Predicting how organisms’ characteristics respond to not only their genes, but also their environments (a nascent field called predictive phenomics), is extraordinarily challenging. Researchers at PNNL are using AI to tackle that challenge.
PNNL researchers have found yet another way to turn trash into treasure: using algal biochar, a waste production from hydrothermal liquefaction, as a supplementary material for cement.
Researchers at PNNL advised elementary and middle school student teams with their problem-solving research for the FIRST® LEGO® League robotics competitions.
Led by interns from multiple DOE programs, a newly expanded dataset allows researchers to use easy-to-obtain measurements to determine the elemental composition of a promising carbon storage mineral.
A multi-institutional team of researchers systematically compared extraction techniques for characterizing plant litter composition that relies on organic matter extraction.
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.
Research identifies the mechanisms through which peptoids affect ions in solution and a mineral surface, increasing the rate of carbonate crystal growth.
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.
