Zhiqun (Daniel) Deng, Lab Fellow at PNNL, has been named a fellow of the American Society of Mechanical Engineers, an honor that recognizes outstanding engineering achievements.
PNNL’s science and technology helps hydropower operators detect, prevent and recover from cyberattacks while protecting a source of electricity that enhances grid reliability and resilience.
Researchers at PNNL advised elementary and middle school student teams with their problem-solving research for the FIRST® LEGO® League robotics competitions.
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’s experts in electrification advised ports how to modernize the use of energy resources at the Port of Anacortes. Now they will help do the same with several others.
The ARPA-E Energy Innovation Summit brings together researchers, industry leaders, entrepreneurs, and investors to showcase the latest technologies shaping tomorrow’s energy landscape. This year, eight projects led by PNNL were featured.
By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
PNNL was well represented at the NAWEA/WindTech 2024 Conference with 13 PNNL experts at the conference sponsored by the North American Wind Energy Academy.
PNNL will analyze current and projected transportation fuel dynamics, supply chain risks, and risk comparators with relevant sectors, such as transportation electrification.
A new analysis shows how renewable energy sources like solar, wind and hydropower respond to climate patterns, and how utilities can use this data to save money and invest in energy storage.
Alicia Amerson's passion for science communication, expertise in marine mammal research, and experience in wildlife photography provide a robust foundation for her new role with the Clallam County Marine Resources Committee.
A new digital twin platform can help hydropower dam operators by providing accurate and predictive models of physical turbines that improve facilities and enhance reliability.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
