This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

Senior Advisor Isabella van Rooyen shares her journey from child to internationally recognized scientist in nuclear materials and manufacturing.

PNNL is highlighting scientific and technical experts in the national security domain who were recently promoted to scientist and engineer level 5, one of PNNL’s most senior research roles.

Chemist April Carman was recognized for her career accomplishments with the Professional Achievement Award from the University of Nevada, Reno, College of Science.

Science and engineering solutions helping keep America safe from explosive, radiological, biological and cyber threats.

PNNL commemorates 9/11 and reflects on the 20 years of science and technology produced since to protect against threats and make America safer.

Rotational Hammer Riveting, developed by PNNL, joins dissimilar materials quickly without preheating rivets. The friction-based riveting enables use of lightweight magnesium rivets and also works on aluminum and speeds manufacturing.

New study elucidates the complex relaxation kinetics of supercooled water using a pulsed laser heating technique at previously inaccessible temperatures.

PNNL's Rich Ozanich, project manager of opioids standards and equipment testing, served on an expert panel about opioid detection as part of a Department of Homeland Security S&T research and development showcase.

PNNL’s infrared pulsed heating technique reveals supercooled water’s weird behavior; opens door to other fluid studies at new Energy Sciences Center.

In the face of the COVID-19 pandemic, PNNL pivoted to online learning approaches to conduct critical national security training across the globe.

PNNL researchers are using explainable artificial intelligence to develop new ways to understand and interpret data for national security.

Explainable artificial intelligence—understanding and explaining the reasoning behind AI decisions—is a necessity for national security.

Explainable AI helps scientists understand and untangle the threads of information that the system uses to make choices or recommendations.

In 2020, virtual Washington State University teams successfully worked together in a program sponsored by the National Nuclear Security Administration’s (NNSA) Office of International Nuclear Safeguards.

A recent edition of the Infrastructure Resilience Research Group Journal featured an article written by PNNL researchers Rob Siefken and Jake Burns about “Design Basis Threat and the Low Threat Environment.”

As a physicist at PNNL, Jon Burnett’s work is about developing instruments to detect ultra-trace radionuclide signatures, analyze samples from around the world to look for evidence of nuclear explosions, and then interpret that information.

A new review paper led by senior research scientist Chun-Long Chen and featured on the cover of Accounts of Chemical Research summarizes advances by PNNL scientists in developing sequence-defined peptoids.