Lauren Charles, a chief data scientist at PNNL, showcased the vital research coming out of her program at The National Academies Forum workshop in Washington, D.C., January 15–16, 2025.
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.
PNNL biodefense experts seek to identify, understand and mitigate the risks of biological pathogens—whether naturally occurring or intentionally created—so steps can be taken to prepare and respond.
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.
In a new paper, researchers point to three major efforts where the biggest climate mitigation gains stand to be realized: ramping up carbon dioxide removal, reigning in non-carbon dioxide emissions and halting deforestation.
A new discovery by PNNL researchers has illuminated a previously unknown key mechanism that could inform the development of new, more effective catalysts for abating NOx emissions from combustion-engines burning diesel or low carbon fuel.
Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.