With the help of the U.S. State Department, PNNL climate scientists assisted counterparts in Malaysia and Thailand in exploring carbon neutral goals.

PNNL’s Center for the Remediation of Complex Sites convened attendees from around the world to discuss challenges associated with environmental contamination.

Ang participated in a White House-hosted CHIPS R&D event and roundtable discussion with senior leaders from industry, academia and key government agencies.

Scientists stop the motion of atoms to watch electrons move in liquid water.

Clustering wildfires into four types based on their land-atmosphere drivers can potentially improve regional wildfire predictions.

Differences in water interactions lead to two gibbsite dissolution pathways modulated by solution acidity.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

Three scientists at PNNL are lending more than 50 years of expertise to lead efforts to advance the nation’s Blue Economy.

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.

PNNL’s Climate Network with MSIs and HBCUs aims to bring opportunities to diverse populations.

Identifying how curvature affects the doping and hydrogen binding energies of carbon-based materials provides a framework for designing hydrogen storage materials.

Atmospheric rivers, filaments of intense moisture transport in the atmosphere, can now be automatically detected in satellite observations.

Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.

Combining X-ray-based techniques allows researchers to locate and characterize small concentrations of electrons in complex semiconductor films.

A simple gel-based system separates metals ions from a model solution of dissolved battery electrodes without the need for specialty chemicals, membranes, or toxic solvents.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

Proposing a new synthetic route to layered materials that demonstrate efficient photocatalysis.

PNNL study explored multiple factors that could impact future snowpack—a critical source of water for communities, irrigation and energy generation.

A 19-person, multi-institutional national laboratory team received the inaugural Gordon Bell Prize for Climate Modeling from the Association for Computing Machinery for their work on more accurately modeling deep convective clouds.