PNNL partnered with the Treasury and AWS to develop Cache, a cloud-based tool that allows the Treasury’s disparate data to be easily searched, translated, extracted, linked, and analyzed.
PNNL’s pioneering CETC project with regional universities demonstrates transactive controls among multiple commercial buildings and devices for energy efficiency and grid reliability.
PNNL is helping communities with significant historical ties to fossil energy understand opportunities and pursue numerous federal resources available to support coal power plant redevelopment.
The CVAir pilot project transports computed tomography passenger baggage data from originating international airports for targeted flights destined for the U.S.
The Computational and Theoretical Chemistry Institute (CTCI) aspires to establish a premier international center for chemistry and materials science software at extreme scales.
In January 2024, CESER—in partnership with GDO, NASEO, and PNNL—created a set of state energy security cohorts to support the coordination and technical development of state energy security planning, assessment, and mitigation.
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.
E4D is a 3D geophysical modeling and inversion program designed for subsurface imaging and monitoring using static and time-lapse electrical resistivity tomography (ERT), spectral induced polarization (SIP) and travel-time tomography data.
PNNL is a leader in the integration of aberration-corrected electron microscopy, in-situ techniques, and atom probe tomography to address challenges in nuclear materials, environmental remediation, energy storage, and national security.
PNNL is laying the groundwork for advancing energy equity and environmental justice through research to develop an innovative energy system that benefits everyone
GeoBOSS is a software library that combines the data-handling capabilities of Spark and the user-friendliness of Python to simplify geospatial analytics and the transition between small-scale research and large-scale operational projects.