Researchers at PNNL advised elementary and middle school student teams with their problem-solving research for the FIRST® LEGO® League robotics competitions.
A new digital twin platform can help hydropower dam operators by providing accurate and predictive models of physical turbines that improve facilities and enhance reliability.
PNNL researchers are exploring the kinds of flicker waveforms that the eye and brain can detect, seeking to understand the different visual and non-visual effects that result.
Although climate change may bring increased precipitation to many parts of the United States, some areas may face drier conditions and lower streamflow, resulting in decreased hydropower generation.
Early life exposure to polycyclic aromatic hydrocarbons (PAHs), found in smoke, has been linked to developmental problems. To study the impacts of these pollutants, PAH metabolism in infants and adults were compared.
The first-of-its kind vessel will allow researchers to transport large equipment and take measurements in near-silence with reduced impact on wildlife.
GUV can reduce transmission of airborne disease while reducing energy use and carbon emissions. But fulfilling that promise depends on having accurate and verifiable performance data.
Researchers used a combination of sophisticated laboratory incubations and field measurements to determine the role of microbial production and consumption of methane in soils with different exposure to tidal inundation
Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.
PNNL scientists have been studying how rivers and streams breathe. Their research focuses on respiration, organic matter, and natural disturbances that affect rivers and streams.
Pacific Northwest National Laboratory launches the Training Outreach and Recruitment for Cybersecurity Hydropower program at the University of Texas at El Paso.
Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.