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.

Glenn Fugate was selected to serve on the American Chemical Society's Science Committee.

Germany Harris, Dewayne Maye, Sarah Olocha, Shaniya Pettway, and Rayonna Redmon became the first interns of the Minority Serving Institution Partnership Program Partnership for Radiation Studies Consortium at PNNL.

A panel at the National Homeland Security Conference highlighted latest modeling and operational tools aiding response.

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

Summer is for science! PNNL’s interns are diving into science and technology and getting a front-row view of the research and development of a national laboratory.

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.

At the Nonproliferation, Counterproliferation, and Disarmament Science Gordon Research Conference, researchers from PNNL shared research and scientific approaches for countering diverse threats.

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.

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.

A holistic evaluation of the uncertainty sources that arise from configuring the model to processing the output for global variable-resolution models.

Researchers found a 150-day period in Antarctic circulation, something that many current climate models do not represent accurately.

The award was given at the 29th International Conference on Information, Communication and Automation Technologies.

Two teams were recognized for technologies to help the International Atomic Energy Agency verify peaceful use of nuclear material.

Four PNNL researchers received highly competitive DOE Early Career Research Program awards, providing five continuous years of funding for their projects.