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.
Distributed science is thriving at PNNL, where scientists share data and collaborate with researchers around the world to increase the impact of the work.
A closed-loop workflow brings together digital and physical frameworks to advance high-throughput experimentation on redox-active molecules in flow batteries.
This summer, PNNL hosted the inaugural “As Conductive As Copper” (AC2.0) workshop, fostering a collaborative conversation on the future of the U.S. copper supply chain.
Ampcera has an exclusive licensing agreement with PNNL to commercially develop and license a new battery material for applications such as vehicles and personal electronics.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
To improve our ability to “see” into the subsurface, scientists need to understand how different mineral surfaces respond to electrical signals at the molecular scale.
A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.
Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.