Researchers developed a way to precisely control dopant structures and locations in carbon-based materials.

Shear Assisted Processing and Extrusion (ShAPE) imparts significantly more deformation compared to conventional extrusion. The latest ShAPE system at PNNL, ShAPEshifter, is a purpose-built machine designed for maximum configurability.

Researchers developed a pathway for the solution synthesis of core–crown heterostructures with controlled electronic properties.

Under an optical field, nanoparticle attachment in a model system heavily depends on surface roughness.

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

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

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

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

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

Fully identifying what controls nanocrystal assembly requires incorporating non-traditional forces into models.

Combining X-ray-based techniques allows researchers to locate and characterize small concentrations of electrons in complex semiconductor films.

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

Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.

The pathway of oxygen ion movement can be controlled based on the initial structure and substrate of strontium iron oxide thin films.

PNNL is supporting the floating offshore wind industry to enable gigawatt-scale development of floating offshore wind in the United States while minimizing environmental impacts and supporting local workforces.

PNNL researchers demonstrated a simple method to create stable, identical nanoparticles of PdTe2-like composition, which is known to be superconducting, on a WTe2 TMD support.

A combined experimental and theoretical study identified multiple interactions that affect the performance of redox-active metal oxides for potential electrochemical separation and quantum computing applications.

A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.