In a water-containing system, the open circuit potential stabilizes positively charged species and lowers the energy barrier of the reaction.

Researchers found that specific landscape metrics consistently outperform traditional population downscaling methods.

The flux of carbon dioxide from forest soil may acclimate to some aspects of climate change.

Identifying links between large-scale and local-scale model biases in tropical precipitation.

Harnessing the sea for decarbonized energy is the focus of a new collaboration between PNNL and the University of Guam.

A systematic, multiple scenario approach was used to analyze the compounding impacts of demands for land for biofuels with increased land scarcity under a diverse set of uncertainties.

Interactive freshwater flux works to prolong density anomalies over deep water formation areas in the North Atlantic.

A new approach broadens the reach of quantum computers to previously challenging systems.

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

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

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

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

The nature of an iron oxide mineral and amount of light control electron movement from a light-absorbing molecule to the mineral, rather than the conditions of the surrounding aqueous environment.

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

Researchers identified a new intermediate phase that forms during the crystallization of hydrated aluminum-containing salts.

PNNL researchers are moving energy research forward through collaborative projects with universities across the United States.

One year into an Early Career Research Program award, Marcel Baer is developing new workflows to model biologically inspired systems more efficiently.

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.

When wildfires burn in the west, the heat and airborne particles they release inflict stronger rain and larger hail upon central states.