PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
A switchable single-atom catalyst is activated in the presence of surface intermediates and reverts to its stable inactive form when the reaction is completed.
Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.
Catalysts that efficiently transfer hydrogen for storage in organic hydrogen carriers are key for more sustainable generation and use of hydrogen. New research identifies activity descriptors that can accelerate novel catalyst development.
Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.
Highly precise and controllable single-atom catalysts are affected by reaction conditions, which can alter the bonding around the atoms and the activity.
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.
