Jingshan Du, a postdoctoral scientist at PNNL whose research focuses on crystallization pathways of water and other materials, was named a 2025 CAS Future Leader.
Machine learning and autonomous experimentation are poised to revolutionize how scientists grow very thin films on surfaces, important for technologies like microelectronics and quantum computing.
Over the next four years, PNNL and University of Arizona will develop open-source computational tools to better identify and characterize the viruses associated with the human microbiome.
Researchers developed a robust, cost-effective, and easy-to-use cap-based technique for spatial proteome mapping, addressing the lack of accessible proteomics technologies for studying tissue heterogeneity and microenvironments.
Controlling the nanostructure of silk fibroin—a protein found in silk—is a key step toward designing and fabricating electronics that leverage the material’s promising mechanical, optical and biocompatible properties.
This study evaluated the sensitivity of multiple geophysical methods to measure and evaluate the spatiotemporal variability of select soil properties across terrestrial–aquatic interfaces.
Researchers integrated field measurements, lab experiments, and model simulations to study oxygen consumption dynamics in soils along a coastal gradient.
This research explores how changes in groundwater levels affect the chemistry of underground water, especially in areas where land meets water, like wetlands.