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

A study by researchers at PNNL assessed the feasibility of using strontium isotope ratios and an existing machine learning–based model to predict and verify a product’s source—in this case, honey.

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.

Christina Lomasney moderates panel on commercialization pathways for emerging artificial intelligence and high-performance computing technologies.

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.

The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.

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

PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

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.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

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

In the latest issue of the Domestic Preparedness Journal, Ashley Bradley and Kristin Omberg share how new research is shedding light on the scientific and technological challenges with detecting fentanyl.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.

A new report highlights the results of an assessment PNNL conducted of field-portable detection products used by first responders to detect illicit substances like fentanyl in the field.