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

Simulations show an amorphous intermediate that allows impurities to incorporate into crystallizing calcium carbonate.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

Bradley Crowell with the U.S. Nuclear Regulatory Commission sees advanced materials integrity, radiological measurement, and environmental capabilities on his first visit to PNNL.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

A crystalline silicate framework forms through a non-classical, multi-step nucleation mechanism based on structurally precise clusters.

A new review describes the insights that may be gained into important electrochemical processes using ion soft landing.

PNNL researchers demonstrated a simple method to create stable, identical nanoparticles of PdTe2-like composition, which is known to be superconducting, on a WTe2 TMD support.

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

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.

Varying environmental conditions enabled researchers to quantitatively control the dissolution of metal oxide nanorods.

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

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

The complex interactions between the land and atmosphere can affect cloud growth.

PNNL’s ARENA test bed analyzes how electrical cables degrade in extreme environments and how nondestructive examination inspection technologies can detect and locate damage.

Assessing observed weather conditions that support or suppress the growth of clouds into deep precipitating storms during the Cloud, Aerosol, and Complex Terrain Interactions experiment.

Multiple concurrent single-particle measurements help develop a quantitative and predictive understanding of secondary organic aerosols.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.