Yong Wang will lead the Institute for Integrated Catalysis, advancing the science and technology of catalysis to address global challenges in energy resilience.
A study by researchers at PNNL assessed the feasibility of using strontium isotope ratios and an existing machine learning–based model to predict and verify a product’s source—in this case, honey.
A comprehensive investigation provides quantitative data on the interaction between zeolite pores and linear alcohols, with hydroxyl group interactions playing the largest role.
Two new publications provide emergency response agencies with critical insights into commercially available unmanned ground vehicles used for hazardous materials response.
Jonathan Barr, senior systems engineer at PNNL, was recently invited to co-present on a panel at the Texas Department of Emergency Management Annual Conference.
The Wildfire Mitigation Plan Database was built to support electric utilities, state governments, policymakers, and regulators in understanding and improving wildfire risk and resilience strategies.
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.
PNNL's “co-scientist” serves as a one-stop AI shop for accelerating scientific discovery. By leveraging AI agents, researchers can explore scientific databases, conduct analyses and request step-by-step plans for testing their hypotheses.
PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.
PNNL and one of the world’s largest tire makers will work to develop a commercially viable process that converts ethanol derived from sustainable sources or waste, like recycled tires, to butadiene, synthetic rubber’s main ingredient.
