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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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Enhancing Ce x Zr 1– x O 2 Activity for Methane Dry Reforming Using Subsurface Ni Dopants
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Enabling High Precision Gradient Index Control in Subsurface Multiphoton Lithography
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Microbial Community Composition in Deep‐Subsurface Reservoir Fluids Reveals Natural Interwell Connectivity
Abstract The identification of natural fractures and the wells they connect is crucial for the development of geological reservoirs because it may have an important impact on reservoir model construction and hydraulic fracture propagation. In this study we investigated the use of a novel data source, the microbial community composition in the reservoir formation fluids, for identification of interwell connectivity caused by natural fractures. We verified this concept at a newly developed mesoscale enhanced geothermal system (EGS) field testbed located 4,850 ft (1,478 m) beneath ground surface at the Sanford Underground Research Facility in Lead, SD. Fluids produced at or near the EGS testbed were sampled and subjected to high‐throughput 16S rRNA gene amplicon sequencing to analyze the microbial community profile therein. Despite the typically substantial heterogeneity across the community profiles of samples spatially distributed (10 m to 1.9 km apart) throughout the site, samples from two wells at the EGS testbed showed highly similar microbial community composition, suggesting the two wells intersected the same natural fracture. This evidence of natural connectivity between the two wells at the EGS testbed was later corroborated by core log analysis and sewer camera surveys into the boreholes. Besides the field case described in this study, microbial community composition as a reservoir diagnostic tool would be applicable in a much broader context such as unconventional hydrocarbon exploitation, groundwater reservoir characterization, and environmental remediation, adding valuable “hard” data capable of pinpointing the origins of fluids unambiguously.
Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems
In this paper, we present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) models. We use PDEs in addition to measurements to train DNNs to approximate unknown parameters and constitutive relationships as well as states. The proposed approach increases the accuracy of DNN approximations of partially known functions when a limited number of measurements is available and allows for training DNNs when no direct measurements of the functions of interest are available. We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation. For the parameter estimation problem, we assume that partial measurements of the coefficient and states are available and demonstrate that under these conditions, the proposed method is more accurate than state-of-the-art methods. For the non-linear diffusion PDE model with a fully unknown constitutive relationship (i.e., no measurements of constitutive relationship are available), the physics informed DNN method can accurately estimate the non-linear constitutive relationship based on state measurements only. Finally, we demonstrate that the proposed method remains accurate in the presence of measurement noise.
Potential Chemical Impacts of Subsurface CO 2 : An Integrated Experimental and Numerical Assessment for a Case Study of the Ogallala Aquifer
Leakage from geologic CO 2 sequestration (GCS) reservoirs to overlying underground sources of drinking water (USDW) is a tangible risk. This study is an integrated assessment that combines column experiments and reactive transport simulations of sediments sampled from the Ogallala aquifer above an active commercial-scale GCS site (the Farnsworth Unit in northern Texas). Experimental and simulation results suggest that carbonate mineral (calcite and dolomite) dissolution is the most significant reaction following CO 2 intrusion, and is also the dominant source of trace metal release. Cation exchange is another key mechanism controlling trace metal release by cation interference. Most of the trace metals, including Ba, Sr, As, Pb, and Zn, show a short-term release and quickly drop to the baseline values, suggesting low risk to the overlying USDW quality. Other trace metals, such as Mn and U, exhibit a tangible increase of their concentrations in the beginning, and drop to a higher level compared to the baseline, which may become a potential concern of long-term USDW quality change with CO2introduction. This study provides a comprehensive example of combining laboratory experiments and simulations for assessment of CO 2 -sediment interactions with combined release mechanisms in shallow groundwater aquifers. Data presented here provides useful insights for quantitative risk assessment and effective public education regarding CO 2 geological sequestration, and trace metal reactive transport studies in shallow groundwater aquifers.
Understanding the Hydrogeochemical Response of a Mountainous Watershed Using Integrated Surface‐Subsurface Flow and Reactive Transport Modeling
Abstract Climate change and other disturbances significantly impact hydrogeochemical exports from mountainous headwater catchments such as the Upper Colorado River Basin. Developing a mechanistic understanding of how the physical and chemical processes interact in time and space in an integrated manner is key to quantifying the future impacts of such disturbances. The hydrogeochemical response of a mountainous catchment in the 2010–2019 period is evaluated quantitatively using a high‐resolution model that simulates integrated hydrology, and transport and reactions for selected solutes and minerals. The model assumes that pyrite is present only at depth while calcite is distributed uniformly, and captures the observed C‐Q reasonably well. Distinct C‐Q dynamics are observed in an average (WY16), a wet (WY17), and a dry (WY18) water year. The model also quantifies the water fraction from surface, shallow and deep groundwater compartments using tracers, and suggests greater groundwater contributions to peak stream discharge in the dry WY18. Results demonstrate that calcium concentrations do not change significantly from year to year, while sulfate shows significant temporal variability. Pyrite dissolution is affected by the changing hydrological drivers where it is enhanced in the dry WY18; calcite dissolution supplements calcium dilution under high flow conditions. The model simulates the reaction hotspots controlled by hydrological conditions, and the spatially‐resolved results show that higher soil saturation and less snowpack occur earlier on the south‐facing side than on the north‐facing side. This is a first‐of‐its‐kind demonstration of a model that integrates hydrologic processes, including evapotranspiration, and reactive transport to enable a predictive understanding of hydrogeochemical exports.
Impacts of Permeability Heterogeneity and Background Flow on Supercritical CO 2 Dissolution in the Deep Subsurface
Motivated by CO 2 capture and sequestration (CCS) design considerations, we consider the coupled effects of permeability heterogeneity and background flow on the dissolution of a supercritical CO 2 lens into an underlying deep, confined aquifer. We present the results of a large-scale Monte Carlo simulation study examining the interaction of background flow rate and three parameters describing multi-Gaussian log-permeability fields: mean, variance, and correlation length. Hundreds of high-resolution simulations were performed using the PFLOTRAN finite volume software to model CO 2 dissolution in a kilometer-scale aquifer over 1,000 years. Predictive dimensionless scaling relationships relating CO 2 dissolution rate to heterogeneity statistics, Rayleigh (Ra) and Péclet (Pe) numbers were developed for both gravitationally dominated free convection to background flow-dominated forced convection regimes. An empirical criterion, Pe = Ra 3/4 , was discovered for regime transition. All simulations converged quickly to a quasi-steady, approximately linear dissolution rate. However, this rate displayed profound variability between permeability field realizations sharing the same heterogeneity statistics, even under mild permeability heterogeneity. In general, increased heterogeneity was associated with a lower mean and higher variance of dissolution rate, undesirable from a CCS design perspective. The relationship between dissolution rate and background flow was found to be complex and nonlinear. Dimensionless scaling relationships were uncovered for a number of special cases. Results call into question the validity of the Boussinesq approximation in the context of modest-to-high background flow rates and the general applicability of numerical simulations without background flow.
Subsurface Cooling Rates and Microstructural Response during Laser Based Metal Additive Manufacturing
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Subsurface carbon dioxide and hydrogen storage for a sustainable energy future
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Dynamic structural evolution of MgO-supported palladium catalysts: from metal to metal oxide nanoparticles to surface then subsurface atomically dispersed cations
Supported noble metal catalysts, ubiquitous in chemical technology, often undergo dynamic transformations between reduced and oxidized states—which influence the metal nuclearities, oxidation states, and catalytic properties.
Probing thermal conductivity of subsurface, amorphous layers in irradiated diamond
In this study, we report on the thermal conductivity of amorphous carbon generated in diamond via nitrogen ion implantation (N 3+ at 16.5 MeV). Here, transmission electron microscopy techniques demonstrate amorphous band formation about the longitudinal projected range, localized approximately 7 μm beneath the sample surface. While high-frequency time-domain thermoreflectance measurements provide insight into the thermal properties of the near-surface preceding the longitudinal projected range depth, a complimentary technique, steady-state thermoreflectance, is used to probe the thermal conductivity at depths which could not otherwise be resolved. Through measurements with a series of focusing objective lenses for the laser spot size, we find the thermal conductivity of the amorphous region to be approximately 1.4 W m –1 K –1 , which is comparable to that measured for amorphous carbon films fabricated through other techniques.