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.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

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

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

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

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.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

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

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.

Researchers isolated a molecular copper hydride monomer for the first time.

A new simple and scalable synthesis produces nanoparticle assemblies that can perform catalytic hydrogen sensing at room temperature for the first time.

A study that combined experiments and theory showed how model enzymes control challenging reactions involving highly reactive carbocations.

By decreasing the amount of precious metal in a catalyst, the system increased its efficiency and selectivity for breaking apart plastics.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

A comprehensive understanding of the electronic structure of uranyl ions provides insight into the chemistry of nuclear waste and uranium separation technologies.

PNNL researchers worked to decouple the effects that solvents impose on reactions catalyzed in the liquid phase.