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.

PNNL's accomplishments in FY 2024 help address the remaining challenges the wind industry faces.

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

PNNL will analyze current and projected transportation fuel dynamics, supply chain risks, and risk comparators with relevant sectors, such as transportation electrification.

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.

Klickitat Valley Health's fuel cell and microgrid project boosts rural healthcare, enhancing energy resilience and sustainability for social equity.

PNNL researchers co-organized a workshop on fusion commercialization with partners from industry and academia

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.

Secretary Granholm praises heat pump workforce development efforts at this year's instructor training program.

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.

Three PNNL-supported projects are at the forefront of developing advanced data analytics technologies to enhance the U.S. power grid’s reliability, resilience, and affordability.

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