Ampcera has an exclusive licensing agreement with PNNL to commercially develop and license a new battery material for applications such as vehicles and personal electronics.
Researchers at PNNL are pursuing new approaches to understand, predict and control the phenome—the collection of biological traits within an organism shaped by its genes and interactions with the environment.
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’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.
Scientists at PNNL have published a new article that focuses on understanding the composition, dynamics, and deployment of beneficial soil microbiomes to get the most out of soil.
A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.
Clean hydrogen energy infrastructure is coming to the Pacific Northwest with a newly announced hydrogen hub, and PNNL experts are advising the work to come.
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.
SAGE is a high-efficiency genome integration strategy for bacteria that makes the stable introduction of new traits simple for newly discovered microbes.
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.
Steven Spurgeon’s research is featured in the cover of the MRS Bulletin along with his team’s invited perspective on the future of machine learning for electron and scanning probe microscopy.
