PNNL partners with SRNL, FIU, and industry to test fixative technologies that could be used to immobilize radioactive contaminants on the surface of building materials during the deactivation and decommissioning of DOE legacy facilities.

Yong Wang’s joint appointment connects PNNL, WSU, and the next generation of scientists.

Using multiple specialized techniques, IDREAM researchers gained a better understanding of how trace impurities within gibbsite affect the hydrogen yield.

PNNL researchers are advancing AI-assisted nuclear fuel recycling to tap more than 90% of the energy potential left in used fuel, reduce waste and support secure, cost-effective next-generation reactors.

Five PNNL innovations earned 2026 R&D 100 Awards for advances in biotechnology, manufacturing, critical minerals, and nuclear security.

An innovative new approach enables direct measurements of electronic effects at solid–liquid interfaces.

Genesis Mission awards to PNNL will advance scientific discovery, next-generation energy systems and advanced manufacturing, among others.

Award-winning research advances AI applications for environmental cleanup.

PNNL scientists lead a first-of-its-kind study using real-time imaging to reveal how ultrasonic processing influences aluminum solidification.

PNNL’s nondestructive evaluation capabilities support advancements in nuclear energy, including research for the U.S. Nuclear Regulatory Commission to preserve the nation’s existing nuclear fleet.

A new invention from PNNL could soon help manufacturers more easily adopt the Lab’s patented Shear Assisted Processing and Extrusion (ShAPE™) technology, enabling faster manufacturing of superior extrusion products.

Mechanical engineer Scott Whalen is named the Department of Energy’s Innovator of the Year for turning the patented metal extrusion invention ShAPE™ into an industrially relevant extruded metal product juggernaut.

Chiral nanotubes facilitate the direction-specific diffusion of carbon dioxide to be faster than nitrogen gas.

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Critical minerals could be sourced from industrial waste at scale thanks to an AI-guided separation workflow developed by researchers at PNNL.

Graduate student researcher wins best poster awards from the American Ceramic Society.

High-Velocity Joining is a new joining technology that could reduce energy use and cost for manufacturers.

A newly published custom collection of glass formulas for nuclear waste vitrification — developed and validated by PNNL over the past three years — was made possible in that timeframe with AI tools, including a PNNL machine learning model.

At the summit, PNNL engaged with partners and investors, highlighting seven projects that accelerate the deployment of new energy technologies.

Replacing commercial acid with acidic waste enables researchers to improve nickel extraction efficiency, lower projected costs, and improve process economics.