Reproducible workflows for binning and visualizing nuclear magnetic resonance spectra from environmental samples.

ChatGrid™ is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.

PNNL researchers use chemometric analysis to model chemistry in molten salt.

Scientists stop the motion of atoms to watch electrons move in liquid water.

Program seeks to develop exceptional scientists, engineers, and operations staff who can materially advance DOE missions.

Results of the PR100 study outline near- and short-term strategies for Puerto Rico to achieve a renewable power grid by 2050.

Differences in water interactions lead to two gibbsite dissolution pathways modulated by solution acidity.

A simple gel-based system separates metals ions from a model solution of dissolved battery electrodes without the need for specialty chemicals, membranes, or toxic solvents.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

Physicist Elke Arenholz’s contributions to x-ray magnetic spectroscopy led to her elevation to Fellow status by the IEEE.

Irradiation in a sodium hydroxide solution increases the dissolution rate and particle roughness of gibbsite compared to dry irradiation.

From energy storage to cybersecurity, this 2023 wrap-up offers a glimpse of PNNL innovations with a seasonal twist.

PNNL research unlocks key findings needed for potential commercial deployment of carbon mineralization technology.

Elias Nakouzi brings 3D atomic force microcopy expertise to a project focused on understanding how hybrid bio-based nanomaterials assemble.

A seemingly simple shift in lithium-ion battery manufacturing could pay big dividends, improving electric vehicles’ ability to store more energy per charge and to withstand more charging cycles.

Understanding the risk of compound energy droughts—times when the sun doesn’t shine and the wind doesn’t blow—will help grid planners understand where energy storage is needed most.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

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.

This is the first time a flow battery with 24-hour discharge capacity will be deployed and tested in a field environment.

Researchers designed ultra-low radiation cables to reduce background noise for highly sensitive neutrino and dark matter detectors.