From vehicles and airplanes to solid-phase processing of metals—how Curt Lavender and his team at PNNL solve industry problems with practical ingenuity.

The Department of Energy Art of Science contest entry focuses on the extraordinary universe of chemical bond making and breaking under irradiation.

Experiments focus on catalysts for off-road vehicles that optimize the use of critical minerals while lasting longer and costing less.

Machine learning and autonomous experimentation are poised to revolutionize how scientists grow very thin films on surfaces, important for technologies like microelectronics and quantum computing.

For PNNL’s Jonathan Evarts, Hope Lackey, and Erik Reinhart, this partnership with WSU opened doors and provided opportunities for their scientific careers to flourish.

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.

The environmentally friendly, streamlined solution for recovering valuable minerals from “e-waste” is also faster and more economical than conventional approaches—opening new doors to recycling materials from old devices.

Led by interns from multiple DOE programs, a newly expanded dataset allows researchers to use easy-to-obtain measurements to determine the elemental composition of a promising carbon storage mineral.

PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.

Controlling the nanostructure of silk fibroin—a protein found in silk—is a key step toward designing and fabricating electronics that leverage the material’s promising mechanical, optical and biocompatible properties.

Artificial intelligence, high-throughput experimentation, and materials science expertise combine to efficiently develop next-generation energy storage materials.

New funding spurs a new approach to researching the effective retrieval and processing of legacy radioactive waste. Four-year focus of the IDREAM EFRC will link attosecond timescales to decades-long chemical processes.

Long-duration energy storage gets the spotlight in a new Energy Storage Research Alliance featuring PNNL innovations, like a molecular digital twin and advanced instrumentation.

PNNL welcomed United Kingdom researcher for a six-week hands-on research exchange to develop nuclear forensic techniques.

PNNL’s patented Shear Assisted Processing and Extrusion (ShAPE™) technique is an advanced manufacturing technology that enables better-performing materials and components while offering opportunities to reduce costs and energy consumption.

PNNL researchers learn to control peptoid “handedness,” one key to precision drug delivery and diagnostics.

A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

The next-generation ShAPE machine has arrived at PNNL, where it will help prove the mettle of the ShAPE extrusion technique. ShAPE 2 is designed to allow researchers to produce larger, more complex extrusions.

A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.

Researchers at PNNL have developed a new technique to get a high-resolution look at how—and why—corrosion happens.