The NNSA Office of Defense Programs recognized efforts of a team that made sure a $100 million basic ordering agreement was awarded and approved on time.

Vrabie co-organized the 2022 Workshop on Artificial Intelligence for Isotope R&D and Production for the Department of Energy Isotope Program.

Researchers combined crystallographic data and computational studies to investigate plutonium-ligand bonding within a hybrid material construct.

The American Society of Mechanical Engineers recognized radioactive material transportation expert Matt Feldman with its 2022 Dedicated Service Award.

David Senor has been selected for the Department of Energy’s Fusion Energy Sciences Advisory Committee.

PNNL research, featured on the cover of two science journals, describes advancements in using Raman spectrometry for Hanford Site nuclear waste remediation.

PNNL experts in nuclear forensics helped lead laboratories around the world through a rigorous test involving nuclear materials.

The size of the alkali metal within the material influences the crystallization pathway.

Two PNNL teams have been recognized for their work on safe, secure radioactive material shipping.

Combining advanced powder synthesis with solid state processing allows researchers to streamline oxide dispersion-strengthened steel fabrication.

Different sized helium cavities that form after ion irradiation are unevenly distributed in a ductile-phase toughened tungsten composite.

A PNNL team is leading the design, fabrication, and regulatory testing, and delivery of new packaging units that will be used to ship radioactive materials safely and securely.

PNNL is lending analytics expertise to a new first-of-its-kind electric-vehicle carsharing program being launched in Hood River, Oregon.

A webapp developed by PNNL in collaboration with the University of Washington to help drive efficiencies for urban delivery drivers is now in the prototype stage and ready for testing.

Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.

Researchers at PNNL are contributing artificial intelligence, machine learning, and app development expertise to a U of W project that will ease challenges with urban freight delivery. The project will provide delivery drivers with a tool