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.
By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.
The environmentally friendly, streamlined solution for recovering valuable minerals from “e-waste” is also faster and more economical than conventional approaches—opening new doors to recycling materials from old devices.
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.
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.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
To improve our ability to “see” into the subsurface, scientists need to understand how different mineral surfaces respond to electrical signals at the molecular scale.
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.
The Department of Energy Office of Nuclear Energy acting assistant secretary makes his first visit to a national laboratory in his new role, touring PNNL's Radiochemical Processing Laboratory.
A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.
PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.
The world is becoming reliant on increasingly smaller sensors that improve daily life in many ways. A PNNL-led paper takes a closer look at these technologies and their future development for environmental and sensitive species monitoring.
