New PNNL-developed nanogenerator harnesses the renewable energy in ocean waves to power sensors that provide critical weather and wave information.

Scientists used a new analysis approach—coupled laser ablation sampling and capillary absorption spectroscopy—to gather more information on how the rhizosphere processes carbon.

STEM Education programs at DOE and national labs offer environmental management internships; applications are due soon for summer 2023.

New PNNL research makes it easier to differentiate between chemical and nuclear explosions.

A collaborative effort led by PNNL researchers analyzed decarbonization pathways for Argentina, Chile, Egypt, Indonesia, and Nigeria.

PNNL’s contributions over the last year span scientific discovery, sustainable energy and national security.

Clarivate recently released the list of Highly Cited Researchers for 2022.

Abigail Snyder named to international team focused on designing and scoping models for use in large intercomparison project.

The Joint Appointment program at PNNL is one of the most diverse among other U.S. national laboratories, involving nearly 60 universities and research institutions in the United States and abroad.

rmap enables users with limited to no geographic information system knowledge use R to visualize simple tabular data.

A review article led by researcher Jade Holliman explores the different classes of metamaterials, from the underlying fundamental science to potential applications.

PNNL-Sequim team wins ESTCP Project of the Year for their work supporting underwater munition remediation.

The American Geophysical Union will honor Andrew Gettelman at their Fall Meeting as a new Fellow.

The methods researchers use to estimate crop yield gaps determines the location of projected future agricultural “breadbasket” regions.

Interactions between fronts and the land-sea breeze lead to strong vertical exchange in the northern Gulf of Mexico.

Steven Smith joins an international team that will evaluate current data and identify what should be included for future climate modeling.

A PNNL team developed and used a model framework to understand the performance and structural reliability of a state-of-the-art solid oxide electrolysis cell design.

Iron-based fertilizer may stimulate plankton to pull carbon dioxide from the ocean, driving a carbon-negative process.

A new project will produce a database of optimized conditions for efficient, carbon negative recovery of energy-relevant minerals in red mud waste.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.