PNNL has formed a partnership with the Orlando-based firm Refinity, which has licensed a key process from PNNL that will help the company convert mixed plastic waste into valuable industrial products like chemicals, plastics, and fuels.

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

Researchers discovered that a polymer additive promotes smooth, layer-by-layer deposition on metal electrodes by tuning interactions with the substrate.

Anisotropies in surface potential driven by competitive ion adsorption can alter facet selectivity during oriented attachment.

Summarizing the state of designed protein hybrid materials, researchers celebrate both the 50th anniversary of the MRS Bulletin and the 2025 Fred Kavli Distinguished Lecturers in Materials Science, Jim De Yoreo and David Baker.

Specially designed histidine-containing peptoids mimic carbonic anhydrase to accelerate calcite step growth.

Imaging ice microstructures formed by water freezing with atomic resolution for the first time.

Tailored assemblies mimic natural enzyme activity with significantly higher stability.

In high-entropy oxides, chromium tends to segregate during growth due to oxidation-induced migration of smaller, high-valence cations.

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

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.

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

Recycling polyolefin materials is challenging. One waste management strategy is plastic upcycling. New work demonstrates a single-step upcycling route coupling cracking and alkylation, recycling carbon and keeping valuable resources active.

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