PNNL’s SHAD-TAGS+ allows researchers to monitor fish species to help inform energy project development.

Micron and PNNL co-design the Crete computer system, delivering a new memory architecture for data-intensive scientific computing and AI applications.

After isolating copper hydride monomers, researchers identified the mechanistic details of catalyst aggregation and cluster formation.

Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

Scientists have tapped AI and powerful computing to speed up how quickly officials are able to learn important details about nuclear events.

Trace amounts of iron and chromium can incorporate into gibbsite, affecting how the mineral dissolves.

Quantum computing promises leaps in chemistry and materials science, and PNNL is preparing to realize its potential with a collaborative approach.

Experiments focus on catalysts for off-road vehicles that optimize the use of critical minerals while lasting longer and costing less.

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

PNNL STEM Ambassadors engage students about salmon migration, hydropower, cybersecurity, water quality, and more at the annual Salmon Summit.

Engineers at PNNL devised a system that allows radar antennae to maintain stable orientation while mounted on platforms in open water that pitch and roll unpredictably. They were recently invited to participate in DOE's I-Corps program.

Properly identifying iodoplumbate species that are present and stable in a perovskite precursor solution is vital. New research offers insight into reactivity and dynamical processes in solution and the chemical properties of precursors.

Zhiqun (Daniel) Deng, Lab Fellow at PNNL, has been named a fellow of the American Society of Mechanical Engineers, an honor that recognizes outstanding engineering achievements.

A new time-dependent wavefunction formulation for the cumulant Green’s functions can produce more accurate simulations.

PNNL’s science and technology helps hydropower operators detect, prevent and recover from cyberattacks while protecting a source of electricity that enhances grid reliability and resilience.

Scientists are reviewing the current science of the mechanism and structural dynamics of methyl coenzyme-M reductase, an enzyme involved in biological methane conversion.

A team combined experiments and theory to predict the collective properties of electrolyte solutions.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

Under an optical field, nanoparticle attachment in a model system heavily depends on surface roughness.

Directionally biased electrostatic interactions are the key to significantly tuning the reduction potential of iron/sulfur clusters.