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 researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
Staff at PNNL recently traveled to Cyprus to facilitate a multilateral workshop on chemical forensics investigations hosted by the U.S. Department of State, Office of Weapons of Mass Destruction Terrorism.
Capstone engineering projects deliver equipment to improve accuracy of chemistry lab elutions and enhance training to safeguard critical infrastructure.
PNNL research, featured on the cover of two science journals, describes advancements in using Raman spectrometry for Hanford Site nuclear waste remediation.
Rotational Hammer Riveting, developed by PNNL, joins dissimilar materials quickly without preheating rivets. The friction-based riveting enables use of lightweight magnesium rivets and also works on aluminum and speeds manufacturing.