Secretary Wright visited PNNL's campus in Richland, Washington, late last year to meet with innovators in chemistry, biology, computing, and more.

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

For nearly 20 years, PNNL has invested in a multidisciplinary effort to develop better batteries.

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.

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.

PNNL researchers developed a new computational workflow to study day-to-night gene expression dynamics of cyanobacterium in real time.

Predicting how organisms’ characteristics respond to not only their genes, but also their environments (a nascent field called predictive phenomics), is extraordinarily challenging. Researchers at PNNL are using AI to tackle that challenge.

PNNL researchers have found yet another way to turn trash into treasure: using algal biochar, a waste production from hydrothermal liquefaction, as a supplementary material for cement.

Researchers are uncovering the secrets of a fast-acting molecular messaging network that helps determine how the genetic code plays out.

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.

Big energy storage science is brewing at GSL just one year after its groundbreaking.

Alex Beliaev, a biologist at PNNL, was named a Society for Industrial Microbiology and Biotechnology fellow.

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.

Matt Fayette, a materials scientist, is testing a new method to extend battery life for grid storage by circulating the electrolyte.