PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

Twenty years after the first radiation portal monitor was installed, PNNL continues supporting the Department of Homeland Security’s efforts to detect and prevent terrorist weapons from crossing our borders.

The amount of radiation in a system influences how fast aggregated boehmite particles dissolve.

Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

In honor of National Intern Day, PNNL and intern Jonathan Mills were named among the Top 100 in the nation by early-career recruiting firm WayUp.

Simulations show that pH influences boehmite nanoparticle structure and sorption ability.

Developing a new understanding of the structure of natrophosphate, a complex mineral found in radioactive tank waste at the Hanford Site, by integrating experimental techniques.

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

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

As wildfires grow increasingly severe, PNNL researchers are working to slow their spread and mitigate their harmful effects.

TransMED analyzes patient data from similar diseases across multiple sources to understand COVID-19 patient outcome risk factors.

Eight PNNL research papers were recognized by the 2022 Waste Management Symposia as “Papers of Note” and “Superior.” One paper received a Best Oral Paper/Presentation Award.

The size and concentration of ions influences the aggregation behavior of solutions of boehmite.

A new longer-lasting sodium-ion battery design is much more durable and reliable in lab tests. After 300 charging cycles, it retained 90 percent of its charging capacity.

PNNL’s Rob Howard received the 2022 Fuel Cycle and Waste Management Award from the American Nuclear Society.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.

Performing a network analysis of forces between particles reveals new insight into shear induced thinning and thickening in non-Newtonian fluids.

IDREAM researchers show that high concentrations of sodium hydroxide significantly impact the molecular and macroscale properties of sodium nitrite solutions.

PNNL experts perform fundamental climate science work at multiple scales to understand impacts on energy, water, food, and infrastructure systems.