PNNL chemical engineer Mariefel Olarte shares passion for mentoring students and the next-generation workforce.

Tuning the environment of supported palladium atoms with hydrogen treatment enhances their catalytic activity.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

PNNL researchers will pursue both fundamental and applied projects with this support from the Department of Energy

A switchable single-atom catalyst is activated in the presence of surface intermediates and reverts to its stable inactive form when the reaction is completed.

Increasing the concentration of ions in zeolite micropores leads to a significant increase in the rate of acid catalyzed reactions.

Electronic modifications from hydrogen spillover onto the oxide support increase the activity of single-atom rhodium catalysts.

ChemReasoner combines decades of chemistry knowledge with large language models to propose strategies for effective new catalysts.

Catalysts that efficiently transfer hydrogen for storage in organic hydrogen carriers are key for more sustainable generation and use of hydrogen. New research identifies activity descriptors that can accelerate novel catalyst development.

New apps help researchers with statistical analyses of data.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

1-propanol converts from a monomer into a trimer at aluminum sites within zeolite pores.

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.

Studying single crystal reactions provides molecular-level insight into catalytic processes.

Highly precise and controllable single-atom catalysts are affected by reaction conditions, which can alter the bonding around the atoms and the activity.

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

Newly developed models can help rapidly optimize systems to meet the need for rapidly deployable commercial carbon capture.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.