PNNL researchers are moving energy research forward through collaborative projects with universities across the United States.

The nature of an iron oxide mineral and amount of light control electron movement from a light-absorbing molecule to the mineral, rather than the conditions of the surrounding aqueous environment.

Researchers identified a new intermediate phase that forms during the crystallization of hydrated aluminum-containing salts.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

One year into an Early Career Research Program award, Marcel Baer is developing new workflows to model biologically inspired systems more efficiently.

A new project will produce a database of optimized conditions for efficient, carbon negative recovery of energy-relevant minerals in red mud waste.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

Silicone wristbands could be more convenient option for collecting exposure data.

Space Systems Command and PNNL welcome the University of Texas at El Paso to the Cyber Halo Innovation Research Program.

A review article examines a decade of work on the atom-scale process of turning carbon dioxide into stable minerals.

HiSVSIM enables large-scale quantum simulations through graph partitioning.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new deep learning approach allows researchers to rapidly analyze three-dimensional representations of multi-component catalysts.

Where molybdenum dopants sit within catalytic material affects catalysis at a mechanistic level.

Using electrocatalysis offers a different path for reactions involving polar groups.

The amount of radiation in a system influences how fast aggregated boehmite particles dissolve.

PNNL research, featured on the cover of two science journals, describes advancements in using Raman spectrometry for Hanford Site nuclear waste remediation.

A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.

Launching a premier partnership, PNNL and the University of Texas El Paso (UTEP) signed a memorandum of agreement on UTEP’s campus on October 6.