From developing new energy storage materials to revealing patterns of Earth’s complex systems, studies led by PNNL researchers are recognized for their innovation and influence.

A breakthrough at PNNL could free friction stir from current constraints—and open the door for increased use of the advanced manufacturing technique on commercial assembly lines.

The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.

Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

Scientists have developed a way to detect tiny amounts of hard-to-detect explosives more than eight feet away.

Senior Advisor Isabella van Rooyen shares her journey from child to internationally recognized scientist in nuclear materials and manufacturing.

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

Sue Southard's one thousand dives as a PNNL staff member leave a ripple effect on efforts to keep our ocean healthy, our economy thriving, and our waters safe.

A paper by PNNL scientists on nuclear explosion monitoring technology is among top articles in nuclear instruments journal to draw most social media “buzz.”

Science and engineering solutions helping keep America safe from explosive, radiological, biological and cyber threats.

Rotational Hammer Riveting, developed by PNNL, joins dissimilar materials quickly without preheating rivets. The friction-based riveting enables use of lightweight magnesium rivets and also works on aluminum and speeds manufacturing.

Tomorrow’s scientific leaders gain real-world experience contributing to PNNL’s research and development.