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.

PNNL is working with the Port of Seattle and Seattle City Light to assess the risks of long-term hydrogen storage that can bring clean power for decarbonization.

A new system can convert common plastics into valuable fuel molecules.

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

Researchers combined crystallographic data and computational studies to investigate plutonium-ligand bonding within a hybrid material construct.

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

Harnessing the sea for decarbonized energy is the focus of a new collaboration between PNNL and the University of Guam.

A new approach broadens the reach of quantum computers to previously challenging systems.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

The nature of an iron oxide mineral and amount of light control electron movement from a light-absorbing molecule to the mineral, rather than the conditions of the surrounding aqueous environment.

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

PNNL researchers are moving energy research forward through collaborative projects with universities across the United States.

A PNNL team developed and used a model framework to understand the performance and structural reliability of a state-of-the-art solid oxide electrolysis cell design.

One year into an Early Career Research Program award, Marcel Baer is developing new workflows to model biologically inspired systems more efficiently.

A new project will produce a database of optimized conditions for efficient, carbon negative recovery of energy-relevant minerals in red mud waste.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

Tailoring hydrogen-based energy storage solutions to specific real-world applications.

A review article examines a decade of work on the atom-scale process of turning carbon dioxide into stable minerals.

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