Demonstrated that cathodic potential accelerates aqueous phase benzaldehyde hydrogenation.

Amine functionalized graphene oxide yields laminates with an enhanced capacity to adsorb rare earth elements, improved water transport, and greater stability for effective membrane-based separation of critical minerals.

Establishing how electronic redistribution, protonation, and solvent rearrangement are coupled during a proton-coupled electron transfer process.

By selectively stabilizing either a right- or left-handed helical pose of its substrate, an enzyme tunes the final product.

Research provided definitive spectroscopic characterization, reactivity, and deactivation of a reactive monomeric diphosphine-copper hydride intermediate.

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

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

New software enables the study of nuclear quantum effects in large, condensed phase systems up to 10× faster than traditional methods.

New theoretical framings of quantum dynamics enable better quantum-based simulation of molecular systems and dynamics.

Protein and water environments set the overall redox scale of iron-sulfur clusters, but local coordination geometry fine-tunes the final potential.

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.

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.

An international research team deployed AI-powered computing to solve important chemical structures for energy applications using NVIDIA hardware.

A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

Matteo Muratori, director of transportation and industry programs at PNNL, has been named to the 2026–2028 cohort of the National Academies of Sciences, Engineering, and Medicine’s New Voices Program.

Secretary Wright visited PNNL's campus in Richland, Washington, late last year to meet with innovators in chemistry, biology, computing, and more.