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.

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.

This study evaluated the sensitivity of multiple geophysical methods to measure and evaluate the spatiotemporal variability of select soil properties across terrestrial–aquatic interfaces.

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.

Researchers integrated field measurements, lab experiments, and model simulations to study oxygen consumption dynamics in soils along a coastal gradient.

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

This research explores how changes in groundwater levels affect the chemistry of underground water, especially in areas where land meets water, like wetlands.

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