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

Scientists uncover how cyanobacteria's circadian cycle controls carbon and energy metabolism, increasing carbon dioxide-to-bioproducts yields.

Scientists map how transitions from day to night control gene regulatory networks in cyanobacteria, revealing key orchestrators of metabolic switching.

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.

PNNL scientists use proteomics to unveil how high-density lipoproteins protect against cholesterol accumulation in the artery wall.

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

Computing and artificial intelligence aid scientists who seek to understand function-driven design and control of biological systems.

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.

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