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.

PNNL researchers continue to deliver high-quality, high-impact research on radioactive waste and nuclear materials management, earning “Papers of Note” and “Superior Paper” awards.

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.

PNNL researchers showcased subsurface sampling and remediation expertise at the 2025 Federal Remediation Technologies Roundtable.

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.

RemPlex and IAEA convene international leaders to tackle recruitment and retention in complex environmental cleanup efforts.

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

PNNL researchers earned five Papers of Note, 17 Superior Papers, and one poster award for their environmental remediation, radioactive waste, and nuclear energy-related presentations.

Frederick Day-Lewis, Lab Fellow and chief geophysicist at PNNL, was named the 2024 recipient of the Geological Society of America Public Service Award.

A PNNL Deep Vadose Zone Program publication that shows ferrihydrite helps protect groundwater is featured on the cover of ACS Earth and Space Chemistry.

Data scientist at PNNL receives the Environmental and Engineering Geophysical Society and Geonics Limited Early Career Award for work with geophysical modeling and subsurface inversion codes.

The Deep Vadose Zone program leads a treatability study for subsurface remediation of the Hanford Site’s Central Plateau.