the molecules.

PNNL chemists investigate the performance of ion exchange resins for subsurface remediation of uranium, technetium, and chromium.

Scientists at PNNL were awarded nearly $12 million to better understand pathogens, how they spread, and how to prepare the nation against future outbreaks.

Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.

Scientists can now generate a protein database directly from proteomics data gathered from a specific soil sample using a digital tool and deep learning computer model called Kaiko.

PNNL data scientist in the Biological Sciences Division won the 2023 JSM Data Challenge Expo. The award-winning analysis focused on the ability to identify crime patterns by correlating them with historical events and trends.

Corinne Fuller has been named the new co-director of the Bioproducts Institute, a research collaboration between Washington State University and PNNL, as of July 2023.

PNNL’s Grid Storage Launchpad delivers tomorrow’s energy storage solutions today.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

The Department of Energy’s Vehicle Technologies Office recently issued two awards to researchers at PNNL for their contributions to areas that are crucial for the expansion of electric vehicles.

David Barajas-Solano and Aaron Luttman worked with students to create models to evaluate remediation strategies at the Hanford Site.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

PNNL-Sequim scientists will spend the next year testing a new technology that could allow the ocean to soak up more carbon dioxide without contributing to ocean acidification.

Regional wind data helps utilities save money, plan for windy days.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.