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.

Tom Autrey presented at a forum to foster collaboration between Korea and the United States to develop hydrogen-related technologies.

New PNNL research makes it easier to differentiate between chemical and nuclear explosions.

PNNL’s contributions over the last year span scientific discovery, sustainable energy and national security.

Researchers at PNNL leveraged their experiences to connect with attendees at the 2022 SACNAS National Diversity in STEM Conference, October 27–29, 2022, in San Juan, Puerto Rico.

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

PNNL scientists Daniel Gaspar and Wei Wang were among nine scientists featured in journal article recognizing 2021-2022 ACS award honorees.

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.

The American Society of Mechanical Engineers recognized radioactive material transportation expert Matt Feldman with its 2022 Dedicated Service Award.

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

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

Physicist named specialty chief editor of Battery Systems and Applications for Frontiers in Batteries and Electrochemistry and associate editor for Frontiers in Energy Research—Nano Energy.

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.

Ancient seawater pockets offer a new source of clues to climate change in vanished oceans and our own.

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

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