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 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.
PNNL and collaborators developed new models—recently approved by the U.S. Western Electricity Coordinating Council (WECC)—to help utilities understand how new grid-forming inverter technology will enhance grid stability.
PNNL’s patented Shear Assisted Processing and Extrusion (ShAPE™) technique is an advanced manufacturing technology that enables better-performing materials and components while offering opportunities to reduce costs and energy consumption.
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.
Understanding the risk of compound energy droughts—times when the sun doesn’t shine and the wind doesn’t blow—will help grid planners understand where energy storage is needed most.
PNNL led one of five Pathway Summer School programs nationwide, with a specific focus on engaging students from Native American or Indigenous backgrounds.
The Department of Energy’s Vehicle Technologies Office recently issued two awards to researchers at PNNL for their contributions to areas that are crucial for the expansion of electric vehicles.
At the Nonproliferation, Counterproliferation, and Disarmament Science Gordon Research Conference, researchers from PNNL shared research and scientific approaches for countering diverse threats.
The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.
PNNL researchers developed a new model to help power system operators and planners better evaluate how grid-forming, inverter-based resources could affect the system stability.
