Researchers at PNNL are sharing work grounded in One Health, which connects human, animal, plant, and environmental data to improve situational awareness, early warning, and decision-making.
PNNL has formed a partnership with the Orlando-based firm Refinity, which has licensed a key process from PNNL that will help the company convert mixed plastic waste into valuable industrial products like chemicals, plastics, and fuels.
A new report highlights a public workshop hosted by the National Academies Forum on Microbial Threats and features PNNL's Lauren Charles and the AI-Driven One Health Security program.
Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.
Matteo Muratori, director of transportation and industry programs at PNNL, has been named to the 2026–2028 cohort of the National Academies of Sciences, Engineering, and Medicine’s New Voices Program.
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.
PNNL researchers have found yet another way to turn trash into treasure: using algal biochar, a waste production from hydrothermal liquefaction, as a supplementary material for cement.
This summer, PNNL hosted the inaugural “As Conductive As Copper” (AC2.0) workshop, fostering a collaborative conversation on the future of the U.S. copper supply chain.
Shear Assisted Processing and Extrusion (ShAPE) imparts significantly more deformation compared to conventional extrusion. The latest ShAPE system at PNNL, ShAPEshifter, is a purpose-built machine designed for maximum configurability.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.