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.

PNNL recently joined the Department of Homeland Security for two technical meetings exploring national security research spanning the threat realm, from chemical and biological attacks to adversarial artificial intelligence.

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

PNNL’s wide-ranging report maps the current nanobiotechnology landscape, flags potential concerns, and details the need for an organizing body to coordinate currently disparate disciplines.

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

The ChemSpace Tool, when fully developed, is intended to divide chemical space into three subsets: the detectable space, the identifiable space, and the region that includes compounds that are not detectable or identifiable.

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.

Performing closure studies using aerosol size, aerosol composition, and cloud condensation nuclei measurements of mixed aerosol from the Southern Great Plains region.

How a model treats gas transfer behavior onto dust and sea salt particles significantly influences nitrate aerosols and their effects on climate.

Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.

New PNNL research makes it easier to differentiate between chemical and nuclear explosions.

PNNL researchers created rugged, adaptable, mass-manufacturable luminescent particle tracers for use in harsh environments.

When wildfires burn in the west, the heat and airborne particles they release inflict stronger rain and larger hail upon central states.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

Scientists can detect the virus that causes COVID-19 by using a bubble that spills its contents like a piñata when encountering the virus.

A new study shows for the first time that wildfires burning in West Coast states can, despite great distance, strengthen storms in downwind states.

Secondary organic aerosol formation from monoterpenes is more strongly influenced by oxidant and monoterpene structure than by nitric oxides and hydroperoxy radical concentrations.