At PNNL, subsurface science inhabits two separate but interlocking worlds. One looks at basic science, the other at applied science and engineering. Both are funded by the U.S. Department of Energy (DOE).
Existing techniques to detect pertechnetate in the environment have drawbacks. PNNL’s redox sensor technology uses a gold probe to accurately and efficiently measure low levels of pertechnetate—and possibly other contaminants—in groundwater
A recent paper published in Water Resources Research found that the spatial variability of subsurface sediments, and seasonal fluctuations in a river’s water level, influences the behavior of a uranium contaminant plume, particularly in ...
DOE researchers investigated the role of microbial genetic diversity in two major subsurface biogeochemical processes: nitrification and denitrification.
The Federal Laboratory Consortium for Technology Transfer has honored three innovations at the U.S. Department of Energy’s Pacific Northwest National Laboratory.
PNNL coastal ecologist Heida Diefenderfer was a featured speaker in February at the National Academies of Sciences, Engineering, and Medicine’s Government-University-Industry Research Roundtable on policy and global affairs.
PNNL’s Juliet Homer was an invited panelist at a California Energy Commission workshop, which highlighted research on water treatment, delivery, and energy.
Researchers have identified two processes responsible for fracturing rock at lower pressures for geothermal energy production using PNNL’s fracturing fluid, StimuFrac™.
Biogeochemical activity in the hyporheic zone (HZ), sediments where the flowing waters of a river mix with shallow groundwater, supports many of the biological processes that occur within a watershed.
Co-authors of a paper in Hydrological Processes led by PNNL researchers Zhangshuan Hou, Timothy Scheibe, and Christopher Murray, produced a map that identifies different classes of sediments which compose the riverbed along the Hanford ...
A multi-institutional team of scientists developed a new sensitivity analysis framework using Bayesian Networks to quantify which parameters and processes in complex multi-physics models are least understood.