Hydrogen preferentially inserts at grain boundaries between interconnected chains of palladium nanoparticles, which have a lower energy barrier for hydrogen incorporation into the material.

A low-temperature, single-stage catalytic process converts PVC and polyolefin into valuable alkanes.

Scientists provide a guide for the rational application of continuum descriptions of fluid flows based on interfacial chemistry.

A team independently verified solid-state plutonium signal in nuclear magnetic resonance spectroscopy and acquired new fundamental insights of the physics and chemistry of plutonium dioxide.

PNNL researchers are opening up a new front in the fight to stop viral pathogens, protecting people against multiple viruses.

Capturing protein structural changes in host cells exposes vulnerabilities exploited by viruses to hijack cellular machinery.

Utilities across Washington join PNNL and the Washington State Department of Commerce to explore new tools and strategies for building resilient and reliable power systems.

Distributed science is thriving at PNNL, where scientists share data and collaborate with researchers around the world to increase the impact of the work.

From developing new energy storage materials to revealing patterns of Earth’s complex systems, studies led by PNNL researchers are recognized for their innovation and influence.

A closed-loop workflow brings together digital and physical frameworks to advance high-throughput experimentation on redox-active molecules in flow batteries.

An award-winning computing method called poly-streaming accelerates the analysis of massive datasets while using limited memory.

AI for Good event commemorates 60 years of community engagement, features AI applications, and offers practical AI tools to empower people.

Ice crystals are surprisingly tolerant of defects in their structure. The findings come from the first-ever molecular-resolution observations of nanoscale samples of ice frozen from liquid water.

Imaging ice microstructures formed by water freezing with atomic resolution for the first time.

A study by researchers at PNNL assessed the feasibility of using strontium isotope ratios and an existing machine learning–based model to predict and verify a product’s source—in this case, honey.

PNNL brings its expertise to the space domain by joining an organization that facilitates collaboration across the global space industry.

A comprehensive investigation provides quantitative data on the interaction between zeolite pores and linear alcohols, with hydroxyl group interactions playing the largest role.

PNNL researchers developed a new computational workflow to study day-to-night gene expression dynamics of cyanobacterium in real time.

This achievement earned DIMPLES a Best Paper award at the ACM International Conference on Supercomputing 2025.

Predicting how organisms’ characteristics respond to not only their genes, but also their environments (a nascent field called predictive phenomics), is extraordinarily challenging. Researchers at PNNL are using AI to tackle that challenge.