The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

EGRASS wins in the software/services category and is awarded a gold medal as special recognition in corporate social responsibility.

PNNL researchers will pursue both fundamental and applied projects with this support from the Department of Energy

The National Transmission Planning Study presents several transmission expansion scenarios that would reliably support the growing demand for energy across the nation.

Combining cold spray deposition and friction stir processing for a more efficient route to oxide dispersion strengthened steel.

PNNL researchers studied the microstructure of lithium silicates under emulated fusion conditions.

Three PNNL technologies have been named 2024 R&D 100 Award winners.

Researchers studied the boundary precipitation of tungsten heavy alloys in an extended high temperature irradiation environment

A new approach validates quantum algorithms for low-energy nuclear physics applications

The Department of Energy Office of Nuclear Energy acting assistant secretary makes his first visit to a national laboratory in his new role, touring PNNL's Radiochemical Processing Laboratory.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

PNNL research exploring tungsten heavy alloys for use in advanced nuclear fusion reactors makes Scientific Reports Top 100 Collection.

ChatGrid™ is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.

Program seeks to develop exceptional scientists, engineers, and operations staff who can materially advance DOE missions.

Results of the PR100 study outline near- and short-term strategies for Puerto Rico to achieve a renewable power grid by 2050.

Researchers designed ultra-low radiation cables to reduce background noise for highly sensitive neutrino and dark matter detectors.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

Scientists are studying tiny subatomic particles that pass through humans and possess clues about the origins of the universe.

PNNL’s Richard Saldanha will lead a new project to benchmark and validate models of cosmic ray exposure

Newly developed ultra-low radiation cables reduce background noise for neutrino and dark matter detectors.