The Low-cost Earth-abundant Na-ion Storage consortium is a major effort to create superior, no-compromise batteries that replace lithium with inexpensive, domestically abundant sodium and use few—if any—critical materials.
High-resolution hydrodynamic-sediment modeling shows that inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics drive sediment deposition in Amazonian floodplains.
This study used historical data, remote sensing, and aquatic sensors to measure how far wildfire impacts propagated through the watershed after the 2022 Hermit’s Peak/Calf Canyon fire, New Mexico’s largest wildfire in history.
To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.
PNNL scientist James Stegen and an international team of collaborators recently published a comprehensive review of variably inundated ecosystems (VIEs).
This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.
PNNL researchers have published their paper, “Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data,” in the Journal of Proteome Research.
The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.
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.
Backed by $75,000 in Department of Energy funding from the Office of Electricity, a PNNL researcher works to refine solid-state sodium batteries for the grid.
Seawater threatens to intrude into coastal freshwater aquifers that millions of people depend on for drinking water and irrigation. This study investigates sea-level rise impacts on the global coastal groundwater table.
EZBattery Model allows energy storage researchers to more quickly and easily identify the best performing battery designs without the need for extensive physical prototyping or computationally expensive simulations.