A study by researchers at PNNL assessed the feasibility of using strontium isotope ratios and an existing machine learning–based model to predict and verify a product’s source—in this case, honey.
This summer, PNNL hosted the inaugural “As Conductive As Copper” (AC2.0) workshop, fostering a collaborative conversation on the future of the U.S. copper supply chain.
The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.
Atmospheric aerosol particles modulate climate and the Earth’s energy balance by scattering and absorbing sunlight. They also seed clouds, acting as cloud condensation nuclei.
Extensive in situ and remote sensing measurements were collected to address data gaps and better understand the interactions of convective clouds and the surrounding environment.
Ampcera has an exclusive licensing agreement with PNNL to commercially develop and license a new battery material for applications such as vehicles and personal electronics.
Pyrocumulonimbus clouds are increasing in frequency as large wildfires become more prevalent in a warming climate. These clouds can inject smoke particles into the atmosphere, where they can remain suspended for several months.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.
Using numerical simulations to reproduce the laboratory experiments, this study reveals that liquid droplets are present near the bottom surface, which warms and moistens the air in the chamber.
In the latest issue of the Domestic Preparedness Journal, Ashley Bradley and Kristin Omberg share how new research is shedding light on the scientific and technological challenges with detecting fentanyl.
Researchers show how satellite observations from the MODerate Resolution Imaging Spectroradiometer and CloudSat radar can be used to constrain the ACI radiative forcing that is linked to droplet collection in marine liquid clouds.
Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.
PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.
Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.
A new report highlights the results of an assessment PNNL conducted of field-portable detection products used by first responders to detect illicit substances like fentanyl in the field.
