Increasing concentrations of ions slows the rate of particle aggregation in boehmite suspensions.

New IDREAM research explores the effects of electrolyte species on boehmite nanoparticle aggregation in legacy wastes.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

Hydrogen atoms released and trapped in irradiated gibbsite are destabilized by synthetic-precursor residuals.

When exposed to a sufficiently high dose of ionizing radiation, gibbsite and boehmite are less susceptible to dehydration-induced breakdown.

IDREAM scientists capture electronic response to ionization.

IDREAM research shows the size of cations in highly concentrated nitrite solutions affects the production of radicals.

Researchers investigated how stable nanoparticle suspensions form using facet engineering on hematite nanoparticles, demonstrating that controlling the faceting of nanoparticles can effectively maintain particle dispersity.

Differences in water interactions lead to two gibbsite dissolution pathways modulated by solution acidity.

Irradiation in a sodium hydroxide solution increases the dissolution rate and particle roughness of gibbsite compared to dry irradiation.

Resolving how nanoparticles come together is important for industry and environmental remediation. New work predicts nanoparticle aggregation behavior across a wide range of scales for the first time.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

A poem inspired by radioactive tank waste—“Can a Scientist Dream it Alone?”—was awarded first place in the Department of Energy’s Poetry of Science Art Contest.

The first study modeling the mechanism of hydrated electron formation from aqueous iodide.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

New research reveals enhanced CO2 transport across a polyether ether ketone membrane and water-lean solvent interface

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

IDREAM research shows that keeping only the most important two- and three-body terms in reactive force fields can decrease computational cost by one order of magnitude, while preserving satisfactory accuracy.

PNNL and University of Texas at El Paso leverage partnership and joint appointment program for a laboratory directed research and development project.

Computational chemistry libraries from NWChem, NWChemEX, SPEC, and more are now available on Azure Quantum Elements.