A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.
A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.
In 2006, battery research was practically non-existent at PNNL. Today, the lab is lauded for its battery research. How did PNNL go from a new player to a leader in state-of-the-art storage for EVs and the grid?
The Health Physics Society has selected Jonathan Napier, a PNNL environmental health physicist, to serve as a delegate to the International Radiation Protection Association’s General Assembly.
How do you make an operational technology assurance course more relevant to attendees? Washington State University students brought a fresh perspective by designing and fabricating a realistic mock training system—a vintage-style glove box.
Harish Gadey, David Peeler, and Tom Brouns named to Waste Management Symposia Program Advisory Committee positions to help develop radioactive waste management discussions.
A team of scientists at PNNL developed new computational models to predict the behavior of these impurities and reduce the expense and risk related to actinide metal production.
PNNL has joined Gender Champions in Nuclear Policy, a leadership network that brings together leaders of organizations working in nuclear policy who are committed to breaking down gender barriers.