The correlation between weld flash emissions and melt pool composition can provide near real-time feedback on weld quality.

A PNNL team has developed an energy- and chemical-efficient method of separating valuable critical minerals from dissolved solutions of rare earth element magnets.

Increasing concentrations of ions slows the rate of particle aggregation in boehmite suspensions.

The structure and exposed surfaces of hematite particles affect their growth and dissolution.

Researchers developed a robust, cost-effective, and easy-to-use cap-based technique for spatial proteome mapping, addressing the lack of accessible proteomics technologies for studying tissue heterogeneity and microenvironments.

Chromium cations better adapt to changes in oxygen stoichiometry than iron cations.

Molecular simulations highlight the key interactions between mineral surfaces and organic matter that lead to carbon stabilization in soils.

Using interfacial charge transfer to control cation charges in oxide superlattices offers insights for designing functional heterostructures.

New IDREAM research explores the effects of electrolyte species on boehmite nanoparticle aggregation in legacy wastes.

Precipitation with sodium hydroxide in a flow-gel device enables the rapid extraction of magnesium from seawater in a new scalable process.

Calcium carbonate mineralization occurs through a multi-step process that begins with a dense liquid phase.

Nineteen PNNL-affiliated researchers have been named to Clarivate’s 2024 list of the world's most highly cited researchers.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

A newly funded project will develop the fundamental understanding of phenomena that underpin material properties important for quantum applications.

Research identifies the mechanisms through which peptoids affect ions in solution and a mineral surface, increasing the rate of carbonate crystal growth.

Pacific Northwest National Laboratory scientists discussed their materials research at Accelerate’24.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

Pyrocumulonimbus clouds are increasing in frequency as large wildfires become more prevalent in a warming climate. These clouds can inject smoke particles into the atmosphere, where they can remain suspended for several months.

Using numerical simulations to reproduce the laboratory experiments, this study reveals that liquid droplets are present near the bottom surface, which warms and moistens the air in the chamber.

PNNL researchers will pursue both fundamental and applied projects with this support from the Department of Energy