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 scientist Gokul Iyer was co-lead author of an award-winning paper that assessed the impact of pledges of more than 100 nations to reduce greenhouse gas emissions
Study demonstrates that choosing more accurate numerical process coupling helps improve simulation of dust aerosol life cycle in a global climate model.
Researchers seek to bring down costs, address potential environmental risks and maximize the benefits of harnessing wind energy above the deep waters of the Pacific.
The SHASTA program is doing a deep dive on subsurface hydrogen storage in underground caverns, helping to lay the foundation for a robust hydrogen economy.
Researchers used a combination of sophisticated laboratory incubations and field measurements to determine the role of microbial production and consumption of methane in soils with different exposure to tidal inundation
Researchers show that small-scale turbulent fluctuations lead to larger concentrations of cloud droplets than would be possible in conventional models of atmospheric clouds
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.
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.