Publications

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Direct air capture (DAC) of CO₂ is a negative emission technology under development to limit the impacts of climate change. The dilute concentration of CO₂ in the atmosphere (~400 ppm) requires new materials for carbon capture with increased CO₂ selectivity that is not met with current carbon capture materials. Porous liquids (PLs) are an emerging candidate for carbon capture and consists of a combination of solvents and porous hosts that creates a liquid with permanent porosity. The fundamental mechanisms of carbon capture in a PL are relatively unknown. To uncover these mechanisms, PLs were synthesized consisting of three different zeolitic-imidazolate framework (ZIF-8, ZIF-67, or ZIF-69) porous host in a water/glycol/2-methylimidazole solvent. The most stable composition was based on ZIF-8 and exhibited carbon capture following exposure to CO₂. Density functional theory identified a three-step carbon capture mechanism based on (i) reaction of OH- with ethylene glycol in the solution followed by (ii) formation of 2-hydroxyethyl carbonate, which (iii) further react with OH- to form a carbonate species. This mechanism was validated with experimental nuclear magnetic resonance spectroscopy (NMR) to identify the dissolved carbonate phases and the decrease in the pH during CO₂ exposure. Deuterated samples of the ZIF-8 PLs were synthesized and analyzed via neutron diffraction at the Spallation Neutron Sources at Oak Ridge National Laboratory. Results identified differences in diffraction for PLs pre- and post-CO₂ exposure that will be combined with ab initio molecular dynamics data of the same PL composition to identify how the presence of a solvent-porous host interfaces results in carbon capture.

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Poster presentation at Keystone Symposia on BBB penetrating nanobody project.

We evaluate neural radiance fields (NeRFs) as a method for reconstructing 3D volumetric scenes from low Earth orbit satellite imagery. We leverage commercial satellite data to reconstruct a scene using existing software tools. In doing so, we identify difficulties in these mapping datasets for NeRF generation. We propose potential applications in geospatial intelligence for context and improved image interpretation.

Neural ordinary differential equations (NODEs) have recently regained popularity as large-depth limits of a large class of neural networks. In particular, residual neural networks (ResNets) are equivalent to an explicit Euler discretization of an underlying NODE, where the transition from one layer to the next is one time step of the discretization. The relationship between continuous and discrete neural networks has been of particular interest. Notably, analysis from the ordinary differential equation viewpoint can potentially lead to new insights for understanding the behavior of neural networks in general. In this work, we take inspiration from differential equations to define the concept of stiffness for a ResNet via the interpretation of a ResNet as the discretization of a NODE. Here, we then examine the effects of stiffness on the ability of a ResNet to generalize, via computational studies on example problems coming from climate and chemistry models. We find that penalizing stiffness does have a unique regularizing effect, but we see no benefit to penalizing stiffness over L² regularization (penalization of network parameter norms) in terms of predictive performance.

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This report identifies current best understanding of federal agencies that are responsible for the safe transportation and handling of nuclear materials during various phases of space launch activities and how they interact. It explores the following questions: (1) Which federal agencies have roles, responsibilities, and statutory authorities related to the launch, orbit, and reentry of nuclear materials and components? (2) What relevant current/recent activities are those federal agencies involved in?

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There are several U.S. government-sponsored programs with significant experience engaging with foreign government and industry partners to support capacity-building in export controls. This work seeks to answer the question: How can the outreach experience of the U.S. government-sponsored export control capacity-building programs (ECCBP) inform best practices for engaging with advanced reactor vendors in the domain of international nuclear safeguards? To answer this question, we interviewed export control subject matter experts with experience working for the U.S. ECCBPs – the Bureau of Industry and Security (BIS), the Export Control and Related Border Security (EXBS) program, and the International Nonproliferation Export Control Program (INECP) – and developed a set of recommendations for industry engagement based on the collective experience of interviewees.

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Albany is a parallel C++ finite element library for solving forward and inverse problems involving partial differential equations (PDEs). In this paper we introduce PyAlbany, a newly developed Python interface to the Albany library. PyAlbany can be used to effectively drive Albany enabling fast and easy analysis and post-processing of applications based on PDEs that are pre-implemented in Albany. PyAlbany relies on the library PyBind11 to bind Python with C++ Albany code. Here we detail the implementation of PyAlbany and showcase its capabilities through a number of examples targeting a heat-diffusion problem. In particular we consider the following: (1) the generation of samples for a Monte Carlo application, (2) a scalability study, (3) a study of parameters on the performance of a linear solver, and finally (4) a tool for performing eigenvalue decompositions of matrix-free operators for a Bayesian inference application.

Several studies have proven how ducted fuel injection (DFI) reduces soot emissions for compression-ignition engines. Nevertheless, no comprehensive study has investigated how DFI performs over a load range in combination with low-net-carbon fuels. In this study, optical-engine experiments were performed with four different fuels—conventional diesel and three low-net-carbon fuels—at low and moderate load, to measure emissions levels and performance. The 1.7-liter single-cylinder optical engine was equipped with a high-speed camera to capture natural luminosity images of the combustion event. Conventional diesel and DFI combustion were investigated at four different dilution levels (to simulate exhaust-gas recirculation effects), from 14 to 21 mol% oxygen in the intake. At a given dilution level, with commercial diesel fuel, DFI reduced soot by 82% at medium load, and 75% at low load without increasing NO_x. The results further show how DFI with dilution reduces soot and NO_x without compromising engine performance or other emission types, especially when combined with low-net-carbon fuels. DFI with the oxygenated low-net-carbon blend HEA67 simultaneously reduced soot and NO_x by as much as 93 % and 82 %, respectively, relative to conventional diesel combustion with commercial diesel fuel. These soot and NO_x reductions occurred while lifecycle CO₂ was reduced by at least 70 % when using low-net-carbon fuels instead of conventional diesel. All emissions changes were compared with future emissions regulations for different vehicle sectors to investigate how DFI can be used to facilitate achievement of the regulations. Finally, the results show how the DFI cases fall below several future emissions regulation levels, rendering less need for aftertreatment systems and giving a possible lower cost of ownership.

Here, we introduce a mathematically rigorous formulation for a nonlocal interface problem with jumps and propose an asymptotically compatible finite element discretization for the weak form of the interface problem. After proving the well-posedness of the weak form, we demonstrate that solutions to the nonlocal interface problem converge to the corresponding local counterpart when the nonlocal data are appropriately prescribed. Several numerical tests in one and two dimensions show the applicability of our technique, its numerical convergence to exact nonlocal solutions, its convergence to the local limit when the horizons vanish, and its robustness with respect to the patch test.

Many applications require minimizing the sum of smooth and nonsmooth functions. For example, basis pursuit denoising problems in data science require minimizing a measure of data misfit plus an $\ell^1$-regularizer. Similar problems arise in the optimal control of partial differential equations (PDEs) when sparsity of the control is desired. Here, we develop a novel trust-region method to minimize the sum of a smooth nonconvex function and a nonsmooth convex function. Our method is unique in that it permits and systematically controls the use of inexact objective function and derivative evaluations. When using a quadratic Taylor model for the trust-region subproblem, our algorithm is an inexact, matrix-free proximal Newton-type method that permits indefinite Hessians. We prove global convergence of our method in Hilbert space and demonstrate its efficacy on three examples from data science and PDE-constrained optimization.

Here we present a new method for coupled linear elasticity problems whose finite element discretization may lead to spatially non-coincident discretized interfaces. Our approach combines the classical Dirichlet–Neumann coupling formulation with a new set of discretized interface conditions obtained through Taylor series expansions. We show that these conditions ensure linear consistency of the coupled finite element solution. We then formulate an iterative solution method for the coupled discrete system and apply the new coupling approach to two representative settings for which we also provide several numerical illustrations. The first setting is a mesh-tying problem in which both coupled structures have the same Lamé parameters whereas the second setting is an interface problem for which the Lamé parameters in the two coupled structures are different.

Satellite imagery can detect temporary cloud trails or ship tracks formed from aerosols emitted from large ships traversing our oceans, a phenomenon that global climate models cannot directly reproduce. Ship tracks are observable examples of marine cloud brightening, a potential solar climate intervention that shows promise in helping combat climate change. In this paper, we demonstrate a simulation-based approach in learning the behavior of ship tracks based upon a novel stochastic emulation mechanism. Our method uses wind fields to determine the movement of aerosol-cloud tracks and uses a stochastic partial differential equation (SPDE) to model their persistence behavior. This SPDE incorporates both a drift and diffusion term which describes the movement of aerosol particles via wind and their diffusivity through the atmosphere, respectively. We first present our proposed approach with examples using simulated wind fields and ship paths. We then successfully demonstrate our tool by applying the approximate Bayesian computation method-sequential Monte Carlo for data assimilation.

Interfacial segregation and chemical short-range ordering influence the behavior of grain boundaries in complex concentrated alloys. In this study, we use atomistic modeling of a NbMoTaW refractory complex concentrated alloy to provide insight into the interplay between these two phenomena. Hybrid Monte Carlo and molecular dynamics simulations are performed on columnar grain models to identify equilibrium grain boundary structures. Our results reveal extended near-boundary segregation zones that are much larger than traditional segregation regions, which also exhibit chemical patterning that bridges the interfacial and grain interior regions. Furthermore, structural transitions pertaining to an A2-to-B2 transformation are observed within these extended segregation zones. Both grain size and temperature are found to significantly alter the widths of these regions. An analysis of chemical short-range order indicates that not all pairwise elemental interactions are affected by the presence of a grain boundary equally, as only a subset of elemental clustering types are more likely to reside near certain boundaries. The results emphasize the increased chemical complexity that is associated with near-boundary segregation zones and demonstrate the unique nature of interfacial segregation in complex concentrated alloys.

Automatic differentiation (AD) is a well-known technique for evaluating analytic derivatives of calculations implemented on a computer, with numerous software tools available for incorporating AD technology into complex applications. However, a growing challenge for AD is the efficient differentiation of parallel computations implemented on emerging manycore computing architectures such as multicore CPUs, GPUs, and accelerators as these devices become more pervasive. In this work, we explore forward mode, operator overloading-based differentiation of C++ codes on these architectures using the widely available Sacado AD software package. In particular, we leverage Kokkos, a C++ tool providing APIs for implementing parallel computations that is portable to a wide variety of emerging architectures. We describe the challenges that arise when differentiating code for these architectures using Kokkos, and two approaches for overcoming them that ensure optimal memory access patterns as well as expose additional dimensions of fine-grained parallelism in the derivative calculation. We describe the results of several computational experiments that demonstrate the performance of the approach on a few contemporary CPU and GPU architectures. We then conclude with applications of these techniques to the simulation of discretized systems of partial differential equations.

Li-metal batteries (LMBs) employing conversion cathode materials (e.g., FeF₃) are a promising way to prepare inexpensive, environmentally friendly batteries with high energy density. Pseudo-solid-state ionogel separators harness the energy density and safety advantages of solid-state LMBs, while alleviating key drawbacks (e.g., poor ionic conductivity and high interfacial resistance). In this work, a pseudo-solid-state conversion battery (Li-FeF₃) is presented that achieves stable, high rate (1.0 mA cm^–2) cycling at room temperature. The batteries described herein contain gel-infiltrated FeF₃ cathodes prepared by exchanging the ionic liquid in a polymer ionogel with a localized high-concentration electrolyte (LHCE). The LHCE gel merges the benefits of a flexible separator (e.g., adaptation to conversion-related volume changes) with the excellent chemical stability and high ionic conductivity (~2 mS cm^–1 at 25 °C) of an LHCE. The latter property is in contrast to previous solid-state iron fluoride batteries, where poor ionic conductivities necessitated elevated temperatures to realize practical power levels. Importantly, the stable, room-temperature Li-FeF₃ cycling performance obtained with the LHCE gel at high current densities paves the way for exploring a range of architectures including flexible, three-dimensional, and custom shape batteries.

Wind turbine applications that leverage nacelle-mounted Doppler lidar are hampered by several sources of uncertainty in the lidar measurement, affecting both bias and random errors. Two problems encountered especially for nacelle-mounted lidar are solid interference due to intersection of the line of sight with solid objects behind, within, or in front of the measurement volume and spectral noise due primarily to limited photon capture. These two uncertainties, especially that due to solid interference, can be reduced with high-fidelity retrieval techniques (i.e., including both quality assurance/quality control and subsequent parameter estimation). Our work compares three such techniques, including conventional thresholding, advanced filtering, and a novel application of supervised machine learning with ensemble neural networks, based on their ability to reduce uncertainty introduced by the two observed nonideal spectral features while keeping data availability high. The approach leverages data from a field experiment involving a continuous-wave (CW) SpinnerLidar from the Technical University of Denmark (DTU) that provided scans of a wide range of flows both unwaked and waked by a field turbine. Independent measurements from an adjacent meteorological tower within the sampling volume permit experimental validation of the instantaneous velocity uncertainty remaining after retrieval that stems from solid interference and strong spectral noise, which is a validation that has not been performed previously. All three methods perform similarly for non-interfered returns, but the advanced filtering and machine learning techniques perform better when solid interference is present, which allows them to produce overall standard deviations of error between 0.2 and 0.3ms-1, or a 1%-22% improvement versus the conventional thresholding technique, over the rotor height for the unwaked cases. Between the two improved techniques, the advanced filtering produces 3.5% higher overall data availability, while the machine learning offers a faster runtime (i.e., 1/41s to evaluate) that is therefore more commensurate with the requirements of real-time turbine control. The retrieval techniques are described in terms of application to CW lidar, though they are also relevant to pulsed lidar. Previous work by the authors (Brown and Herges, 2020) explored a novel attempt to quantify uncertainty in the output of a high-fidelity lidar retrieval technique using simulated lidar returns; this article provides true uncertainty quantification versus independent measurement and does so for three techniques rather than one.

It is hoped that quantum computers will offer advantages over classical computers for combinatorial optimization. Here, we introduce a feedback-based strategy for quantum optimization, where the results of qubit measurements are used to constructively assign values to quantum circuit parameters. We show that this procedure results in an estimate of the combinatorial optimization problem solution that improves monotonically with the depth of the quantum circuit. Importantly, the measurement-based feedback enables approximate solutions to the combinatorial optimization problem without the need for any classical optimization effort, as would be required for the quantum approximate optimization algorithm. We demonstrate this feedback-based protocol on a superconducting quantum processor for the graph-partitioning problem MaxCut, and present a series of numerical analyses that further investigate the protocol's performance.

This report is the revised (Revision 9) Task F specification for DECOVALEX-2023. Task F is a comparison of the models and methods used in deep geologic repository performance assessment. The task proposes to develop a reference case for a mined repository in a fractured crystalline host rock (Task F1) and a reference case for a mined repository in a salt formation (Task F2). Teams may choose to participate in the comparison for either or both reference cases. For each reference case, a common set of conceptual models and parameters describing features, events, and processes that impact performance will be given, and teams will be responsible for determining how best to implement and couple the models. The comparison will be conducted in stages, beginning with a comparison of key outputs of individual process models, followed by a comparison of a single deterministic simulation of the full reference case, and moving on to uncertainty propagation and uncertainty and sensitivity analysis. This report provides background information, a summary of the proposed reference cases, and a staged plan for the analysis.

As atomic layer deposition (ALD) emerges as a method to fabricate architectures with atomic precision, emphasis is placed on understanding surface reactions and nucleation mechanisms. ALD of titanium dioxide with TiCl₄ and water has been used to investigate deposition processes in general, but the effect of surface termination on the initial TiO₂ nucleation lacks needed mechanistic insights. Here, this work examines the adsorption of TiCl₄ on Cl–, H–, and HO– terminated Si(100) and Si(111) surfaces to elucidate the general role of different surface structures and defect types in manipulating surface reactivity of growth and non-growth substrates. The surface sites and their role in the initial stages of deposition are examined by X-ray photoelectron spectroscopy (XPS) and atomic force microscopy (AFM). Density functional theory (DFT) computations of the local functionalized silicon surfaces suggest oxygen-containing defects are primary drivers of selectivity loss on these surfaces.

Ionic conduction in silicate glasses is mainly influenced by the nature, concentration, and mobility of the network-modifying (NWM) cations. The electrical conduction in SLS is dominated by the ionic migration of sodium moving from the anode to the cathode. An activation energy for this conduction process was calculated to be 0.82eV and in good agreement with values previously reported. The conduction process associated to the leakage current and relaxation peak in TSDC for HPFS is attributed to conduction between nonbridging oxygen hole centers (NBOHC). It is suggested that ≡Si-OH = ≡Si-O^- + H⁰ under thermo-electric poling, promoting hole or proton injection from the anode and responsible for the 1.5eV relaxation peak. No previous TSDC data have been found to corroborate this mechanism. The higher activation energy and lower current intensity for the coated HPFS might be attributed to a lower concentration of NBOHC after heat treatment (Si-OH + OH-Si = SiO-Si + H₂O). This could explain the TSDC signal around room temperature for the coated HPFS. Another possible explanation could be a redox reaction at the anode region dominating the current response.

This development of empirical data to support realistic and science-based input to safety regulations and transportation standards is a critical need for the hazardous material (HM) transportation industry. Current regulations and standards are based on the TNT equivalency model. However, real world experience indicates that use of the TNT equivalency model to predict composite overwrapped pressure vessel (COPV) potential energy release is unrealistically conservative. The purpose of this report is to characterize and quantify rupture events involving damaged COPV’s of the type used in HM transportation regulated by the Department of Transportation (DOT). This was accomplished using a series of five tests; 2 COPV tests for compressed natural gas (CNG), 2 COPV tests for hydrogen, and 1 COPV test for nitrogen. Measured overpressures from these tests were compared to predicted overpressures from a TNT equivalence model and blast curves. Comparison between the measurements and predictions shows that the predictions are generally conservative, and that the extent of conservatism is dominated by predictions of the chemical contribution to overpressure from fuel within the COPVs.

Cemented annulus fractures are a major leakage path in a wellbore system, and their permeability plays an important role in the behavior of fluid flow through a leaky wellbore. The permeability of these fractures is affected by changing conditions including the external stresses acting on the fracture and the fluid pressure within the fracture. Laboratory gas flow experiments were conducted in a triaxial cell to evaluate the permeability of a wellbore cement fracture under a wide range of confining stress and pore pressure conditions. For the first time, an effective stress law that considers the simultaneous effect of confining stress and pore pressure was defined for the wellbore cement fracture permeability. Here the results showed that the effective stress coefficient (λ) for permeability increased linearly with the Terzaghi effective stress ( -p) with an average of λ = 1 in the range of applied pressures. The relationship between the effective stress and fracture permeability was examined using two physical-based models widely used for rock fractures. The results from the experimental work were incorporated into numerical simulations to estimate the impact of effective stress on the interpreted hydraulic aperture and leakage behavior through a fractured annular cement. Accounting for effective stress-dependent permeability through the wellbore length significantly increased the leakage rate at the wellhead compared with the assumption of a constant cemented annulus permeability.

This report provides a summary of measurement results used to compare the performance of the PHDS Fulcrum40h and Ortec Detective-X High Purity Germanium (HPGe) detector systems. Specifically, the measurement data collected was used to assess each detector system for gamma efficiency and resolution, gamma angular response and efficiency for an in-situ surface distribution, neutron efficiency, gamma pulse-pileup response, and gamma to neutron crosstalk.

Terahertz (THz) external-cavity lasers based on quantum-cascade (QC) metasurfaces are emerging as widely-tunable, single-mode sources with the potential to cover the 1--6 THz range in discrete bands with milliwatt-level output power. By operating on an ultra-short cavity with a length on the order of the wavelength, the QC vertical-external-cavity surface-emitting-laser (VECSEL) architecture enables continuous, broadband tuning while producing high quality beam patterns and scalable power output. The methods and challenges for designing the metasurface at different frequencies are discussed. As the QC-VECSEL is scaled below 2 THz, the primary challenges are reduced gain from the QC active region, increased metasurface quality factor and its effect on tunable bandwidth, and larger power consumption due to a correspondingly scaled metasurface area. At frequencies above 4.5 THz, challenges arise from a reduced metasurface quality factor and the excess absorption that occurs from proximity to the Reststrahlen band. The results of four different devices — with center frequencies 1.8 THz, 2.8 THz, 3.5 THz, and 4.5 THz — are reported. Each device demonstrated at least 200 GHz of continuous single-mode tuning, with the largest being 650 GHz around 3.5 THz. The limitations of the tuning range are well modeled by a Fabry-Pérot cavity which accounts for the reflection phase of the metasurface and the effect of the metasurface quality factor on laser threshold. Lastly, the effect of different output couplers on device performance is studied, demonstrating a significant trade-off between the slope efficiency and tuning bandwidth.

National security applications require artificial neural networks (ANNs) that consume less power, are fast and dynamic online learners, are fault tolerant, and can learn from unlabeled and imbalanced data. We explore whether two fundamentally different, traditional learning algorithms from artificial intelligence and the biological brain can be merged. We tackle this problem from two directions. First, we start from a theoretical point of view and show that the spike time dependent plasticity (STDP) learning curve observed in biological networks can be derived using the mathematical framework of backpropagation through time. Second, we show that transmission delays, as observed in biological networks, improve the ability of spiking networks to perform classification when trained using a backpropagation of error (BP) method. These results provide evidence that STDP could be compatible with a BP learning rule. Combining these learning algorithms will likely lead to networks more capable of meeting our national security missions.

Several studies suggest that metal ordering within metal-organic frameworks (MOFs) is important for understanding how MOFs behave in relevant applications; however, these siting trends can be difficult to determine experimentally. To garner insight into the energetic driving forces that may lead to nonrandom ordering within heterometallic MOFs, we employ density functional theory (DFT) calculations on several bimetallic metal-organic crystals composed of Nd and Yb metal atoms. We also investigate the metal siting trends for a newly synthesized MOF. Our DFT-based energy of mixing results suggest that Nd will likely occupy sites with greater access to electronegative atoms and that local homometallic domains within a mixed-metal Nd-Yb system are favored. We also explore the use of less computationally extensive methods such as classical force fields and cluster expansion models to understand their feasibility for large system sizes. This study highlights the impact of metal ordering on the energetic stability of heterometallic MOFs and crystal structures.

We have measured, analyzed, and simulated the ground state valence photoelectron spectrum, x-ray absorption (XA) spectrum, x-ray photoelectron (XP) spectrum as well as normal and resonant Auger-Meitner electron (AE) spectrum of oxazole at the carbon, oxygen, and nitrogen K-edge in order to understand its electronic structure. Experimental data are compared to theoretical calculations performed at the coupled cluster, restricted active space perturbation theory to second-order and time-dependent density functional levels of theory. We demonstrate (1) that both N and O K-edge XA spectra are sensitive to the amount of dynamical electron correlation included in the theoretical description and (2) that for a complete description of XP spectra, additional orbital correlation and orbital relaxation effects need to be considered. The normal AE spectra are dominated by a singlet excitation channel and well described by theory. The resonant AE spectra, however, are more complicated. While the participator decay channels, dominating at higher kinetic energies, are well described by coupled cluster theory, spectator channels can only be described satisfactorily using a method that combines restricted active space perturbation theory to second order for the bound part and a one-center approximation for the continuum.

In order to meet 2025 goals for enhanced peak power (100 kW), specific power (50 kW/L), and reduced cost (3.3 $\$$/kW) in a motor that can operate at ≥ 20,000 rpm, improved soft magnetic materials must be developed. Better performing soft magnetic materials will also enable rare earth free electric motors. In fact, replacement of permanent magnets with soft magnetic materials was highlighted in the Electrical and Electronics Technical Team (EETT) Roadmap as a R&D pathway for meeting 2025 targets. Eddy current losses in conventional soft magnetic materials, such as silicon steel, begin to significantly impact motor efficiency as rotational speed increases. Soft magnetic composites (SMCs), which combine magnetic particles with an insulating matrix to boost electrical resistivity (ρ) and decrease eddy current losses, even at higher operating frequencies (or rotational speeds), are an attractive solution. Today, SMCs are being fabricated with values of ρ ranging between 10^-3 to 10^-1 μohm∙m, which is significantly higher than 3% silicon steel (~0.05 μohm∙m). The isotropic nature of SMCs is ideally suited for motors with 3D flux paths, such as axial flux motors. Additionally, the manufacturing cost of SMCs is low and they are highly amenable to advanced manufacturing and net-shaping into complex geometries, which further reduces manufacturing costs. There is still significant room for advancement in SMCs, and therefore additional improvements in electrical machine performance. For example, despite the inclusion of a non-magnetic insulating material, the electrical resistivities of SMCs are still far below that of soft ferrites (10 – 10⁸ μohm∙m).

A laser-strike detection system includes an imaging sensor mounted on a platform, and a computing device. The imaging sensor outputs image frames that are each representative of a portion of the platform at a different time, during which a laser may be striking the platform. The computing device receives the image frames, and computes a delay map that indicates time-of-arrival delays of the laser beam at points on the portion of the platform. The computing device converts the delay map to a path-length variation map by multiplying the delay map by the propagation speed of light. The computing device fits a plane to the path-length variation map constrained by a topological model of the platform. The computing device computes angular deflections in x- and y-directions based upon the fit, which angular deflections define a direction from the platform to an emitter of the laser beam.

More efficient power conversion devices are able to transmit greater electrical power across larger distances to satisfy growing global electrical needs. A critical requirement to achieve more efficient power conversion are the soft magnetic materials used as core materials in transformers, inductors, and motors. To that effect it is well known that the use of non-equilibrium microstructures, which are, for example, nanocrystalline or consist of single phase solid solutions, can yield high saturation magnetic polarization and high electrical resistivity necessary for more efficient soft magnetic materials. In this work, we synthesized CoFe – P soft magnetic alloys containing nanocrystalline, single phase solid solution microstructures and studied the effect of a secondary intermetallic phase on the saturation magnetic polarization and electrical resistivity of the consolidated alloy. Single phase solid solution CoFe – P alloys were prepared through mechanically alloying metal powders and phase decomposition was observed after subsequent consolidation via spark plasma sintering (SPS) at various temperatures. The secondary intermetallic phase was identified as the orthorhombic (CoxFe1−x)2P phase and the magnetic properties of the (CoxFe1−x)2P intermetallic phase were found to be detrimental to the soft magnetic properties of the targeted CoFe – P alloy.

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Heavy metals released from kerogen to produced water during oil/gas extraction have caused major enviromental concerns. To curtail water usage and production in an operation and to use the same process for carbon sequestration, supercritical CO2 (scCO2) has been suggested as a fracking fluid or an oil/gas recovery agent. It has been shown previously that injection of scCO2 into a reservoir may cause several chemical and physical changes to the reservoir properties including pore surface wettability, gas sorption capacity, and transport properties. Using molecular dynamics simulations, we here demonstrate that injection of scCO2 might lead to desorption of physically adsorbed metals from kerogen structures. This process on one hand may impact the quality of produced water. On the other hand, it may enhance metal recovery if this process is used for in-situ extraction of critical metals from shale or other organic carbon-rich formations such as coal.

In this article, we present a general methodology to combine the Discontinuous Petrov–Galerkin (DPG) method in space and time in the context of methods of lines for transient advection–reaction problems. We first introduce a semidiscretization in space with a DPG method redefining the ideas of optimal testing and practicality of the method in this context. Then, we apply the recently developed DPG-based time-marching scheme, which is of exponential-type, to the resulting system of Ordinary Differential Equations (ODEs). We also discuss how to efficiently compute the action of the exponential of the matrix coming from the space semidiscretization without assembling the full matrix. Finally, we verify the proposed method for 1D+time advection–reaction problems showing optimal convergence rates for smooth solutions and more stable results for linear conservation laws comparing to the classical exponential integrators.

Abstract: Due to a beneficial balance of computational cost and accuracy, real-time time-dependent density-functional theory has emerged as a promising first-principles framework to describe electron real-time dynamics. Here we discuss recent implementations around this approach, in particular in the context of complex, extended systems. Results include an analysis of the computational cost associated with numerical propagation and when using absorbing boundary conditions. We extensively explore the shortcomings for describing electron–electron scattering in real time and compare to many-body perturbation theory. Modern improvements of the description of exchange and correlation are reviewed. In this work, we specifically focus on the Qb@ll code, which we have mainly used for these types of simulations over the last years, and we conclude by pointing to further progress needed going forward. Graphical abstract: [Figure not available: see fulltext.].

Numerical algorithms for stiff stochastic differential equations are developed using linear approximations of the fast diffusion processes, under the assumption of decoupling between fast and slow processes. Three numerical schemes are proposed, all of which are based on the linearized formulation albeit with different degrees of approximation. The schemes are of comparable complexity to the classical explicit Euler-Maruyama scheme but can achieve better accuracy at larger time steps in stiff systems. Convergence analysis is conducted for one of the schemes, that shows it to have a strong convergence order of 1/2 and a weak convergence order of 1. Approximations arriving at the other two schemes are discussed. Numerical experiments are carried out to examine the convergence of the schemes proposed on model problems.

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Clays are known for their small particle sizes and complex layer stacking. We show here that the limited dimension of clay particles arises from the lack of long-range order in low-dimensional systems. Because of its weak interlayer interaction, a clay mineral can be treated as two separate low-dimensional systems: a 2D system for individual phyllosilicate layers and a quasi-1D system for layer stacking. The layer stacking or ordering in an interstratified clay can be described by a 1D Ising model while the limited extension of individual phyllosilicate layers can be related to a 2D Berezinskii–Kosterlitz–Thouless transition. This treatment allows for a systematic prediction of clay particle size distributions and layer stacking as controlled by the physical and chemical conditions for mineral growth and transformation. Clay minerals provide a useful model system for studying a transition from a 1D to 3D system in crystal growth and for a nanoscale structural manipulation of a general type of layered materials.

Direct disposal of dual-purpose canisters (DPC) has been proposed to streamline the disposal of spent nuclear fuel. However, there are scenarios where direct disposal of DPCs may result in temperatures in excess of the specified upper temperature limits for some engineered barrier system (EBS) materials, which may cause alteration within EBS materials dependent on local conditions such as host rock composition, chemistry of the saturating groundwaters, and interactions between barrier materials themselves. Here we report the results of hydrothermal experiments reacting EBS materials—bentonite buffer and steel—with an analogue crystalline host rock and groundwater at 250 °C. Experiment series explored the effect of reaction time on the final products and the effects of the mineral and fluid reactants on different steel types. Post-mortem X-ray diffraction, electron microprobe, and scanning electron microscopy analyses showed characteristic alteration of both bentonite and steel, including the formation of secondary zeolite and calcium silicate hydrate minerals within the bentonite matrix and the formation of iron-bearing clays and metal oxides at the steel surfaces. Swelling clays in the bentonite matrix were not quantitatively altered to non-swelling clay species by the hydrothermal conditions. The combined results of the solution chemistry over time and post-mortem mineralogy suggest that EBS alteration is more sensitive to initial groundwater chemistry than the presence of host rock, where limited potassium concentration in the solution prohibits conversion of the smectite minerals in the bentonite matrix to non-swelling clay species.

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We report the method-of-moments implementation of the electric-field integral equation (EFIE) yields many code-verification challenges due to the various sources of numerical error and their possible interactions. Matters are further complicated by singular integrals, which arise from the presence of a Green's function. To address these singular integrals, an approach is presented in wherein both the solution and Green's function are manufactured. Because the arising equations are poorly conditioned, they are reformulated as a set of constraints for an optimization problem that selects the solution closest to the manufactured solution. In this paper, we demonstrate how, for such practically singular systems of equations, computing the truncation error by inserting the exact solution into the discretized equations cannot detect certain orders of coding errors. On the other hand, the discretization error from the optimal solution is a more sensitive metric that can detect orders less than those of the expected convergence rate.

Carbon fiber composites offer superior mechanical performance compared to nearly all other useful materials for the design of structures. However, for cost-driven industries, such as with the wind energy and vehicle industries, the cost of commercial carbon fiber materials is often prohibitive for their usage compared to alternatives. This paper develops an approach to optimize fiber geometries for use in carbon fiber reinforced polymers to increase the compressive strength per unit cost. Compressive strength is a composite property that depends on the fiber, matrix, and interface, and an exact analytic expression does not exist that can accurately represent these complicated relationships. The approach taken instead is to use a weighted summation between the fiber cross-sectional area moment of inertia and perimeter as a proxy for compressive strength, with different weightings explored within the paper. Analyses are performed to identify optimal fiber geometries that increase the cost-specific compressive strength based on various assumptions and desired fiber volume fraction. Robust optimal shapes are identified which outperform circular fibers due to increases in area moment of inertia and perimeter, as well as decreases in carbon fiber processing costs.

Understanding the adsorption of isolated metal cations from water on to mineral surfaces is critical for toxic waste retention and cleanup in the environment. Heterogeneous nucleation of metal oxyhydroxides and other minerals on material surfaces is key to crystal growth and dissolution. The link connecting these two areas, namely cation dimerization and polymerization, is far less understood. In this work we apply ab initio molecular dynamics calculations to examine the coordination structure of hydroxide-bridged Cu(II) dimers, and the free energy changes associated with Cu(II) dimerization on silica surfaces. The dimer dissociation pathway involves sequential breaking of two Cu2+-OH− bonds, yielding three local minima in the free energy profiles associated with 0-2 OH− bridges between the metal cations, and requires the design of a (to our knowledge) novel reaction coordinate for the simulations. Cu(II) adsorbed on silica surfaces are found to exhibit stronger tendency towards dimerization than when residing in water. Cluster-plus-implicit-solvent methods yield incorrect trends if OH− hydration is not correctly depicted. The predicted free energy landscapes are consistent with fast equilibrium times (seconds) among adsorbed structures, and favor Cu2+ dimer formation on silica surfaces over monomer adsorption.

The effect of crystallography on transgranular chloride-induced stress corrosion cracking (TGCISCC) of arc welded 304L austenitic stainless steel is studied on >300 grains along crack paths. Schmid and Taylor factor mismatches across grain boundaries (GBs) reveal that cracks propagate either from a hard to soft grain, which can be explained merely by mechanical arguments, or soft to hard grain. In the latter case, finite element analysis reveals that TGCISCC will arrest at GBs without sufficient mechanical stress, favorable crystallographic orientations, or crack tip corrosion. GB type does not play a significant role in determining TGCISCC cracking behavior nor susceptibility. TGCISCC crack behaviors at GBs are discussed in the context of the competition between mechanical, crystallographic, and corrosion factors.

We study the problem of designing a distributed observer for an LTI system over a time-varying communication graph. The limited existing work on this topic imposes various restrictions either on the observation model or on the sequence of communication graphs. In contrast, we propose a single-time-scale distributed observer that works under mild assumptions. Specifically, our communication model only requires strong-connectivity to be preserved over nonoverlapping, contiguous intervals that are even allowed to grow unbounded over time. We show that under suitable conditions that bound the growth of such intervals, joint observability is sufficient to track the state of any discrete-time LTI system exponentially fast, at any desired rate. We also develop a variant of our algorithm that is provably robust to worst-case adversarial attacks, provided the sequence of graphs is sufficiently connected over time. The key to our approach is the notion of a 'freshness-index' that keeps track of the age-of-information being diffused across the network. Such indices enable nodes to reject stale estimates of the state, and, in turn, contribute to stability of the error dynamics.

Migration of seismic events to deeper depths along basement faults over time has been observed in the wastewater injection sites, which can be correlated spatially and temporally to the propagation or retardation of pressure fronts and corresponding poroelastic response to given operation history. The seismicity rate model has been suggested as a physical indicator for the potential of earthquake nucleation along faults by quantifying poroelastic response to multiple well operations. Our field-scale model indicates that migrating patterns of 2015–2018 seismicity observed near Venus, TX are likely attributed to spatio-temporal evolution of Coulomb stressing rate constrained by the fault permeability. Even after reducing injection volumes since 2015, pore pressure continues to diffuse and steady transfer of elastic energy to the deep fault zone increases stressing rate consistently that can induce more frequent earthquakes at large distance scales. Sensitivity tests with variation in fault permeability show that (1) slow diffusion along a low-permeability fault limits earthquake nucleation near the injection interval or (2) rapid relaxation of pressure buildup within a high-permeability fault, caused by reducing injection volumes, may mitigate the seismic potential promptly.

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Advances in machine learning (ML) have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost, parallel efficiency of empirical potentials. However, ML-based potentials struggle to achieve transferability, i.e., provide consistent accuracy across configurations that differ from those used during training. In order to realize the promise of ML-based potentials, systematic and scalable approaches to generate diverse training sets need to be developed. This work creates a diverse training set for tungsten in an automated manner using an entropy optimization approach. Subsequently, multiple polynomial and neural network potentials are trained on the entropy-optimized dataset. A corresponding set of potentials are trained on an expert-curated dataset for tungsten for comparison. The models trained to the entropy-optimized data exhibited superior transferability compared to the expert-curated models. Furthermore, the models trained to the expert-curated set exhibited a significant decrease in performance when evaluated on out-of-sample configurations.

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker-Planck equation. We discuss how to calibrate the drift and diffusion terms of the Fokker-Planck equation from the theoretical and computational perspectives. The calibrated Fokker-Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

We show that piezoelectric strain actuation of acoustomechanical interactions can produce large phase velocity changes in an existing quantum phononic platform: aluminum nitride on suspended silicon. Using finite element analysis, we demonstrate a piezo-acoustomechanical phase shifter waveguide capable of producing ±π phase shifts for GHz frequency phonons in 10s of μm with 10s of volts applied. Then, using the phase shifter as a building block, we demonstrate several phononic integrated circuit elements useful for quantum information processing. In particular, we show how to construct programmable multi-mode interferometers for linear phononic processing and a dynamically reconfigurable phononic memory that can switch between an ultra-long-lifetime state and a state strongly coupled to its bus waveguide. From the master equation for the full open quantum system of the reconfigurable phononic memory, we show that it is possible to perform read and write operations with over 90% quantum state transfer fidelity for an exponentially decaying pulse.

Shuttling ions at high speed and with low motional excitation is essential for realizing fast and high-fidelity algorithms in many trapped-ion-based quantum computing architectures. Achieving such performance is challenging due to the sensitivity of an ion to electric fields and the unknown and imperfect environmental and control variables that create them. Here we implement a closed-loop optimization of the voltage waveforms that control the trajectory and axial frequency of an ion during transport in order to minimize the final motional excitation. The resulting waveforms realize fast round-trip transport of a trapped ion across multiple electrodes at speeds of 0.5 electrodes per microsecond (35 m·s−1 for a one-way transport of 210 μm in 6 μs) with a maximum of 0.36 ± 0.08 mean quanta gain. This sub-quanta gain is independent of the phase of the secular motion at the distal location, obviating the need for an electric field impulse or time delay to eliminate the coherent motion.

Although gold remains a preferred surface finish for components used in high-reliability electronics, rapid developments in this area have left a gap in the fundamental understanding of solder joint gold (Au) embrittlement. Furthermore, as electronic designs scale down in size, the effect of Au content is not well understood on increasingly smaller solder interconnections. As a result, previous findings may have limited applicability. The current study focused on addressing these gaps by investigating the interfacial microstructure that evolves in 63Sn-37Pb solder joints as a function of Au layer thickness. Those findings were correlated to the mechanical performance of the solder joints. Increasing the initial Au concentration decreased the mechanical strength of a joint, but only to a limited degree. Kirkendall voids were the primary contributor to low-strength joints, while brittle fracture within the intermetallic compounds (IMC) layers is less of a factor. The Au embrittlement mechanism appears to be self-limiting, but only once mechanical integrity is degraded. Sufficient void evolution prevents continued diffusion from the remaining Au.

The extreme miniaturization of a cold-atom interferometer accelerometer requires the development of novel technologies and architectures for the interferometer subsystems. Here, we describe several component technologies and a laser system architecture to enable a path to such miniaturization. We developed a custom, compact titanium vacuum package containing a microfabricated grating chip for a tetrahedral grating magneto-optical trap (GMOT) using a single cooling beam. In addition, we designed a multi-channel photonic-integrated-circuit-compatible laser system implemented with a single seed laser and single sideband modulators in a time-multiplexed manner, reducing the number of optical channels connected to the sensor head. In a compact sensor head containing the vacuum package, sub-Doppler cooling in the GMOT produces 15 μK temperatures, and the GMOT can operate at a 20 Hz data rate. We validated the atomic coherence with Ramsey interferometry using microwave spectroscopy, then demonstrated a light-pulse atom interferometer in a gravimeter configuration for a 10 Hz measurement data rate and T = 0–4.5 ms interrogation time, resulting in Δg/g = 2.0 × 10−6. This work represents a significant step towards deployable cold-atom inertial sensors under large amplitude motional dynamics.

Hydrogen is an attractive option for energy storage because it can be produced from renewable sources and produces environmentally benign byproducts. However, the volumetric energy density of molecular hydrogen at ambient conditions is low compared to other storage methods like batteries, so it must be compressed to attain a viable energy density for applications such as transportation. Nanoporous materials have attracted significant interest for gas storage because they can attain high storage density at lower pressure than conventional compression. In this work, we examine how to improve the cryogenic hydrogen storage capacity of a series of porous aromatic frameworks (PAFs) by controlling the pore size and increasing the surface area by adding functional groups. We also explore tradeoffs in gravimetric and volumetric measures of the hydrogen storage capacity and the effects of temperature swings using grand canonical Monte Carlo simulations. We also consider the effects of adding functional groups to the metal–organic framework NU-1000 to improve its hydrogen storage capacity. We find that highly flexible alkane chains do not improve the hydrogen storage capacity in NU-1000 because they do not extend into the pores; however, rigid chains containing alkyne groups do increase the surface area and hydrogen storage capacity. Finally, we demonstrate that the deliverable capacity of hydrogen in NU-1000 can be increased from 40.0 to 45.3 g/L (at storage conditions of 100 bar and 77 K and desorption conditions of 5 bar and 160 K) by adding long, rigid alkyne chains into the pores.

Soil organic carbon (SOC) changes under future climate warming are difficult to quantify in situ. Here we apply an innovative approach combining space-for-time substitution with meta-analysis to SOC measurements in 113,013 soil profiles across the globe to estimate the effect of future climate warming on steady-state SOC stocks. We find that SOC stock will reduce by 6.0 ± 1.6% (mean±95% confidence interval), 4.8 ± 2.3% and 1.3 ± 4.0% at 0–0.3, 0.3–1 and 1–2 m soil depths, respectively, under 1 °C air warming, with additional 4.2%, 2.2% and 1.4% losses per every additional 1 °C warming, respectively. The largest proportional SOC losses occur in boreal forests. Existing SOC level is the predominant determinant of the spatial variability of SOC changes with higher percentage losses in SOC-rich soils. Our work demonstrates that warming induces more proportional SOC losses in topsoil than in subsoil, particularly from high-latitudinal SOC-rich systems.

This study explores the effect of surface re-finishing on the corrosion behavior of electron beam manufactured (EBM) Ti-G5 (Ti-6Al-4V), including the novel application of an electron beam surface remelting (EBSR) technique. Specifically, the relationship between material surface roughness and corrosion resistance was examined. Surface roughness was tested in the as-printed (AP), mechanically polished (MP), and EBSR states and compared to wrought (WR) counterparts. Electrochemical measurements were performed in chloride-containing media. It was observed that surface roughness, rather than differences in the underlying microstructure, played a more significant role in the general corrosion resistance in the environment explored here. While both MP and EBSR methods reduced surface roughness and enhanced corrosion resistance, mechanical polishing has many known limitations. The EBSR process explored herein demonstrated positive preliminary results. The surface roughness (Ra) of the EBM-AP material was considerably reduced by 82%. Additionally, the measured corrosion current density in 0.6 M NaCl for the EBSR sample is 0.05 µA cm−2, five times less than the value obtained for the EBM-AP specimen (0.26 µA cm−2).

Engineering arrays of active optical centers to control the interaction Hamiltonian between light and matter has been the subject of intense research recently. Collective interaction of atomic arrays with optical photons can give rise to directionally enhanced absorption or emission, which enables engineering of broadband and strong atom-photon interfaces. Here, we report on the observation of long-range cooperative resonances in an array of rare-earth ions controllably implanted into a solid-state lithium niobate micro-ring resonator. We show that cooperative effects can be observed in an ordered ion array extended far beyond the light’s wavelength. We observe enhanced emission from both cavity-induced Purcell enhancement and array-induced collective resonances at cryogenic temperatures. Engineering collective resonances as a paradigm for enhanced light-matter interactions can enable suppression of free-space spontaneous emission. The multi-functionality of lithium niobate hosting rare-earth ions can open possibilities of quantum photonic device engineering for scalable and multiplexed quantum networks.

Rock, concrete, and other engineered materials are often composed of several minerals that change volumetrically in response to variations in the moisture content of the local environment. Such differential shrinkage is caused by varying shrinkage rates between mineral compositions during dehydration. Using both 3D X-ray imaging of geo-architected samples and peridynamic (PD) numerical simulations, we show that the spatial distribution of the clay affects the crack network geometry with distributed clay particles yielding the most complex crack networks and percent damage (99.56%), along with a 60% reduction in material strength. We also demonstrate that crack formation, growth, coalescence, and distribution during dehydration, are controlled by the differential shrinkage rates between a highly shrinkable clay and a homogeneous mortar matrix. Sensitivity tests performed with the PD models show a clay shrinkage parameter of 0.4 yields considerable damage, and reductions in the parameter can result in a significant reduction in fracturing and an increase in material strength. Additionally, isolated clay inclusions induced localized fracturing predominantly due to debonding between the clay and matrix. These insights indicate differential shrinkage is a source of potential failure in natural and engineered barriers used to sequester anthropogenic waste.

Kinetic gas dynamics in rarefied and moderate-density regimes have complex behavior associated with collisional processes. These processes are generally defined by convolution integrals over a high-dimensional space (as in the Boltzmann operator), or require evaluating complex auxiliary variables (as in Rosenbluth potentials in Fokker-Planck operators) that are challenging to implement and computationally expensive to evaluate. In this work, we develop a data-driven neural network model that augments a simple and inexpensive BGK collision operator with a machine-learned correction term, which improves the fidelity of the simple operator with a small overhead to overall runtime. The composite collision operator has a tunable fidelity and, in this work, is trained using and tested against a direct-simulation Monte-Carlo (DSMC) collision operator.

Hydrogen produced through low-temperature water electrolysis using anion exchange membranes (AEM) combines the benefits of liquid-electrolyte alkaline electrolysis and solid-polymer proton exchange membrane electrolysis. The anion conductive ionomers in the oxygen-producing anode and hydrogen-producing cathode are a critical part of the three-dimensional electrodes. The ionomer in the hydrogen-producing cathode facilitates hydroxide ion conduction from the cathode catalyst to the anode catalyst, and water transport from the anode to the cathode catalyst through the AEM. This ionomer also binds the catalyst particles to the porous transport layer. In this study, the cathode durability was improved by use of a self-adhesive cathode ionomer to chemically bond the cathode catalyst particles to the porous transport layer. It was found that the cathode ionomers with high ion exchange capacity (IEC) were more effective than low IEC ionomers because of the need to transport water to the cathode catalyst and transport hydroxide away from the cathode. The cathode durability was improved by using ionomers which were soluble in the spray-coated cathode ink. Optimization of the catalyst and ionomer content within the cathode led to electrolysis cells which were both mechanically durable and operated at low voltage.

The interaction of an intense laser with a solid foil target can drive ∼ TV/m electric fields, accelerating ions to MeV energies. In this study, we experimentally observe that structured targets can dramatically enhance proton acceleration in the target normal sheath acceleration regime. At the Texas Petawatt Laser facility, we compared proton acceleration from a 1μm flat Ag foil, to a fixed microtube structure 3D printed on the front side of the same foil type. A pulse length (140–450 fs) and intensity ((4–10) × 10 20 W/cm2) study found an optimum laser configuration (140 fs, 4 × 10 20 W/cm2), in which microtube targets increase the proton cutoff energy by 50% and the yield of highly energetic protons (> 10 MeV) by a factor of 8×. When the laser intensity reaches 10 21 W/cm2, the prepulse shutters the microtubes with an overcritical plasma, damping their performance. 2D particle-in-cell simulations are performed, with and without the preplasma profile imported, to better understand the coupling of laser energy to the microtube targets. The simulations are in qualitative agreement with the experimental results, and show that the prepulse is necessary to account for when the laser intensity is sufficiently high.

The growing demand for bandwidth makes photonic systems a leading candidate for future telecommunication and radar technologies. Integrated photonic systems offer ultra-wideband performance within a small footprint, which can naturally interface with fiber-optic networks for signal transmission. However, it remains challenging to realize narrowband (∼MHz) filters needed for high-performance communications systems using integrated photonics. In this paper, we demonstrate all-silicon microwave-photonic notch filters with 50× higher spectral resolution than previously realized in silicon photonics. This enhanced performance is achieved by utilizing optomechanical interactions to access long-lived phonons, greatly extending available coherence times in silicon. We use a multi-port Brillouin-based optomechanical system to demonstrate ultra-narrowband (2.7 MHz) notch filters with high rejection (57 dB) and frequency tunability over a wide spectral band (6 GHz) within a microwave-photonic link. We accomplish this with an all-silicon waveguide system, using CMOS-compatible fabrication techniques.

Spin–orbit effects, inherent to electrons confined in quantum dots at a silicon heterointerface, provide a means to control electron spin qubits without the added complexity of on-chip, nanofabricated micromagnets or nearby coplanar striplines. Here, we demonstrate a singlet–triplet qubit operating mode that can drive qubit evolution at frequencies in excess of 200 MHz. This approach offers a means to electrically turn on and off fast control, while providing high logic gate orthogonality and long qubit dephasing times. We utilize this operational mode for dynamical decoupling experiments to probe the charge noise power spectrum in a silicon metal-oxide-semiconductor double quantum dot. In addition, we assess qubit frequency drift over longer timescales to capture low-frequency noise. We present the charge noise power spectral density up to 3 MHz, which exhibits a 1/fα dependence consistent with α ~ 0.7, over 9 orders of magnitude in noise frequency.

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Picuris Pueblo is a small tribal community in Northern New Mexico consisting of about 306 members and 86 homes. Picuris Pueblo has made advances with renewable energy implementation, including the installation of a 1 megawatt photovoltaic (PV) array. This array has provided the tribe with economic and other benefits that contribute toward the tribe's goal of tribal sovereignty. The tribe is seeking to implement more PV generation as well as battery energy storage systems. Picuris Pueblo is considering different implementation methods, including the formation of a microgrid system. This report studies the potential implementation of a PV and battery storage microgrid system and the associated benefits and challenges. The benefits of a microgrid system include cost savings, increased resiliency, and increased tribal sovereignty and align with the tribe's goals of becoming energy independent and lowering the cost of electricity.

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Scribe3D© is an application designed to allow users to develop risk threat scenarios on a 3D image of a facility/location of the users choosing. Once the facility/terrain is loaded, entities such as personnel, vehicles (ground and air), and structures/objects can be added. Then the user can make the entities move around the facility/terrain. Responder and Adversary forces can be outfitted with weapons and move from place to place on foot or in vehicles. In Scribe3D©, once the entities are identified, they can be moved to visualize realistic scenario situations by using a suite of tools to enhance decision making and scenario development. This manual can guide you through setting up and editing all aspects of Scribe3D© and guide you to the creation of a Scenario Exercise.

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This report updates the Regional Disruption Economic Impact Model (RDEIM) GDP-based model described in Bixler et al. (2020) used in the MACCS accident consequence analysis code. MACCS is the U.S. Nuclear Regulatory Commission (NRC) used to perform probabilistic health and economic consequence assessments for atmospheric releases of radionuclides. It is also used by international organizations, both reactor owners and regulators. It is intended and most commonly used for hypothetical accidents that could potentially occur in the future rather than to evaluate past accidents or to provide emergency response during an ongoing accident. It is designed to support probabilistic risk and consequence analyses and is used by the NRC, U.S. nuclear licensees, the Department of Energy, and international vendors, licensees, and regulators. The update of the RDEIM model in version 4.2 expresses the national recovery calculation explicitly, rather than implicitly as in the previous version. The calculation of the total national GDP losses remains unchanged. However, anticipated gains from recovery are now allocated across all the GDP loss types – direct, indirect, and induced – whereas in version 4.1, all recovery gains were accounted for in the indirect loss type. To achieve this, we’ve introduced new methodology to streamline and simplify the calculation of all types of losses and recovery. In addition, RDEIM includes other kinds of losses, including tangible wealth. This includes loss of tangible assets (e.g., depreciation) and accident expenditures (e.g., decontamination). This document describes the updated RDEIM economic model and provides examples of loss and recovery calculation, results analysis, and presentation. Changes to the tangible cost calculation and accident expenditures are described in section 2.2. The updates to the RDEIM input-output (I-O) model are not expected to affect the final benchmark results Bixler et al. (2020), as the RDEIM calculation for the total national GDP losses remains unchanged. The reader is referred to the MACCS revision history for other cost modelling changes since version 4.0 that may affect the benchmark. RDEIM has its roots in a code developed by Sandia National Laboratories for the Department of Homeland Security to estimate short-term losses from natural and manmade accidents, called the Regional Economic Accounting analysis tool (REAcct). This model was adapted and modified for MACCS. It is based on I-O theory, which is widely used in economic modeling. It accounts for direct losses to a disrupted region affected by an accident, indirect losses to the national economy due to disruption of the supply chain, and induced losses from reduced spending by displaced workers. RDEIM differs from REAcct in in its treatment and estimation of indirect loss multipliers, elimination of double-counting associated with inter-industry trade in the affected area, and that it is intended to be used for extended periods that can occur from a major nuclear reactor accident, such as the one that occurred at the Fukushima Daiichi site in Japan. Most input-output models do not account for economic adaptation and recovery, and in this regard RDEIM differs from its parent, REAcct, because it allows for a user-definable national recovery period. Implementation of a recovery period was one of several recommendations made by an independent peer review panel to ensure that RDEIM is state-of-practice. For this and several other reasons, RDEIM differs from REAcct.

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Mobile sources is a term most commonly used to describe radioactive sources that are used in applications requiring frequent transportation. Such radioactive sources are in common use world-wide where typical applications include radiographic non-destructive evaluation (NDE) and oil and gas well logging, among others requiring lesser amounts of radioactivity. This report provides a general overview of mobile sources used for well logging and industrial radiography applications including radionuclides used, equipment, and alternative technologies. Information presented here has been extracted from a larger study on common mobile radiation sources and their use.

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The Waste Isolation Pilot Plant (WIPP) is an underground facility designed to safely dispose of radioactive waste. The WIPP uses many heavy vehicles to transport materials and equipment underground. Most of these vehicles are powered by traditional internal combustion engines (ICE) with diesel fuel. Recently, electric vehicles (EVs) powered with batteries have been used at the WIPP. EVs have very low operational and maintenance costs, not considering battery replacements, and they have zero emissions during operation. This absence of emissions makes them ideal for underground facilities with limited ventilation. Even if a facility has robust ventilation normally, ventilation systems can break down leading to restrictions in ICE powered operations. Figure 1 shows a rendering of the WIPP.

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In July 2022, Sandia National Laboratories hosted a workshop in Washington, D.C., bringing together representatives from eleven Federal Government agencies, responsible for public health, environmental security, and biodefense, as well as six Department of Energy (DOE) National Laboratories, to discuss how to work together to address climate-driven zoonotic disease risk. The primary goal of this workshop was to provide a forum for Federal and DOE National Lab attendees to share their missions, programs, and capabilities relevant to zoonotic disease emergence, to discuss how to best leverage these collective resources, identify key gaps, and to determine an effective path forward.

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This white paper describes the program vision, objectives, and R&D targets in 5 to 10 years for the Department of Energy (DOE) Office of Electricity (OE) Microgrid R&D Program. The vision is to facilitate the nation’s transitions to (1) a more resilient and reliable, (2) more decarbonized electricity infrastructure, in which (3) microgrids have a reduced cost to implement. This strategy is developed in the context that the United States’ electricity delivery system is becoming more distributed in nature. The electricity generation capacity in 10 years may be 30-50% distributed energy assets.

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The Strategic Petroleum Reserve (SPR) is the world's largest supply of emergency crude oil. The reserve consists of four sites in Louisiana and Texas. Each site stores crude in deep, underground salt caverns. It is the mission of the SPR's Enhanced Monitoring Program to examine all available data to inform our understanding of each site. This report discusses the monitoring data, processes, and results for each of the four sites for fiscal year 2022.

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Zyvex Labs has created several p-n junction devices with a variety of gaps between the boron and phosphorus electrodes, from 0-7.7 nm, which are now being measured. We have developed a different contacting process based on palladium disilicide rather than aluminium to improve the reliability of the device contacts. Preliminary measurements indicate that these new contacts are successfully contacting the buried dopant layers, which are intact after the overgrowth process. Modelling of the p-n junction properties has made good progress, with the model matching previous published data, and modelling of n-p-n junction devices has begun. This now awaits experimental validation.

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Enclosed are the results of analyses for samples received by the laboratory on 11/08/22.

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The FY22 Proxy App Suite Release milestone includes the following activities: Curate a collection of proxy applications that represents the breadth of ECP applications, including application domains, programming models, supporting libraries, numerical methods, etc. Identify gaps in coverage and work with application teams to commission or develop proxies to cover gaps. From within this collection, designate the ”ECP Proxy Application Suite” of 10–15 proxies that balance breadth of coverage with ease of use and quality of implementation. Also designate approximately 6–10 proxies to form the “ECP Machine Learning Proxy Suite”. The ML suite will represent algorithms, use cases, and programming methods typically used by ECP science workloads to incorporate machine learning into their workflows.

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As part of the project “Designing Resilient Communities (DRC): A Consequence-Based Approach for Grid Investment,” funded by the United States (US) Department of Energy’s (DOE) Grid Modernization Laboratory Consortium (GMLC), Sandia National Laboratories (Sandia) is partnering with a variety of government, industry, and university participants to develop and test a framework for community resilience planning focused on modernization of the electric grid. This report provides a summary of the section of the project focused on hardware demonstration of “resilience nodes” concept.

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In support of the DOE Office of Science, Sandia National Labs is one of nine national laboratories which oversees the Atmospheric Radiation Measurement (ARM) Program. The Sandia ARM team has an obligation to fulfill its mission to provide the nation with data to improve the understanding of climate processes and the representation of those processes in climate models. Sandia’s ARM team’s ability to provide detailed and accurate descriptions of the Earth’s atmosphere in diverse climate regimes assists the DOE in development of sustainable solutions to the nation’s energy and environmental challenges. Sandia Labs manages ARM atmospheric facilities along the North Slope of Alaska (NSA) at Utqiagvik as well as the Tethered Balloon System (TBS). Activities conducted at NSA and with the TBS aide in data collection for the ARM data archive. An overview of these activities for the month of December follows.

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This report provides recommendations to improve the assessment method of the Federal Radiological Monitoring and Assessment Center (FRMAC) for the ingestion of crops contaminated with radionuclides. The current FRMAC method of calculating investigation levels (ILs) and crop derived response levels (DRLs) is detailed. Recommended modifications to these calculations are presented based on the following aspects: handling radionuclide mixtures, no immediate equilibrium, washing of contaminated crops, and updated dietary intake rates.

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