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.
Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.
Replacing commercial acid with acidic waste enables researchers to improve nickel extraction efficiency, lower projected costs, and improve process economics.
Localized gradients in magnetic fields have long-range effects on the concentration of rare earth ions in solution, facilitating field-driven extraction of critical minerals.
CESER and PNNL convened a three-day summit with more than 100 state officials, cybersecurity experts, and industry leaders across 35 states to advance energy security planning, cyber risk assessment, and fortify protections against attacks.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.
Pacific Northwest National Laboratory launches the Training Outreach and Recruitment for Cybersecurity Hydropower program at the University of Texas at El Paso.
The Public Infrastructure Security Cyber Education System is a university-community-nonprofit collaboration changing cyber education and cybersecurity.
New mathematical tools developed at PNNL hold promise to transform the way we operate and defend complex cyber-physical systems, such as the power grid.