The first direct molecular-scale evidence of the temperature-driven transformation of the coordination environment of ytterbium at geologically relevant conditions.

This summer, scientists at PNNL led discussions on their latest research related to artificial intelligence and One Health at the Health and Environmental Sciences Institute conference.

Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

Delivering an integrated quantum-mechanical and experimental perspective on the effects of both intrinsic and externally applied electric fields at atomic-scale interfaces.

The size of graphene oxide sheets affects the ability of assembled membranes and adsorbents to separate rare earth elements.

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

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

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

Christina Lomasney moderates panel on commercialization pathways for emerging artificial intelligence and high-performance computing technologies.

Isotopically layered water films and slow heating enable diffusion tracking.

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.

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

CO2 separation is key for natural gas purification, but conventional techniques are high-emission processes. New research reveals a novel, doubly segmented, CO2-selective membrane that increases CO2 permeability and reduces emissions.

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.

The first measurement of the proton diffusion constant at cryogenic temperatures provides insights into the mechanism of proton movement in supercooled water.

PNNL researchers have published their paper, “Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data,” in the Journal of Proteome Research.

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