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

A research buoy managed by PNNL has been deployed in Hawai’ian waters, collecting oceanographic and meteorological measurements off the coast of O’ahu.

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.

The Joint Appointment program at PNNL is one of the most diverse among other U.S. national laboratories, involving nearly 60 universities and research institutions in the United States and abroad.

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

A multi-institutional team of wind energy experts led by PNNL assessed the scientific grand challenges for offshore wind and provided recommendations for closing gaps in models.

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.

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.

Multidisciplinary teams from research and industry are brought together using management systems developed by a team of software engineers at PNNL.

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.

PNNL gathered researchers from eight national laboratories plus the U.S. Department of Energy (DOE) to share ideas and build synergy at the Energy Equity and Environmental Justice Summit.

An evaluation of models and prediction tools for distributed wind turbines has unearthed data that can help potential users make the most informed decisions on upfront investments.

PNNL researchers developed the dummy payload to evaluate the performance of marine energy device prototypes in the Powering the Blue Economy: Ocean Observing Prize Competition.

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