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.

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.

Space Systems Command and PNNL welcome the University of Texas at El Paso to the Cyber Halo Innovation Research Program.

Abigail Gutierrez Deniz is a first-generation, Latina student working to pursue her goal of working in cybersecurity.

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

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.

Using electrocatalysis offers a different path for reactions involving polar groups.

Where molybdenum dopants sit within catalytic material affects catalysis at a mechanistic level.

A new discovery simultaneously reduces the need for rare and expensive platinum and improves its ability to speed up economically important chemical reactions.

PNNL research, featured on the cover of two science journals, describes advancements in using Raman spectrometry for Hanford Site nuclear waste remediation.