News & Media
Secretary of Energy Advisory Board (SEAB) Report Recognizes PNNL Contributions
Report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions
Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report from a working group dedicated to the U.S. Department of Energy’s (DOE’s) capabilities and future in artificial intelligence (AI) and machine learning. PNNL researchers’ expertise is prominent throughout DOE’s AI efforts, particularly in the areas of data sciences and national security.
Based largely on input from DOE sponsors, the report features how PNNL’s computing capabilities are affecting the nation’s security, science, and energy missions. Key highlights include:
- Studying how AI affects the global landscape for securing nuclear materials, potentially using deep learning to enhance physical and digital protections against material concealment, delivery, theft, and sabotage.
- Describing how the United States and its partners might employ deep learning to combat attack efforts for enhanced nuclear security.
- Designing advanced deep learning models to characterize operations with buildings, using electrical signatures on power lines, enabling new designs for energy-efficient buildings in addition to enhanced security features for nuclear facilities.
- Leading the nuclear explosive monitoring project with data scientists working to significantly lower detection thresholds of low-yield, evasive underground nuclear explosions without increasing time-to-detection or the amount of human analysis.
- Co-design of advanced accelerator, memory and data movement concepts to support convergence of AI and machine learning methods with other forms of data analytics and traditional scientific high performance computing (HPC).
The report highlights PNNL’s support to the National Nuclear Security Administration, featuring joint laboratory collaborations between PNNL and others, including the Y-12 National Security Complex, Sandia National Laboratories, Lawrence Livermore National Laboratory, Los Alamos National Laboratory, and Oak Ridge National Laboratory. Additionally, PNNL is working as part of DOE’s comparative advantages in AI, providing the Office of Energy Efficiency and Renewable Energy access to AI subject matter experts.
Study Shows Coastal Wetlands Aid in Carbon Sequestration
Sea-level rise impacts will likely decrease ecosystem carbon stocks
Tidal marshes, seagrass beds, and tidal forests are exceptional at absorbing and storing carbon. They are referred to as total ecosystem carbon stocks, yet little data exists quantifying how much carbon is absorbed and stored by tidal wetlands in the Pacific Northwest (PNW). Knowing this information is valuable, particularly in the context of sea level rise and with the associated need for Earth system modeling to predict changes at the coast.
Researchers found that the average total ecosystem carbon stock in the PNW is higher than in other areas of the U.S. and other parts of the world. Marsh carbon stocks, in particular, are twice the global average. Researchers found progressive increases in total ecosystem carbon stocks along the elevation gradient of coastal wetland types common in the PNW: seagrass, low marshes, high marshes, and tidal forests. Total carbon also increased along the salinity gradient, with more carbon occurring in lower salinity areas.
Additionally, this research showed that common methods used to estimate soil carbon actually underestimate soil carbon stocks in coastal wetlands. Soil carbon storage below the depth of 100 centimeters proved to be an important carbon pool in PNW tidal wetlands.
The results suggest that long-term sea-level rise impacts, such as tidal inundation and increased soil salinity, will likely decrease ecosystem carbon stocks. This is a concern if wetlands can’t migrate with increased sea level due to being bound by topography and human development.
This research arose from the Pacific Northwest Blue Carbon Working Group, of which Amy Borde and Heida Diefenderfer of Pacific Northwest National Laboratory’s Coastal Sciences Division are members. The team studied 28 tidal ecosystems across the PNW coast, from Humboldt Bay, California, to Padilla Bay, Washington. They sampled common coastal wetland types that occur along broad gradients of elevation, salinity, and tidal influences, collecting the data necessary to calculate total carbon stocks in both above ground biomass and the soil profile.
In three years of study, the researchers found that most carbon is in the wetland soils not aboveground, and much of it is deeper than one meter—a typical lower limit of sampling. Total ecosystem carbon stocks progressively increased along the terrestrial-aquatic gradient of coastal wetland ecosystems common in the temperate zone including seagrass, low marshes, high marshes, and tidal forests. The findings were reported in “Total Ecosystem Carbon Stocks at the Marine-Terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, USA,” published in the August 2020 online edition of Global Change Biology (DOI: 10.1111/gcb.15248).
Research Team: PNNL’s Amy Borde and Heida Diefenderfer, along with J. Boone Kauffman, Leila Giovanonni, James Kelly, Nicholas Dunstan, and Christopher Janousek (Oregon State University); Craig Cornu and Laura Brophy (Institute for Applied Ecology/Estuary Technical Group); and Jude Apple (Padilla Bay National Estuarine Research Reserve).
The grant award was administered by the Institute of Applied Ecology, and other partners included Oregon State University and the Padilla Bay National Estuarine Research Reserve. This research was supported by the National Oceanic and Atmospheric Administration, through a cooperative agreement with the University of Michigan.
Kauffman, J Boone, Leila Giovanonni, James Kelly, Nicholas Dunstan, Amy Borde, Heida Diefenderfer, Craig Cornu, Christopher Janousek, Jude Apple, and Laura Brophy. “Total Ecosystem Carbon Stocks at the Marine‐terrestrial Interface: Blue Carbon of the Pacific Northwest Coast, United States.” Global change biology, no. 0 (August 11, 2020). DOI: 10.1111/GCB.15248
NWRTC Notes From the Field (June 2020)
Interviews with public health professionals who are helping to keep us safe
PNNL's Northwest Regional Technology Center interviews Assistant Chief of Resource Management for Seattle Fire Department Willie Barrington about how his team faced the unknown when the COVID-19 pandemic hit Seattle, Washington.
Digging into the Details of Phosphorus Availability
New root blotting technique visualizes relationship between root growth, microbial activity, and soil nutrients.
Phosphorous is an important nutrient for plants. However, the mechanisms used by plants to extract phosphorus from soil and incorporate it into their biomass are not well understood. Now, researchers developed a new technique to visualize the activity and distribution of enzymes that mobilize phosphate around plant roots. Tracking the location of these enzymes can help researchers better understand the chemical dynamics between roots, microbes, and soil that influence how plants get nutrients. The approach could also be applied to other nutrient-cycling enzymes.
Phosphorus is an essential nutrient for plants and therefore, global demand for phosphorus fertilizers is expected to grow to accommodate the world’s growing population. However, most of these fertilizers are made from rock phosphorus, a non-renewable resource. This research provides new insights into the complex dynamics of phosphorous exchange between soil, microbes, and plant roots. Knowledge from this newly developed approach will help scientists identify strategies to improve phosphorus use efficiency for bioenergy crop production in marginal environments, as well as for agriculture in general.
Soil bacteria, fungi, and plants produce enzymes called phosphatases, which convert organic sources of phosphorus into a form that plants can absorb. Researchers have studied the microbial activity in bulk soil samples, providing information about the overall functional potential of the environment. But to better understand the dynamics between soil, plants, and microbes, more detail is needed. To accomplish that, a team of researchers developed a new technique based on root blotting to reveal phosphatase activity and distribution around plant roots. They grew switchgrass in flat pots or “rhizoboxes” containing soil with pellets of root matter as sources of organic phosphorus. Then, they applied a nitrocellulose membrane to capture proteins around the roots. Finally, the researchers stained the membrane with fluorescent indicators for phosphatase activity and protein concentration. This revealed the spatial distribution of phosphatase around the roots of plants, and highlighted regions of increased phosphatase activity.
This approach could be used to study phosphatase activity over time, as well as other nutrient-cycling enzymes. The combination of membrane extraction, with rapid analysis via fluorescent probes to reveal localization of phosphatase activity in the rhizosphere, offers a new technique for environmental applications. Expanding this approach could enable simultaneous visualization of multiple enzyme types in soil systems.
Development of this method was funded by DOE’s Office of Science, Biological and Environmental Research Program by the Early Career Research Award program (PI: Jim Moran).
V.S. Lin, et al. “Non-destructive spatial analysis of phosphatase activity and total protein distribution in the rhizosphere using a root blotting method.” Soil Biology and Biochemistry, 146 (2020). DOI: 10.1016/j.soilbio.2020.107820
Creating better models to predict subsurface water flow and transport
New framework improves the predictions of subsurface sediment permeability
Co-authors of a paper in Water Resources Research led by PNNL researchers developed a new iterative data assimilation framework to more accurately describe the permeability of subsurface sediments in numerical models when using facies, a system that classifies dissimilar sediments into distinct geological units that share important features of interest to modelers. The iterative framework applies data from field observations and experiments to inform the delineation of facies at the start of each model run. Further refinements are achieved at each iteration through the application of statistical constraints that maintain geologic continuity among adjacent locations.
Spatial distribution of three facies (red, yellow and blue colors) in a 2D vertical cross section of a 3D case. Figures show the new method provides a more accurate and continuous estimation of facies distribution compared to the conventional method. White colors in the figures are bore samples and black dots are the conditioning points selected by the new method.
More realistic numerical representations of the permeability of subsurface sediments lead to improved predictions of groundwater flow and the concentration of constituents that are transported with the flow. The data assimilation framework can also be applied to estimate other subsurface properties from field measurements, or from data from other systems such as watersheds, as long as they can be categorized into a few discrete representative units.
Observational data on subsurface permeability is limited for most watersheds because of the impracticality of digging enough boreholes or wells to capture the heterogeneous nature of the subsurface environment. To solve for this limitation, researchers have widely adopted approaches that estimate permeability from field experiments such as a) measuring how water levels at a cluster of wells change when water is pumped at a nearby well, or b) monitoring how quickly a tracer released at one well reaches other wells in the aquifer. The U.S. Department of Energy’s Hanford 300 Area Integrated Field Research Challenge site, for example, is well characterized from data assimilation methods that were used to understand the long-term persistence of nuclear fuel fabrication wastes disposal from 1943 to 1975.
The use of a facies approach to segment the subsurface reduces complexity in numerical models by grouping heterogeneous sediments into distinct homogenous units defined by hydraulic, physical and or chemical properties. A major difficulty with existing facies-based approaches in numerical models is that each facies is treated as its own, independent unit. Therefore, these models fail to capture the spatial continuity of subsurface sediments. The authors of this paper developed a framework that maintains continuity between neighboring facies in numerical models and thus better reflects true subsurface geology, and thereby groundwater movement. The improvements come from an iterative data assimilation approach that incorporates direct and indirect data about subsurface permeability gathered from field observations and experiments at the start of each model run as well as the application of statistical constraints about subsurface geology. The data assimilation and statistical constraint steps are re-imposed for each iteration, leading to refined facies delineation. This framework reduces uncertainty about the spatial distribution of sediment types in the subsurface, which results in more accurate predictions of groundwater flow and constituent transport.
The authors evaluated the performance of the new framework on a two-dimensional, two-facies model and a three-dimensional, three-facies model of DOE’s well-characterized Hanford 300 Area that were conceptualized from borehole and field tracer experiments. The results of the research shows that the framework can identify facies spatial patterns and reproduce tracer breakthrough curves with much improved accuracy over facies-based approaches that lack spatial continuity constraints. With additional data, the authors say that the framework can also be used to categorize biogeochemical reactive units in an aquifer.
Xingyuan Chen, Earth Scientist, Xingyuan.Chen@pnnl.gov
Funding for this research came from DOE Office of Science BER, PNNL Subsurface Biogeochemical Research SFA.
Song, X., Chen, X., Ye, M., Dai, Z., Hammond, G., And Zachara, J.M. (2019). Delineating facies spatial distribution by integrating ensemble data assimilation and Indicator Geostatistics with level-set transformation. Water Resources Research, 55. https://doi.org/10.1029/2018WR023262
Improving nuclear waste storage models by studying the chemistry of material interactions
WastePD EFRC research on the glass-steel interface was published in Nature Materials
New research unravels the chemistry of how materials in the waste packages used for the disposal of high-level radioactive waste interact in deep geologic repository environments. Having a better understanding of the interactions between materials under various conditions provides more information to make waste storage performance models more robust.
“Many performance models use conservative approaches such as assuming that the steel canister walls don’t even exist or that they dissolve very fast. This study provides the opportunity to better incorporate the canister barrier in the models,” said Joseph Ryan, a PNNL materials scientist and coauthor on the paper, “Self-accelerated corrosion of nuclear waste forms at material interfaces,” published in Nature Materials.
The United States is converting highly radioactive nuclear waste, also known as high-level waste, into glass. The molten glass is poured into steel canisters for long-term storage and ultimate disposal in a geologic repository. The goal is to design waste storage and disposal systems that would remain safe for hundreds of thousands of years to come, even if they are exposed to water. Because of the extensive time span of waste storage, researchers turn to cutting-edge science to project what will happen during that time period. The data is used to inform extensive safety analyses—helping make sure the system is engineered to be compatible with the natural system so that waste remains separate from the environment.
“We can’t just do a test on a material and say, ‘That material corroded this much in 30 days and extrapolate that to a million years.’ It doesn’t work that way,” Ryan said. “At the most basic level, we try to understand the underlying chemistry of corrosion. Then, we feed that information into computer models to calculate the expected release over time.”
In this study, led by the WastePD Energy Frontier Research Center based at Ohio State University, researchers unpacked the chemistry that occurs when two materials are close together, focusing on glass-steel along with ceramic-steel interactions. This chemical situation could occur when water has percolated into the repository and has breached the steel canister, exposing the glass-steel interface to water.
When water finally breaches the waste package container, it will fill the microscopic space that forms between the solid glass and the steel canister. Chemical reactions that happen in localized and tiny microenvironments such as these can be quite different than those happening in a more open setting. In this case, this localized area can have a different chemistry than the surrounding solution, causing more corrosion than would be expected.
The researchers tested their theory in the laboratory. They pressed glass and steel together in salty liquid and kept it at 90° C (194° F) for a month. At the end of the experiment, they found differences in the width of thin layers that indicated higher corrosion between the glass-steel couple interface than in a control sample.
Why it matters: This research allows scientists to improve models that project how a disposal canister could perform in a deep geologic environment. Having a better understanding of the interactions between materials under various conditions provides more information to make the models more robust. Currently, some models project what happens to waste under the assumption that the steel canister walls do not exist. Operating under this pretext can result in higher projections of waste degradation than would likely occur when taking a conservative approach. But better understanding the chemistry of how the solid waste and the steel canister interact allows a scientifically based understanding of how the canisters behave and interact with the glass to be included in performance assessment models.
Summary: High-level waste is immobilized as glass in stainless steel canisters. On cooling, a confined crevice space forms at the stainless steel-glass interface. If the disposal canister is breached and if water can enter the steel-glass interface, it could result in anodic dissolution of the stainless steel, generating metal cations, which hydrolyze to form protons and strongly increase the local acidity. This acidic environment may locally enhance the corrosion of both the stainless steel and the glass, which leads to the release of cations from the glass. Further, the coupled corrosion may trigger the precipitation of additional secondary phases that may impact subsequent canister corrosion or nuclear glass durability.
What’s Next: While this study sheds light on the chemical interactions that occur at the stainless steel-glass interface, there are more interactions to explore. Ultimately, a better understanding of different chemical mechanisms will improve the overall performance model.
Sponsors: This work was supported as part of the Center for Performance and Design of Nuclear Waste Forms and Containers, an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Basic Energy Sciences under Award no. DESC0016584.
Research Team: Xiaolei Guo, Gerald S. Frankel, Gopal Viswanathan, Tianshu Li (Ohio State University); Stéphane Gin (CEA, France); Penghui Lei, Tiankai Yao, Jie Lian (Rensselaer Polytechnic Institute); Hongshen Liu, Dien Ngo, Seong H. Kim (Pennsylvania State University); Daniel K. Schreiber, John D. Vienna, Joseph V. Ryan (PNNL); Jincheng Du (University of North Texas)