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At least 253 records · Page 14

Nitrate Controls on the Extent and Type of Metal Retention in Fine-Grained Sediments of a Simulated Aquifer

Aquifer groundwater quality is largely controlled by sediment composition and physical heterogeneity, which commonly sustain a unique redox gradient pattern. Attenuation of heavy metals within these heterogeneous aquifers is reliant on multiple factors, including redox conditions, and redox-active species that can further influence biogeochemical cycling. Here, we simulated an alluvial aquifer system using columns filled with natural coarse-grained sediments and two domains of fine-grained sediment lenses. Our goal was to examine heavy metal (Ni and Zn) attenuation within a complex aquifer network and further explore nitrate-rich groundwater conditions. The fine-grained sediment lenses sustained reducing conditions and served as a sink for Ni sequestration – in the form of Ni-silicates, Ni-organic matter, and a dominant Ni-sulfide phase. Further, the silicate clay and sulfide pools were also important retention mechanisms for Zn; however, Ni was associated more extensively with organic matter compared to Zn that formed layered double hydroxides. Nitrate-rich conditions promoted denitrification within the lenses that was coupled to the oxidation of Fe(II) and the concomitant precipitation of an Fe(III) phase with higher structural distortion. A decreased metal sulfide pool also resulted, where nitrate-rich conditions generated in an average 20% decrease in solid phase Ni, Zn, and Fe. Ultimately, nitrate plays a significant role in the aquifer’s biogeochemical cycling and capacity to retain heavy metals.

54 ENVIRONMENTAL SCIENCES↗

Modeling Volatility-Based Aerosol Phase State Predictions in the Amazon Rainforest

Organic aerosol (OA) is a complex matrix of various constituents—fresh (primary organic aerosols—POA) and aged via oxidation (secondary organic aerosols—SOA), generated from biogenic, anthropogenic, and biomass burning sources. The viscosity of OA can be critical in influencing new particle formation, reactive uptake processes that impact evaporation-growth kinetics, and the lifetime of particles in the atmosphere. We report this work utilizes a well-defined relationship between volatility and viscosity for pure compounds, which we incorporated within the Weather Research and Forecasting Model coupled to chemistry (WRF-Chem) to simulate the phase state and viscosity of bulk OA during the dry-to-wet transition season (September–October) in the Amazon rainforest during 2014. Our simulations indicate spatial and temporal heterogeneity in aerosol phase state often not captured by global-scale models. We show the strong role of water associated with organic aerosol ($w_s$) as the dominant factor that can be used to quantitatively estimate OA viscosity. Analysis of WRF-Chem simulations across the entire atmospheric column indicates a strong inverse log-linear relationship between $w_s$ and OA viscosity with a correlation coefficient approaching 1, in the background and biomass burning-influenced conditions. At high altitudes where relative humidity (RH) and temperatures are low, our simulations indicate that OA exists in a semisolid-/solid-like phase state, consistent with previous studies. OA hygroscopicity is strongly correlated (ca. –0.8) with OA viscosity at RH ca. 30–50%, but this RH range is found mostly at low OA concentrations and the middle troposphere (ca. 6–10 km altitudes) in our simulated domain. OA hygroscopicity is uncorrelated with viscosity at higher-RH (near surface) and lower-RH (upper troposphere) regimes. At the urban site near surface, where day–night differences in RH are significant, RH is found to drive the phase state. At the background forested site near surface, where day–night RH differences are small, biomass burning-influenced OA is semisolid and a significant OA associated with background conditions is liquid-like. Simulations indicate a long tail of OA viscosity frequency distributions extending in the semisolid/solid regimes over background biogenic-influenced conditions due to the role of low-volatility OA components such as monoterpene oxidation products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project

Abstract. The radiative–convective equilibrium (RCE) model intercomparison project (RCEMIP) leveraged the simplicity of RCE to focus attention on moist convective processes and their interactions with radiation and circulation across a wide range of model types including cloud-resolving models (CRMs), general circulation models (GCMs), single-column models, global cloud-resolving models, and large-eddy simulations. While several robust results emerged across the spectrum of models that participated in the first phase of RCEMIP (RCEMIP-I), two points that stand out are (1) the strikingly large diversity in simulated climate states and (2) the strong imprint of convective self-aggregation on the climate state. However, the lack of consensus in the structure of self-aggregation and its response to warming is a barrier to understanding. Gaining a deeper understanding of convective aggregation and tropical climate will require reducing the degrees of freedom with which convection can vary. Therefore, we propose phase II of RCEMIP (RCEMIP-II) that utilizes a prescribed sinusoidal sea surface temperature (SST) pattern to provide a constraint on the structure of convection and move one critical step up the model hierarchy. This so-called “mock-Walker” configuration generates features that resemble observed tropical circulations. The specification of the mock-Walker protocol for RCEMIP-II is described, along with example results from one CRM and one GCM. RCEMIP-II will consist of five required simulations: three simulations with the same three mean SSTs as in RCEMIP-I but with an SST gradient and two additional simulations at one of the mean SSTs with different values of the SST gradients. We also test the sensitivity to the imposed SST gradient and the domain size. Under weak SST gradients, unforced self-aggregation emerges across the entire domain, similar to what was found in RCEMIP. As the SST gradient increases, the convective region narrows and is more confined to the warmest SSTs. At warmer mean SSTs and stronger SST gradients, low-frequency variability in the convective aggregation emerges, suggesting that simulations of at least 200 d may be needed to achieve robust equilibrium statistics in this configuration. Simulations with different domain sizes generally have similar mean statistics and convective structures, depending on the value of the SST gradient. The prescribed SST boundary condition is the only difference in the set-up between RCEMIP-II and RCEMIP-I, which enables comparison between the two; however, we also welcome participation in RCEMIP-II from models that did not participate in RCEMIP-I.

Geology↗

Data and Scripts associated with “Lambda-PFLOTRAN: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling.”

This data package is associated with the publication “Lambda-PFLOTRAN: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling” submitted to Geoscientific Model Development (Muller et al., 2024). In this manuscript, organic matter chemistry and thermodynamics are directly connected to reactive transport simulators through the newly developed Lambda-PFLOTRAN (Parallel Reactive Flow and Transport model) workflow tool that succinctly incorporates organic matter chemistry data generated from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) into reaction networks to simulate aerobic respiration of the organic matter and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow, executed through a Jupyter Notebook interface, that digests raw FTICR-MS data, develops a representative reaction network based on substrate-explicit thermodynamic modeling (also termed lambda modeling due to its key thermodynamic parameter λ used therein), and completes a biogeochemical simulation with the open source, reactive flow, and transport code PFLOTRAN. This data package contains Jupyter Notebook based workflows for two test cases for running biogeochemical simulations of organic matter oxidation identified by FTICR-MS. It contains four primary folders (workflow, data, src, and analysis), a file-level metadata file (Muller_2024_Lambda_PFLOTRAN_Manuscript_Data_Package_flmd.csv) that lists all the files contained in this data package with a short description of each, and a data dictionary (Muller_2024_Lambda_PFLOTRAN_Manuscript_Data_Package_dd.csv) file that describes the tabular column headers. The ‘workflow’ folder contains the Jupyter Notebook based workflows for running the lambda analysis, PFLOTRAN simulation, sensitivity analysis and parameter estimation. The ‘data’ folder contains the FTICR-MS data, initial conditions, and incubation data for test cases 1 and 2 in folders titled ‘WHONDRS’ and ‘Colloids’, respectively. The data folder also has a ‘Database’ folder containing a reaction network for bulk organic matter (assumed to be CH2O) and a general database for PFLOTRAN (hanford_rxn_network). The CH2O reaction network defines bulk organic matter oxidation. Biogeochemical simulations are completed for both the lambda binned organic matter and bulk organic matter reaction networks. The ‘hanford_rxn_network’ database includes information required for PFLTORAN simulations including ion size, molar mass, and charge of the aqueous species, gases, and minerals phases. The ‘src’ folder contains python source codes for performing lambda analysis, PFLOTRAN simulation, sensitivity analysis and parameter estimation. The ‘analysis’ folder contains outputs from the test cases 1 and 2 including lambda analysis, PFLOTRAN runs and the calibration results.

54 ENVIRONMENTAL SCIENCES↗

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - Simulated Marine Hydrokinetic Tidal Turbine

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset is part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with other energy technologies. This dataset contains inputs and outputs from simulations of a floating marine hydrokinetic turbine over approximately half a tidal cycle (~6.6 hours). Inflow conditions were derived from field measurements in Alaska’s Cook Inlet and represent a tidal environment in which the current speed ramps from near 0 m/s to a peak of 3 m/s and back. The original acoustic doppler current profiler dataset is publicly available on the Marine and Hydrokinetic Data Repository. In a full tidal cycle, the flow reverses and the rotor would reorient; this reversal was not modeled. In the Cook Inlet campaign , turbulence intensity was similar in both directions. Two inflow cases are included. In the first case, labeled “raw” in the files, the measured current time series was used directly in the InflowWind module of OpenFAST. Speed and direction were applied as a function of time and elevation, uniformly in the horizontal direction. With full spatial coherence, this approach captures high turbulent variability and results in pronounced power fluctuations, so it is considered a conservative, near-worst-case representation of loading. In the second case, labeled “average” in the files, a 30-minute moving average was applied to extract the slowly varying mean speed. The residual fluctuations about this mean were used to generate spatially varying, full-field turbulence inputs with TurbSim, giving a more physically realistic representation of the inflow across the rotor disk. Two random realizations were used to produce distinct inflow conditions for two OpenFAST simulations representing a two-turbine array. The same turbulence intensity is applied across the full time series, producing larger fluctuations at the start and end, where the mean speed is low. The second case is the more appropriate framework for performance and power assessment but overpredicts turbulence at lower flow speeds and underpredicts it at higher speeds. As the floating platform moves and the rotor changes its x-position, Taylor’s frozen turbulence hypothesis used by InflowWind assumes a constant rather than a time-varying mean velocity, introducing some inaccuracy in the velocity plane sampling. The turbine modeled is the 500-kW Reference Model 1, a horizontal-axis two-bladed hydrokinetic turbine on a four-column floating semisubmersible substructure . Simulations were performed using OpenFAST v4.1 with the Reference Open Source Controller (ROSCO) v2.10. All input files required to reproduce the simulations are included. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel . This unit supports up to 2.5 MW, but NLR has only a single 1.25-MW stack. The datasets report hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. The system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operating current of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The simulated tidal turbine time series data was translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz. Each zip file represents a single tidal electrolysis experiment and is named: {technology}_{inflow method}_{number of 500 kW tidal turbines connected} For instance, “tidal-500kW-RM1_average_2.zip” is a 6-hour experiment using the 500-kW tidal reference model, scaled by 2x (1-MW) to better match the electrolyzer maximum of 1.25MW, fed with the 30-minute moving average current case. Each zip folder contains the following files: A .csv file of raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wave power. A .csv file combines all tidal profiles as "combined_tidal_experiments.csv." A separate experiment, “characterization_200.zip,” shows the MC250 electrolyzer steady-state response with 30-minute load steps over 5 hours and is accessible with this entry.

08 HYDROGEN↗

Small to Full Height Scale Comparisons of Cesium Ion Exchange Performance with Crystalline Silicotitanate

The U.S. Department of Energy’s (DOE) Hanford Site houses 56 million gallons of high-level radioactive waste generated from plutonium production from 1944 to 1988. The supernatant waste, currently stored in underground tanks, is intended to be vitrified following filtration and 137 Cs removal at the Hanford Waste Treatment and Immobilization Plant (WTP) Low-Activity Waste (LAW) Vitrification Facility. The WTP Pretreatment Facility will not be operational for several years. The Tank Side Cesium Removal (TSCR) system is a technology demonstration that will remove cesium from tank waste supernate to support directly feeding LAW to the vitrification facility. The 137 Cs removal is important to meet the WTP LAW contract specification and ultimately for creating a contact-handled waste form. The waste acceptance criteria (WAC) limit for the WTP LAW Facility is <3.18E-5 Ci 137 Cs per mole Na. The TSCR system is skid mounted and employs two key technologies: dead end filtration for solids removal and ion exchange (IX) for cesium removal. Filtration is necessary to protect the functionality of the IX columns. The IX process utilizes sodium form crystalline silicotitanate (CST) IX media produced by UOP Honeywell, LLC (Des Plaines, IL) under the product name IONSIV R9140-B, 18 x 50 mesh, in a lead-lag-polish column configuration. Each column contains a CST bed height of 92 inches and a volume of approximately 157 gallons of CST IX media. The full-scale TSCR operation will run at a residence time of 1.9 bed volumes (BV)/h, which results in a superficial velocity of 7.3 cm/min and a flowrate of ~5 gal/min. Column testing at small (2.5% of the full bed height), medium (12% of the full bed height), and full-height scales has been previously conducted to evaluate process variables and scale up performance of Cs exchange onto the CST. Cesium load performances from various sieve cuts at the small scale indicated that a 30-mesh sieve cut be tested to determine if it better reflects the 12% and full-height column performances at the small scale. Two process flowrates were tested in the small-column configuration with <30-mesh CST and simulant solutions. These tests were compared to full-height column tests at the same residence times to assess CST particle size effect on column scaling. Table S.1 summarizes the observed column performance determined for the two flowrates juxtaposed to the previous work with 5.6 M Na simulant at higher scales (used as benchmarks). The WAC breakthroughs between the small and full-height tests at 1.3 BV/h differed by ~59 BVs. The WAC breakthroughs for small, medium, and full-height tests at 1.8 BV/h were consistent at 240 BVs. The 50% Cs breakthroughs were nominally equivalent for all column tests. The common transition zones and onsets of Cs breakthrough at the 1.8 BV/h tests indicated that the Cs mass transfers were equivalent and thus the <30-mesh CST Cs load performance at the small scale successfully modeled that of the full-height system. It is recommended that the <30-mesh CST be used in subsequent 10-mL CST bed tests.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Corrosion of 347 stainless steel in the presence of uranyl sulfate solution and radiation

The U.S. molybdenum 99 (Mo-99) industry is pursuing production of fission-made Mo-99 using a uranium solution such as uranyl sulfate. In this process, uranyl sulfate solution containing low-enriched uranium will be bombarded by neutrons creating Mo-99 and other fission products. During the production, the uranyl sulfate solution will be irradiated until an acceptable activity level of Mo-99 is produced. The uranyl sulfate solution containing Mo-99 and other fission products will then undergo a series of separation steps. First, uranyl sulfate can be separated from Mo-99 using a primary titania column to recover Mo-99, with the uranyl sulfate solution to be used for another irradiation cycle. Then, raffinate from a primary titania column containing Mo-99 can be concentrated and purified using a LEU modified Cintichem process developed by Argonne National Laboratory. During irradiation, the temperature of the uranyl sulfate solution can reach near boiling (up to ~80° C assumed), causing radiolysis of water and the resultant formation of hydrogen peroxide. Because high-radiation fields will be present during each irradiation cycle, it is important to determine the corrosion rates of SS-347 under such conditions to estimate the life cycle of the target solution vessel. The buildup of corrosion products from the SS components in the uranyl sulfate solution also needs to be well understood because potential accumulation of iron, nickel, and other corrosion products may affect the Mo-99 recovery and purification process. To study the corrosion rates of SS-347 material under conditions relevant to future Mo-99 production facility, SS-347 coupons in uranyl sulfate solution at ~80° C were irradiated using Argonne’s Van de Graaff generator, which can generate high-radiation fields without fissioning of uranium or production of activation products.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Soil water content, matric potential, carbon dioxide and oxygen concentrations, Oct 2018-Dec 2021, Slate River Floodplain, Crested Butte, Colorado

This data package includes a time series of soil sensor data (temperature, water content, bulk electrical conductivity, porewater dissolved oxygen and porewater dissolved carbon dioxide) in a vertical profile from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package includes: (1) soil temperature, volumetric water content and electrical conductivity at 40, 60 and 82.5 cm depth; (2) soil matric potential at 40, 60, 79 and 100 cm depth; (3) soil CO2 concentrations at 40, 60 and 82.5 cm depth; and (4) soil oxygen concentrations at 60, 82.5, 100, 135, 170 and 182 cm depth. Both the carbon dioxide and oxygen sensors are optical sensors that can measure the partial pressure of oxygen in both saturated and unsaturated conditions. Unfortunately, soil CO2 in the profile is unexpectedly high and above the sensor calibration range (0-25,000 ppm). In addition, soil CO2 sensors failed within a year of deployment, so we only report CO2 data from 2019-2020.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 4 "*data.csv" files, one for each calendar year in the dataset. Each "*data.csv" file has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding for each measured parameter. Because sensors have been added over time, not every sensor has data dating back to Oct 2018. Note that there is a data gap over winter 2019-2020 due to a power outage. While this repository currently only contains data through December 2021, the dataset will be updated as additional years are collected and processed.

54 ENVIRONMENTAL SCIENCES↗

Representing surface heterogeneity in land–atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime

Abstract. The Earth's land surface features spatial and temporal heterogeneity over a wide range of scales below those resolved by current Earth system models (ESMs). State-of-the-art land and atmosphere models employ parameterizations to represent their subgrid heterogeneity, but the land–atmosphere coupling in ESMs typically operates on the grid scale. Communicating the information on the land surface heterogeneity with the overlying atmospheric boundary layer (ABL) remains a challenge in modeling land–atmosphere interactions. In order to account for the subgrid-scale heterogeneity in land–atmosphere coupling, we implement a new coupling scheme in the Energy Exascale Earth system model version 1 (E3SMv1) that uses adjusted surface variances and covariance of potential temperature and specific water content as the lower boundary condition for the atmosphere model. The new lower boundary condition accounts for both the variability of individual subgrid land surface patches and the inter-patch variability. The E3SMv1 single-column model (SCM) simulations over the Atmospheric Radiation Measurement (ARM) Southern Great Plain (SGP) site were performed to assess the impacts. We find that the new coupling parameterization increases the magnitude and diurnal cycle of the temperature variance and humidity variance in the lower ABL on non-precipitating days. The impacts are primarily attributed to subgrid inter-patch variability rather than the variability of individual patches. These effects extend vertically from the surface to several levels in the lower ABL on clear days. We also find that accounting for surface heterogeneity increases low cloud cover and liquid water path (LWP). These cloud changes are associated with the change in cloud regime indicated by the skewness of the probability density function (PDF) of the subgrid vertical velocity. In precipitating days, the inter-patch variability reduces significantly so that the impact of accounting for surface heterogeneity vanishes. These results highlight the importance of accounting for subgrid heterogeneity in land–atmosphere coupling in next-generation ESMs.

54 ENVIRONMENTAL SCIENCES↗

The NanoSIMS-HR: The Next Generation of High Spatial Resolution Dynamic SIMS

The high lateral resolution and sensitivity of the NanoSIMS 50 and 50L series of dynamic SIMS instruments have enabled numerous scientific advances over the past 25 years. Here, in this study, we report on the NanoSIMS-HR, the first major upgrade to the series, and analytical tests in a suite of sample types, including an aluminum sample containing silicon crystals, microalgae, and plant roots colonized with a symbiotic fungus. Significant improvements have been made in the Cs + ion source, high voltage (HV) control, stage reproducibility, and other aspects of the instrument that affect performance. The modified design of the NanoSIMS-HR thermal-ionization Cs + source enables a 5 pA primary ion beam to be focused into a 100 nm spot, a ~2.5-fold increase compared to Cs + sources on previous instruments (~2 pA at 100 nm). The brightness of the new Cs + source enables an ultimate lateral resolution as high as 30 nm and improved detection limits for a given analysis area. Sample stage movement accuracy is higher than 500 nm, enabling many-fold higher throughput automated analyses. With the new HV control, the primary ion beam impact energy can be reduced from 16 to 2 keV, which enables higher depth resolution during depth profiling (a 2-fold improvement), albeit with a 5-fold decrease in lateral resolution. In the NanoSIMS-HR, the secondary ion column and detection system are identical to those used in the previous series, and the isotopic analysis performance is as precise as in previous NanoSIMS instruments.

54 ENVIRONMENTAL SCIENCES↗

Impact of Variable Gas Mixtures on Bubble Size Distribution and Mass Transfer in Gas Fermentation Reactors

Gas fermentation has emerged as a promising new technology for the generation of fuels and chemicals from mixtures of greenhouse and energy rich gas streams (CO2/CH4/H2/CO) via microbial bioreaction. Example pathways include biomethanation (CO2/H2 to CH4), biogas upgrading, CO fermentation and wet-waste conversion. Effective Gas-liquid mass-transfer is an important physical phenomenon that determines the design and scale-up of these systems. There is currently a knowledge-gap regarding bubble-size distributions when using a mixture of gases with vastly different properties, which can have a significant impact on overall mass-transfer. For example, hydrogen bubbles are more buoyant compared to other relatively heavier gases (CO2/CH4/CO), resulting in a large distribution of residence times and bubble sizes. This work therefore develops a deeper understanding of bubble dynamics and interphase mass transfer in such heterogenous gas mixtures through well-resolved computational models. We use a detailed multiphase computational-fluid-dynamics (CFD) model to study the impact of gas-mixtures on overall mass-transfer in bubble column and air-lift reactors. The CFD tool previously developed by the authors (1) for simulating aerobic fermentation reactors at scale is used in this study. The Reynolds-averaged mass, momentum, energy, and species transport equations are solved for interpenetrating gas and liquid phase in this model. We use a population balance-based bubble-size-distribution model that is validated against small-scale experiments in our solver. Results pertaining to multiple simulations of gas-fermentation reactors are presented where gas mixtures with varying compositions of CO2/CH4/CO/H2 are imposed at the sparger boundaries. The spatio-temporal variations in bubble-size distribution and mass transfer coefficient is analyzed for varying superficial velocities and gas-compositions for varying sizes of bubble-column and airlift reactors.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗

Dynamic Rupture Simulations of Caldera Collapse Earthquakes: Effects of Wave Radiation, Magma Viscosity, and Evidence of Complex Nucleation at Kilauea 2018

All instrumented basaltic caldera collapses have generated M w > 5 very long period earthquakes. However, previous studies of source dynamics have been limited to lumped models treating the caldera block as rigid, leaving open questions related to how ruptures initiate and propagate around the ring fault, and the seismic expressions of those dynamics. Here, we present the first 3D numerical model capturing the nucleation and propagation of ring fault rupture, the mechanical coupling to the underlying viscoelastic magma, and the associated seismic wavefield. We demonstrate that seismic radiation, neglected in previous models, acts as a damping mechanism reducing coseismic slip by up to half, with effects most pronounced for large magma chamber volume/ring fault radius or highly compliant crust/compressible magma. Viscosity of basaltic magma has negligible effect on collapse dynamics. In contrast, viscosity of silicic magma significantly reduces ring fault slip. We use the model to simulate the 2018 Kilauea caldera collapse. Three stages of collapse, characterized by ring fault rupture initiation and propagation, deceleration of the downward-moving caldera block and magma column, and post-collapse resonant oscillations, in addition to chamber pressurization, are identified in simulated and observed (unfiltered) near-field seismograms. A detailed comparison of simulated and observed displacement waveforms corresponding to collapse earthquakes with hypocenters at various azimuths of the ring fault reveals a complex nucleation phase for earthquakes initiated on the northwest. Our numerical simulation framework will enhance future efforts to reconcile seismic and geodetic observations of caldera collapse with conceptual models of ring fault and magma chamber dynamics.

58 GEOSCIENCES↗

Coastal typologies and surface and subsurface characteristics of the Alaskan Beaufort Sea Coast

This dataset was generated to classify the Alaskan Beaufort Sea Coast (ABSC) into a set of distinct coastal typologies, to understand the surface and subsurface characteristics and variability of the ABSC, and to quantify relationships between these characteristics and historical rates of shoreline change. This geospatial dataset contains two csv files of points along the ABSC at a 50 m spacing, one for points sheltered by a barrier island and one for points exposed to the open ocean. Each point has a lat/lon location, and we have attributed to each point average values for elevation, historical long-term shoreline change rates, shoreline orientation, landcover, mean annual ground temperature, geomorphic unit, lithology, geology, ecological landscape unit, maximum thaw settlement potential, massive ice content, and segregated ice content. Each point is also assigned to one of 16 coastal typologies, determined by a hierarchical clustering algorithm on the elevation, shoreline change, orientation, and ground temperature data. There are 9 sheltered typologies and 7 exposed typologies, identified by an integer label in the last column of each csv file. The other two csv files contain the integer IDs and classes for the landcover and geomorphology datasets.

54 ENVIRONMENTAL SCIENCES↗

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗

Purification and Concentration of 99 Mo from a UREX raffinate

The medical isotope 99m Tc ( t ½ = 6.0 h) accounts for over 80% of isotopes used in diagnostic nuclear medicine today and is normally derived from its transient equilibrium parent 99 Mo ( t ½ = 66 h). To reduce U.S. dependence on 99 Mo derived from HEU by foreign suppliers, the NNSA/M3 program—under the American Medical Isotope Production Act—has been tasked with facilitating the work of domestic 99 Mo suppliers that do not utilize HEU. Superconducting electron linear accelerators with high-Z converter targets can generate bremsstrahlung photons and neutron fluxes that can induce photonuclear reactions and LEU fission. After sufficient production intervals, targets can be rotated out and processed while another batch is irradiated. The process flow chemistry is shown in Figure 1. The irradiated U 3 O 8 targets are retrieved and dissolved in HNO 3 ; the volatile fission products are expelled and captured during this step. The HNO 3 liquor bearing the uranyl, fission and activation products is injected into the UREX suite of liquid-liquid extraction banks. The uranyl is partitioned in typical fashion using tri-$\textit{n}$-butyl phosphate (TBP) in a hydrocarbon diluent. The raffinate—comprising mainly 99 Mo and fission products—is then injected into the MoLLE (Molybdenum Liquid-Liquid Extraction) flowsheet, where the Mo is selectively extracted by an organophosphorous acid extractant such as di-(2-ethylhexyl phosphoric acid) (HDEHP). Trace amounts of Nb, Zr, Np, I, and Te are co-extracted. The Mo is stripped using acetohydroxamic acid (AHA) and fed onto an anion exchange column. Following a series of hydroxide, HCl, and oxalic acid wash steps, the Mo can be recovered in NaOH/NaCl. A manuscript describing this process chemistry in more detail was recently published.

07 ISOTOPE AND RADIATION SOURCES↗

Dense Seismic Array Study of a Legacy Underground Nuclear Test at the Nevada National Security Site

The complex postdetonation geologic structures that form after an underground nuclear explosion are difficult to constrain because increased heterogeneity around the damage zone affects seismic waves that propagate through the explosion site. Generally, a vertical rubble-filled structure known as a chimney is formed after an underground nuclear explosion that is composed of debris that falls into the subsurface cavity generated by the explosion. Compared with chimneys that collapse fully, leaving a surface crater, partially collapsed chimneys can have remnant subsurface cavities left in place above collapsed rubble. The 1964 nuclear test HADDOCK, conducted at the Nevada test site (now the Nevada National Security Site), formed a partially collapsed chimney with no surface crater. Understanding the subsurface structure of these features has significant national security applications, such as aiding the study of suspected underground nuclear explosions under a treaty verification. In this study, we investigated the subsurface architecture of the HADDOCK legacy nuclear test using hybrid 2D–3D active source seismic reflection and refraction data. The seismic data were acquired using 275 survey shots from the Seismic Hammer (a 13,000 kg weight drop) and 65 survey shots from a smaller accelerated weight drop, both recorded by ~1000 three-component 5 Hz geophones. First-arrival, P-wave tomographic modeling shows a low-velocity anomaly at ~200 m depth, likely an air-filled cavity caused by partial collapse of the rock column into the temporary postdetonation cavity. A high-velocity anomaly between 20 and 60 m depth represents spall-related compaction of the shallow alluvium. Hints of low velocities are also present near the burial depth (~364 m). The reflection seismic data show a prominent subhorizontal reflector at ~300 m depth, a short-curved reflector at ~200 m, and a high-amplitude reflector at ~50 m depth. Comparisons of the reflection sections to synthetic data and borehole stratigraphy suggest that these features correspond to the alluvium–tuff contact, the partial collapse cavity, and the spalled layer, respectively.

58 GEOSCIENCES↗

A Novel Technique for Distributed Measuring of Strain in the Vadose Zone

Characterizing water content and pressure changes in the vadose zone is important to understanding a variety of geologic processes, ranging from permeability, to evapotranspiration, and aquifer recharge. Changes in water content, or pressure, cause strain in the solid porous medium of the vadose zone, and it is possible that measuring those strains could be used a characterization tool. The Coherence-length-gated Microwave Photonics Interferometry (CMPI) technique measures strain at high resolution along many intervals defined by pairs of reflectors distributed along an optical fiber. This technique has recently been developed at Clemson University, and it has the spatial, and temporal resolution to characterize the strains that are expected to occur with hydrologic changes in the vadose zone. However, the technique has never been used to measure strain in porous media, so its capabilities remain uncertain. The objective of this thesis is to evaluate the ability of using CMPI to measure strain changes in the vadose zone. The research approach consists of conducting laboratory tests using a column filled with sand that was subjected to changes in water content and pressure. An optical fiber with CMPI reflectors was deployed in a high surface area ribbon and used to measure strain along the axis of the column. The column was made from 8-inch, Schedule 40 PVC pipe (75 cm tall, 20 cm inner diameter) and filled with medium-grained sand (K = 2.5x10-6 m/s, porosity = 0.28, Coefficient of Uniformity = 2, van Genuchten (n = 4.34 and  = 0.00039 m-1), Young’s Modulus = 18 – 47 MPa). Five pressure and four temperature sensors attached to probes inserted into the wall. The optical fiber includes five reflectors spaced 10 cm apart on the inside and outside of the column along the vertical axis. Strain measurements are made between pairs of reflectors. The reflectors are created in 250-micron-diameter acrylate-coated single mode Corning SMF28e+ optical fiber using a femtosecond laser. The optical fiber was laminated between two pieces of polyester film creating a large surface area to transfer strain from the porous media to the optical fiber. The experiments were conducted by filling the column from the bottom or infiltrating water from the top. The water was allowed to equilibrate to room temperature prior to each test in order to limit thermoelastic strain. Five injection tests with a rate of 250 ml/min and three infiltration tests at varying rates were conducted, and the results show patterns of strain and pressure are generally similar. Hydrologic conditions define three zones based on the pressure magnitude and distribution. 1.) Ambient Zone where the pressure heads are quasi-static and the pressure gradient is roughly unity (head gradient of zero). This is the uppermost zone and is characterized by negative pressures. 2.) Transition Zone where the pressures increase from ambient to zero, are changing relatively rapidly and the pressure gradient is relatively steep (pressure head gradients of 2). The Transition zone is 10 to 15 cm thick. 3.) Positive Pressure Zone where the pressure is positive, the rate of change is slower than in the transition zone and the gradient is flatter (pressure head gradient 1.1 to 1.2). This is the lowest zone in the column. Injection of water causes the pressure to increase and the three zones to move upward. The Transition zone moves at a rate of approximately 0.0002 m/s +/- .00005, according to analyses of pressure profiles. This is the same as the average velocity of the water calculated as volumetric flux/effective porosity =5.4x10-5 m/s/0.28. Strain signals in the range of 10s  were observed with a noise level of generally less than 0.1  (signal to noise ratio of greater than 100) during injection and drainage. The spatial and temporal distributions of strain caused by injection depend on the location of the moving pressure zones. The locations of the different pressure zones were determined from pressure profiles at different times and these data were transferred to strain time series. This showed that strain in the Ambient pressure zone is either unchanged or slightly compressive, whereas strain in Transition zone is tensile and roughly proportional to the pressure change, and strain ranges from tensile to compressive in the Positive Pressure zone. In general, the strain is variable at the top of the Positive Pressure zone, but it appears to be consistently tensile lower in the zone. The spatial distribution of strain causes three distinct stages in the strain times series measured between pairs of CMPI reflectors. The strain is unchanged or slightly compressive during Stage 1, it increases (positive strain is tensile) approximately linearly at a rate of 0.02 to 0.03 μ/s during the Stage 2, and then flattens out in Stage 3. The strain increases again during Stage 4 of the time series. The pressure also changes in stages. It is unchanged during Stage 1 and then increases relatively rapidly at a rate of between 4 and 10 Pa/s during Stage 2 and continues to increase during Stages 3 and 4, but at a rate slightly slower than during Stage 2 (from 2.5 to 3.5 Pa/s). The rates of pressure and strain change are consistent with basic analyses. The rate of pressurization is similar to the calculated velocity of flow in the saturated zone, and this is similar to the measured velocity of the strain increase. The ratio of the pressurization rate and strain rate during Stage 2 (2.5 (Pa/s) /0.03 (μ/s)) is 80 MPa, which is approximately equal to the upper range expected considering the uniaxial compression and the Young’s Modulus of the sand. An interesting effect occurs when the upper surface of the sand becomes saturated. Significant compression (several 10s of ) occurs as the pressure and saturation increase at the upper surface. Compression occurs throughout the column, but the effect is greatest at the top of the column. The rate of compression is fastest slightly before ponding occurs, but it slows markedly and nearly stops when water starts to accumulate at the surface (ponding). This effect reverses when the surface of the sand is drained, resulting in tension throughout the column. The compression caused by this effect can be as large or larger than the tensile strain that accumulated during filling. This effect was unexpected because increasing pressure is normally associated with tensile strain. Nevertheless, this effect was observed consistently in all tests where the pressure changes at the upper surface, including tests where water was injected from below or infiltrated from above. This effect behaves as if the pore pressure at the upper surface of the sand exerts a normal force on the boundary (increasing pore pressure exerts a downward compression on the boundary). The results of these laboratory experiments indicate that CMPI can measure strain caused by fluid pressure changes in the vadose zone with a signal to noise ratio of 100 or more. Injection and drainage cause a strain signal that is complex, but repeatable. The magnitudes of the strain signal are consistent with magnitudes that are expected based on poroelastic calculations using independently measured properties of the sand. These results indicate that the CMPI technique with an optical fiber laminated in a polyester ribbon generates data that represent the strain distribution during hydrologic processes of imbibition and drainage in variably saturated sand. This suggests that distributed strain measurements using CMPI could be a viable approach for evaluating processes in the vadose zone, laying the groundwork for future field implementation.

47 OTHER INSTRUMENTATION↗

TEAMER: Biofouling Analysis for Wave Energy Piston Design - Load Cell Data

Biofouling and corrosion are a major concern for all ocean-deployed components, especially when mechanical motion is involved. Triton has developed the concept of a biofouling mitigation seal as part of the piston sealing assembly for the Triton Wave Energy Converter (TSI-WEC). This mitigation seal has the purpose of preventing the formation of a biofilm on the inside of the piston cylinder. It is hypothesized that the prevention of a biofilm will reduce the amount of macro-biofouling that can occur in the piston assembly. The mitigation seal can also reduce the wear on the main dynamic seal, helping to maintain smooth operation and water-tightness. The cylinder is made from a thermoset composite epoxy, which is resistant to corrosion. However, no studies have researched the material's performance with biofouling. Triton placed two prototype Power Take-Off (PTO) assemblies in a PNNL biofouling tank, one with a biofouling mitigation seal and one without, allowing for an evaluation of seal effectiveness at the prevention of biofouling. In actual WEC operation, wave action would react against the piston, which would drive the linear actuator and electric generator, providing electrical power. In the test setup, this was reversed; a linear actuator was powered to drive the piston in a consistent motion within the cylinder. There are two assemblies: one has a biofouling mitigation seal, the other (control) does not. The following data encompasses a 4 month test period, with load cells being used to monitor piston friction force. Results from this testing will be used to improve seal design and material selection, mitigating risk of premature failure during open water testing and evaluation. This project is part of the TEAMER RFTS 3 (request for technical support) program.

16 TIDAL AND WAVE POWER↗