PNNL is central to the Genesis Mission, leading and participating in projects central to the nation’s security and international competitiveness.

The Hanford Site is now immobilizing radioactive waste in glass: a process known as vitrification. PNNL contributed 60 years of materials science expertise—and is providing operational support—to help the nation meet this cleanup milestone.

PNNL offers a third-party testing environment to provide battery companies with independent data about their grid-scale systems.

Mystery of how important aluminum minerals form and dissolve solved with materials science and advanced imaging studies.

Nanosize platelets of an aluminum material slide and join in a staggered orientation to form larger crystals.

GSL's utility-grade battery testing operation is now fully operational.

In a recent study, PNNL researchers found that noncrystalline mineral surfaces can bind microbial residues to build soil organic matter.

Pioneering autonomous science facility puts a big focus on tiny microbes and AI-driven science.

IDREAM researchers uncovered how dimeric complexes control how aluminum hydroxide minerals dissolve.

PNNL’s SHAD-TAGS+ allows researchers to monitor fish species to help inform energy project development.

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.

PermitAI’s newly released dataset, NEPATEC 2.0, captures and curates environmental review data for faster, better federal permitting.

Understanding molecular modifications of proteins in the red yeast could help scientists re-engineer the organism’s phenotype.

Researchers have begun a series of experiments that could bring about more energy for grid, thanks to a special delivery of 11 "high burnup" rods.

Trace amounts of iron and chromium can incorporate into gibbsite, affecting how the mineral dissolves.

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.

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.