PNNL evaluates bacterial, fungi, and algae strains as part of a four-step process to streamline and standardize biomanufacturing processing.

PNNL is working with national laboratories and academia to provide electric vehicle manufacturers with batteries that are more reliable, high-performing, safe, and less expensive.

PNNL provides tools and expertise to bring faster, cost-effective catalysts to industry that can be produced at a large scale.

PNNL and their collaborators are investigating combinations of fuels to boost vehicle efficiency, performance, and affordability.

PNNL is leading an effort to reveal the potential of algae for biofuels to replace conventional fuels for transportation.

A multi-institution research team led by PNNL is addressing curb usage management challenges in large urban areas by developing a city-scale dynamic curb use simulation tool and an open-source curb management platform. 

The HydroPASSAGE project addresses hydropower challenges, which include efforts to improve environmental performance of hydropower.

HydroWIRES initiative helps water stakeholders reap the full benefit of hydropower’s superpower: versatility

The Institute for Integrated Catalysis (IIC) at Pacific Northwest National Laboratory explores and develops the chemistry and technology of catalyzed processes that enable a carbon-neutral future.

The U.S. Department of Energy-sponsored Internet of Things Upgradeable Lighting Challenge is designed to encourage the widespread adoption of IoT-Upgraded Lighting.

PNNL is leading a consortium that provides funding opportunities to the automotive industry for accelerating new lightweight technologies in on-highway vehicles.

The Salish Sea Model (SSM) is a predictive coastal ocean model for estuarine research, restoration planning, water-quality management, and climate change response assessment.

A software suite for working with neutron activation rates measured in a nuclear fission reactor, an accelerator-based neutron source, or any neutron field to determine the neutron flux spectrum using a generalized least-squares approach.