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Densely populated biofilms and linked iron and sulfur cycles in the fractured-rock continental subsurface
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Play Fairway analysis of geothermal resources across the State of Hawai‘i: 4. Updates with new groundwater chemistry, subsurface stress analysis, and focused geophysical surveys
This paper is the fourth in a series on a play fairway analysis of geothermal resources across the State of Hawai‘i. Here we describe recent exploration activities that include groundwater sampling in ten locations statewide, as well as geophysical surveys on Lana‘i, across the SW rift of Haleakala Volcano (Maui), and surrounding Mauna Kea Volcano (Hawai‘i Island). We derive a first-order method for computing topographic stress using Green’s functions, finding that topography can induce appreciable crustal stress. We develop a new method for incorporating depth information about resistivity, density, and topographic stresses into our previously published equations to calculate resource probability and confidence. We incorporate newly collected and modeled data into our calculations to update statewide maps of probability and confidence. Lana‘i Island and southeast Mauna Kea are identified as the top targets for exploratory drilling. The east rift of Haleakala Volcano, the southern-most region of Mauna Loa’s SW rift (Hawai‘i Island), and central Kauai are identified as targets for geophysical surveying.
Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage
In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.
In situ precipitation of hydrous ferric oxide (HFO) for remediation of subsurface iodine contamination
A practical approach for in-situ hydrous ferric oxide (HFO) precipitation was developed for iodine immobilization under field-scale conditions at the Hanford Site. A series of 1D meter-long bench top column experiments packed with Hanford sediments was conducted with a single acidic ferric solution (0.1 M, pH = 1.5) injection. Because carbonate and clay minerals are widely present in sediments, self-pH buffering of the injected acidic ferric solution occurred due to mineral dissolution, leading to HFO precipitation under a neutral condition. Up to ~12 mg/g HFO successfully precipitated and evenly distributed in the column sediments, and the remobilization of the neo-formed HFO precipitates was limited (< 4 wt.% after 100 pore volumes of flushing). The transport of IO3- in the HFO-amended sediments was strongly retarded through both adsorption and co-precipitation processes. However, reversible adsorption of iodine on HFO was observed, which might limit its application to slow moving groundwater systems.
Numerical evaluation of photosensitive tracers as a strategy for separating surface and subsurface transient storage in streams
For this work, we numerically evaluated photosensitive tracers as a potential strategy for separating the effects of surface and hyporheic storage zones (SSZs and HSZs, respectively) on stream corridor transport. Correctly separating HSZ and SSZ effects is critical to estimating the hydro-biogeochemical function of a stream because HSZs and SSZs expose solutes to significantly different biogeochemical conditions, like sunlight exposure, microbial processes, and oxygen availability. Our numerical experiments used a multiscale river-corridor transport model implemented in the ATS code, which accommodates multiple storage zones with distinct travel time distributions and biogeochemical reactions. For parameter inferences, we used Bayesian inverse modeling. We found that breakthrough curves for photo-decaying tracers from day and night injection can delineate surface and hyporheic transient storage contributions, but only when interpreted jointly through a two-storage zone model. Numerical experiments that used only daytime injection or interpreted breakthrough curves with a single storage zone model yielded good fit to breakthrough curves, but parameter estimates were biased and controlling processes misattributed, examples of good model fits for the wrong reasons. Using those biased parameter estimates in reactive transport simulations resulted in significantly different projections of denitrification, which underscores the potential for stream function to be mischaracterized if tracer tests are interpreted through an inappropriately simplified model for transient storage. More generally, this study highlights the role of modeling in evaluating the experimental design and identifying the potential of system mischaracterization and its implications.
Heat pulse testing at monitoring wells to estimate subsurface fluid velocities in geological CO 2 storage
Monitoring the injected CO 2 during geological CO 2 storage (GCS) is essential to assure containment and identify CO 2 leakage. Here in this work, a new approach is introduced to estimate the evolution of the downhole fluid velocity at a monitoring well and identify CO 2 arrival time using in-well heat pulse/tracer test. The proposed technique involves using a downhole heater to generate a series of heat pulses and measuring their corresponding temperature response. The surface temperature of the downhole heater is controlled by the supplied electrical power and the heat loss by convection to the surroundings. Convective heat transfer is well described using Newton's law of cooling in which the temperature difference between the heater and the surrounding fluids drives the heat transfer, for which the convection heat transfer coefficient (h) controls the magnitude of heat loss. Among various factors that control h, it depends on the type of the flowing fluid and its velocity. Through analyzing the measured temperature at different heat pulses, the changes in h - due to mobilization of the in-situ brine or CO 2 arrival - can be estimated. Consequently, the velocity of the flowing fluid across the heater can be obtained. Since heat transfer by convection is sensitive to the type of the surrounding fluid, intrusion of CO 2 can be detected from the relatively higher surface temperature obtained at CO 2 arrival. Churchill and Bernstein (1977)'s correlation is adopted to estimate the change of fluid velocity in terms of the change in h. To demonstrate the validity of the proposed technique, the results are applied and validated against those of COMSOL Multiphysics simulation tool for single-phase brine (before CO 2 arrival) and single-phase CO 2 (after CO 2 arrival). The observed temperature heating is sensitive to the flowing fluid velocity and fluid type. The temperature signal observed at CO 2 arrival is large and easily detectable using temperature monitoring tool which provides reliable indication for tracking CO 2 arrival at monitoring wells compared with passive temperature monitoring. The results obtained using the proposed technique agree very well with the numerical results obtained from the simulation tool with a maximum estimation error of 7 percent.
Reproducible responses of geochemical and microbial successional patterns in the subsurface to carbon source amendment
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Subsurface Transport of Plutonium in Organic and Aqueous Acidic Processing Wastes at the Hanford Site, USA
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Short-Term Groundwater Level Fluctuations Drive Subsurface Redox Variability
As global change processes modify the extent and functions of terrestrial-aquatic interfaces, the variability of critical and dynamic transitional zones between wetlands and uplands increases. However, it is still unclear how fluctuating water levels at these dynamic boundaries alter groundwater biogeochemical cycling. Here, we used high-temporal resolution data along gradients from wetlands to uplands and during fluctuating water levels at freshwater coastal areas to capture spatiotemporal patterns of groundwater redox potential (E h ). We observed that topography influences groundwater E h that is higher in uplands than wetlands; however, the high variability within TAI zones challenged the establishment of distinct redox zonation. Declining water levels generally decreased E h , but most locations exhibited significant E h variability, which is associated with rare instances of short-term water level fluctuations introducing oxygen. The E h -oxygen relationship showed distinct hysteresis patterns, reflecting redox poising capacity at higher E h , maintaining more oxidizing states longer than dissolved oxygen presence. Surprisingly, we observed more frequent oxidizing states in transitional areas and wetlands than in uplands. We infer that occasional oxygen entering specific wetland-upland boundaries acts as critical biogeochemical control points. Finally, high-resolution data can capture such rare yet significant biogeochemical instances, supporting redox-informed models and advancing the predictability of climate change feedback.
Subsurface Nitrogen Dissociation Kinetics in Lithium Metal from Metadynamics
The dissociation of molecular nitrogen in lithium is of interest for several promising technologies, such as the catalytic synthesis of ammonia in ambient or mild conditions. In this work we simulate nitrogen dissociation in the lithium BCC (110) surface at ambient and elevated temperatures using density functional theory (DFT) metadynamics simulations. The rate constants at temperatures of 300, 400, and 500 K are calculated by statistical analysis of the reaction time distributions from the accelerated simulations. This approach finds and estimates rate constants for transition pathways out of the initial state; the required input is the stable initial state and a reasonable choice of collective variable. A single collective variable is used in this case: the N–N distance. The results are robust to changes in metadynamics parameters, and the reaction time distributions follow the expected exponential distribution. We show that the metadynamics-derived rate constants are in agreement with results from the conventional harmonic approximation approach using a climbing image nudged elastic band (NEB) transition state search. The reaction barriers from metadynamics and the NEB/harmonic approximation agree to within 0.02–0.04 eV at all temperatures studied. This paper demonstrates that the harmonic approximation provides an accurate description of the rate constants for nitrogen dissociation in lithium metal, even at temperatures near or above the melting point of lithium, lending credence to previous and future theoretical studies using this approximation. Moreover, this work demonstrates a step toward the automated exploration and discovery of reaction mechanisms and associated rate constants for elementary surface-catalyzed reactions using DFT-based metadynamics.
Subsurface Interface Structure Controlling Local Electronic Properties of Epitaxial Graphene on SiC(0001)
Recently realized high-mobility semiconducting epitaxial graphene on silicon carbide (SiC) [Zhao, J. Nature 2024, 625 (7993), 60−65, 10.1038/s41586-023-06811-0] provided an important step toward integration of the graphene-based system into active components in postsilicon micro- and nanoelectronics. However, the exact atomic-scale structure and complex bonding configurations of the first epitaxial graphene carbon layer (C buffer ) remain an open problem. Our recent report [Kolmer, M. Communications Physics 2024, 7 (1), 16, 10.1038/s42005-023-01515-3] has shed new light on understanding this interface, where the external transverse electric field-dependent dynamic switching behavior of the C buffer –SiC bonds was observed. Here, using scanning tunneling microscopy and spectroscopy (STM and STS), we present direct evidence of silicon (Si) vacancies at the interface and provide their distribution at the topmost reconstructed SiC(0001) layer. Bias voltage and epitaxial graphene thickness-dependent characterization of the collective C buffer –SiC interface showed that “Si” vacancy sites beneath C buffer are stable under STM electric fields. Moreover, the vacancies introduce localized electronic states below the Fermi level, thereby enhancing the charge-transfer phenomenon across the interface.
Temperature and Pressure Dependence of Salt–Brine Dihedral Angles in the Subsurface
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