Artificial intelligence helps analyze images and reduce uncertainty for a key measure of watershed function.

Dynamic organic matter traits tied to reactions can surpass static descriptors in explaining decomposition rates and responses to environmental change.

Using an AI approach to combine outputs from microbial models and apply them as inputs to reactive-transport models reduces computational time by several orders of magnitude.

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

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

Tracking water age using a new precipitation tagging method within a hydrologic model generates key information to support hydropower energy and other water-dependent systems.

Following wildfire, the transformation and mobility of phosphorus-containing compounds depend on the type of vegetation burned and the severity of the fire.

Higher temperatures and land-use pressures were associated with weaker chemical separation between river water and sediment dissolved organic matter across 93 sites.

​​Tests show which low-cost 3D method best matches field measurements to minimize errors in mapping headwaters.​

More rain and less snow shorten how long water stays in watersheds.

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.

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

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.

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

PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.

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

Researchers discovered that a polymer additive promotes smooth, layer-by-layer deposition on metal electrodes by tuning interactions with the substrate.

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.