PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
Capstone engineering projects deliver equipment to improve accuracy of chemistry lab elutions and enhance training to safeguard critical infrastructure.
PNNL-Sequim scientists will spend the next year testing a new technology that could allow the ocean to soak up more carbon dioxide without contributing to ocean acidification.
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.
PNNL researchers developed the dummy payload to evaluate the performance of marine energy device prototypes in the Powering the Blue Economy: Ocean Observing Prize Competition.
PNNL research, featured on the cover of two science journals, describes advancements in using Raman spectrometry for Hanford Site nuclear waste remediation.
