To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.

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.

Irradiated boehmite nanoplatelets form smaller aggregates and have a rougher surface than non-irradiated platelets.

Water affects the intermediates present on the surface of a metal oxide catalyst during ketonization.

Thin oxide films play an important role in electronics and energy storage. Researchers in PNNL’s film growth laboratory create, explore, and improve new thin oxide films.

Combining peptoids and carbon nanotube porins creates a new class of stable membranes with potential applications in separations science.

A combined experimental and modeling study shows that electronic interactions direct how a metal-organic framework grows.

Hydrogen bonding and proton transfer control the identity and reactivity of molecules in highly concentrated and alkaline aluminate solutions.

Within zeolite pores, rates of alcohol dehydration increase with ionic strength until reaching a maximum and then decreasing.

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.

In a water-containing system, the open circuit potential stabilizes positively charged species and lowers the energy barrier of the reaction.

Tungsten heavy alloys show promise for nuclear fusion energy development, according to new research conducted at PNNL.

Researchers identified a new intermediate phase that forms during the crystallization of hydrated aluminum-containing salts.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

Iron-based fertilizer may stimulate plankton to pull carbon dioxide from the ocean, driving a carbon-negative process.

PNNL researchers created rugged, adaptable, mass-manufacturable luminescent particle tracers for use in harsh environments.

Direct visualization of metal atoms during shear deformation has broad applications from battery design to vehicle lightweighting.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

The amount of radiation in a system influences how fast aggregated boehmite particles dissolve.

A new deep learning approach allows researchers to rapidly analyze three-dimensional representations of multi-component catalysts.