Neutron double scatter imaging exploits the kinematics of neutron elastic scattering to enable emission imaging of neutron sources. Due to the relatively low coincidence detection efficiency of fast neutrons in organic scintillator arrays, imaging efficiency for double scatter cameras can also be low. One method to realize significant gains in neutron coincidence detection efficiency is to develop neutron double scatter detectors which employ monolithic blocks of organic scintillator, instrumented with photosensor arrays on multiple faces to enable 3D position and multi-interaction time pickoff. Silicon photomultipliers (SiPMs) have several advantageous characteristics for this approach, including high photon detection efficiency (PDE), good single photon time resolution (SPTR), high gain that translates to single photon counting capabilities, and ability to be tiled into large arrays with high packing fraction and photosensitive area fill factor. However, they also have a tradeoff in high uncorrelated and correlated noise rates (dark counts from thermionic emissions and optical photon crosstalk generated during avalanche) which may complicate event positioning algorithms. We have evaluated the noise characteristics and SPTR of Hamamatsu S13360-6075 SiPMs with low noise, fast electronic readout for integration into a monolithic neutron scatter camera prototype. The sensors and electronic readout were implemented in a small-scale prototype detector in order to estimate expected noise performance for a monolithic neutron scatter camera and perform proof-of-concept measurements for scintillation photon counting and three-dimensional event positioning.
Entangling gates in trapped-ion quantum computers are most often applied to stationary ions with initial motional distributions that are thermal and close to the ground state, while those demonstrations that involve transport generally use sympathetic cooling to reinitialize the motional state prior to applying a gate. Future systems with more ions, however, will face greater nonthermal excitation due to increased amounts of ion transport and exacerbated by longer operational times and variations over the trap array. In addition, pregate sympathetic cooling may be limited due to time costs and laser access constraints. In this paper, we analyze the impact of such coherent motional excitation on entangling-gate error by performing simulations of Mølmer-Sørenson (MS) gates on a pair of trapped-ion qubits with both thermal and coherent excitation present in a shared motional mode at the start of the gate. We quantify how a small amount of coherent displacement erodes gate performance in the presence of experimental noise, and we demonstrate that adjusting the relative phase between the initial coherent displacement and the displacement induced by the gate or using Walsh modulation can suppress this error. We then use experimental data from transported ions to analyze the impact of coherent displacement on MS-gate error under realistic conditions.
Niobium (Nb)-doped lead-tin-zirconate-titanate (PSZT) ceramics near the lead-zirconate-titanate 95/5 orthorhombic AFE-rhombohedral FE morphotropic phase boundary (PSZT 13.5/81/5.5 -1.6Nb) were prepared with up to 10 mol.% of hafnium (Hf) substituted for zirconium. The ceramics were prepared by a traditional solid-state synthesis route and sintered to near full density at 1150°C for 6 h in sealed alumina crucibles with self-same material as the lead vapor source. All compositions were ∼98% dense with no detectable secondary phases by X-ray diffraction. The grain size was ∼3 μm for all compositions, consisting of equiaxed grains with intergranular porosity. The compositions exhibited remnant polarization values of ∼32 μC/cm2. Depolarization by the hydrostatic pressure-induced FE-AFE phase transition occurred at 310 MPa for all compositions, resulting in a total depolarization output of 32.4 μC/cm2 for the PSZT ceramics. Evaluation of the R3c-R3m and R3m-Pm (Formula presented.) m phase transition temperatures by impedance spectroscopy showed temperatures on heating ranging from 86 to 92°C and 186 to 182°C, respectively, for increasing nominal Hf content. Thermal hysteresis of the phase transitions was also observed in the ceramics, with the transition temperature on cooling being 1–4°C lower. The study demonstrated that the PSZT ceramics are relatively insensitive to variations in Hf content in the range of 0 to 10 mol.%.
The capability of a 1-D PFLOTRAN model to simulate the S1-3 bentonite saturation experiment has been demonstrated and validated against experimental data. Work remains to be done to refine 1-D PFLOTRAN simulations of the experiment S1-4 which include evaluation of parameter sensitivities on the prediction of material saturation and relative permeabilities. This and further testing of PFLOTRAN capabilities will be done as part of DECOVALEX 2023 Task D contributions by the SNL team in the coming months.
Soil carbon can be divided into two categories: organic and inorganic. Soil inorganic carbon (SIC) is present in carbonate minerals in the soil and is often found in dry, arid regions. Examples of SIC include calcium carbonate (CaCO3) and magnesium carbonate (MgCO3), both of which play important roles in soil health. Soil organic carbon (SOC) is found in fresh plant matter (available SOC) and as humus or charcoal (inert SOC). Both types of carbon act as storage in the global carbon cycle. As a carbon sink, soil carbon has the potential to store carbon that would otherwise remain in the atmosphere as CO2, one of the primary greenhouse emissions. As such, soil is under increasing attention and research to be used as a sequestration (i.e., isolation) method to reduce the amount of carbon in the atmosphere. This type of carbon sequestration is called biological sequestration. SOC typically stores carbon for several decades (depending on decomposition rates) while SIC can store carbon for more than 70,000 years. Common sequestration techniques for SOC usually fall under the category of land management: planting perennials, keeping plant residue and composting, reducing tilling, and other agricultural practices that vary by region. SIC sequestration through carbonates naturally takes thousands of years but there have been studies to increase SIC sequestration through the addition of silicates.
This document provides a description of the model evaluation protocol (MEP) for pool fires, jet fires, and fireballs involving liquefied natural gas (LNG), refrigerant fluids, and byproducts at LNG facilities. The purpose of the MEP is to provide procedures regarding the assessment of a model's suitability to predict heat flux from fires. Three components, namely, a scientific assessment, model verification, and model validation comprise the MEP. The evaluation of a model satisfying these three components is to be documented in the form of a model evaluation report (MER). Discussion of models for the prediction of fire, detailed information on each of the three MEP components, the MEP procedure regarding new versions of previously approved models, and the format of the model evaluation report (MER) are provided.
Reducing the duration and frequency of blackouts in remote communities poses an engineering challenge for grid operators. Outage effects can also be mitigated locally through microgrids. This paper develops a systematic procedure to account for these challenges by creating microgrids prioritizing high value assets within vulnerable communities. Nighttime satellite imagery is used to identify vulnerable communities. Using an asset classification and rating system, multi-Asset clusters within these communities are prioritized. Infrastructure data, geographic information systems, satellite imagery, and spectral clustering are used to form and rank microgrid candidates. A microgrid sizing algorithm is included to guide through the microgrid design process. An application of the methodology is presented using real event, location, and asset data.
The MELCOR Accident Consequence Code System (MACCS) is used by Nuclear Regulatory Commission (NRC) and various national and international organizations for probabilistic consequence analysis of nuclear power accidents. This User Guide is intended to assist analysts in understanding the MACCS/WinMACCS model and to provide information regarding the code. This user guide version describes MACCS Version 4.0. Features that have been added to MACCS in subsequent versions are described in separate documentation. This User Guide provides a brief description of the model history, explains how to set up and execute a problem, and informs the user of the definition of various input parameters and any constraints placed on those parameters. This report is part of a series of reports documenting MACCS. Other reports include the MACCS Theory Manual, MACCS Verification Report, Technical Bases for Consequence Analyses Using MACCS, as well as documentation for preprocessor codes including SecPop, MelMACCS, and COMIDA2.
Voronin, Alexey; He, Yunhui; Maclachlan, Scott; Olson, Luke N.; Tuminaro, Raymond S.
A well-known strategy for building effective preconditioners for higher-order discretizations of some PDEs, such as Poisson's equation, is to leverage effective preconditioners for their low-order analogs. In this work, we show that high-quality preconditioners can also be derived for the Taylor–Hood discretization of the Stokes equations in much the same manner. In particular, we investigate the use of geometric multigrid based on the (Formula presented.) discretization of the Stokes operator as a preconditioner for the (Formula presented.) discretization of the Stokes system. We utilize local Fourier analysis to optimize the damping parameters for Vanka and Braess–Sarazin relaxation schemes and to achieve robust convergence. These results are then verified and compared against the measured multigrid performance. While geometric multigrid can be applied directly to the (Formula presented.) system, our ultimate motivation is to apply algebraic multigrid within solvers for (Formula presented.) systems via the (Formula presented.) discretization, which will be considered in a companion paper.
Neural networks are largely based on matrix computations. During forward inference, the most heavily used compute kernel is the matrix-vector multiplication (MVM): $W \vec{x} $. Inference is a first frontier for the deployment of next-generation hardware for neural network applications, as it is more readily deployed in edge devices, such as mobile devices or embedded processors with size, weight, and power constraints. Inference is also easier to implement in analog systems than training, which has more stringent device requirements. The main processing kernel used during inference is the MVM.
An existing shared risk framework designed for assessing and comparing threat-based risks to water utilities is being extended to incorporate electric power. An important differentiating characteristic of this framework is the use of a system-centric rather than an asset-centric approach. This approach allows anonymous sharing of results and enables comparison of assessments across different utilities within an infrastructure sector. By allowing utility owners to compare their assessments with others, they can improve their self-assessments and identification of "unknown unknowns". This document provides an approach for extension of the framework to electric power, including treatment of dependencies and interdependencies. The systems, threats, and mathematical description of associated risks used in a prototype framework are provided. The method is extensible so that additional infrastructure sectors can be incorporated. Preliminary results for a proof of concept calculation are provided.
This report contains a summary of our efforts to correlate head injury simulations predicting intracranial fluid cavitation with clinical assessments of brain injury from blunt impact to the head. Magnetic resonance imaging (MRI) data, collected on traumatic brain injury (TBI) subjects by researchers at the MIND Institute of New Mexico, was acquired for the current work. Specific blunt impact TBI case histories were selected from the TBI data for further study and possible correlation with simulation. Both group and single-subject case histories were examined. We found one single-subject case that was particularly suited for correlation with simulation. Diffusion tensor image (DTI) analysis of the TBI subject identified white matter regions within the brain displaying reductions in fractional anisotropy (FA), an indicator of local damage to the white matter axonal structures. Analysis of functional magnetic resonance image (fMRI) data collected on this individual identified localized regions of the brain displaying hypoactivity, another indicator of brain injury. We conducted high fidelity simulations of head impact experienced by the TBI subject using the Sandia head-neck-torso model and the shock physics computer code CTH. Intracranial fluid cavitation predictions were compared with maps of DTI fractional anisotropy and fMRI hypoactivity to assess whether a possible correlation exists. The ultimate goal of this work is to assess whether one can correlate simulation predictions of intracranial fluid cavitation with the brain injured sites identified by the fMRI and DTI analyses. The outcome of this effort is described in this report.
Consider a scalar field f : M → R, where M is a triangulated simplicial mesh in Rd. A level set, or contour, at value v is a connected component of f–1 (v). As v is changed, these contours change topology, merge into each other, or split. Contour trees are concise representations of f that track this contour behavior. The vertices of these trees are the critical points of f, where the gradient is zero. The edges represent changes in the topology of contours. It is a fundamental data structure in data analysis and visualization, and there is significant previous work (both theoretical and practical) on algorithms for constructing contour trees. Suppose M has n vertices, N facets, and t critical points. A classic result of Carr, Snoeyink, and Axen (2000) gives an algorithm that takes O(n log n+Nα(N)) time (where α(·) is the inverse Ackermann function). A further improvement to O(t log t + N) time was given by Chiang et al. All these algorithms involve a global sort of the critical points, a significant computational bottleneck. Unfortunately, lower bounds of Ω(t log t) also exist. We present the first algorithm that can avoid the global sort and has a refined time complexity that depends on the contour tree structure. Intuitively, if the tree is short and fat, we get significant improvements in running time. For a partition of the contour tree into a set of descending paths, P, our algorithm runs in O($\Sigma$pϵP |p| log |p| + tα(t) + N). This is at most O(t log D + N), where D is the diameter of the contour tree. Moreover, it is O(tα(t) + N) for balanced trees, a significant improvement over the previous complexity. Our algorithm requires numerous ideas: partitioning the contour tree into join and split trees, a local growing procedure to iteratively build contour trees, and the use of heavy path decompositions for the time complexity analysis. There is a crucial use of a family of binomial heaps to maintain priorities, ensuring that any comparison made is between comparable nodes of the contour tree. We also prove lower bounds showing that the $\Sigma$pϵP |p| log |p| complexity is inherent to computing contour trees.
This report begins with an evaluation of cladding degradation mechanisms deemed important to assessing barrier capability. Unlike similar efforts done in the past, this evaluation accounts for the hypothetical conditions associated with direct dual-purpose canister (DPC) disposal, including conditions resulting from a postulated, in-package, steady-state criticality event. A total of 16 cladding degradation mechanisms are examined assuming direct disposal of DPCs in two different hypothetical repositories: a saturated repository in shale and an unsaturated repository in alluvium.
In this paper, we propose a hybrid method that uses stochastic and deterministic search to compute the maximum likelihood estimator of a low-rank count tensor with Poisson loss via state-of-theart local methods. Our approach is inspired by Simulated Annealing for global optimization and allows for fine-grain parameter tuning as well as adaptive updates to algorithm parameters. We present numerical results that indicate our hybrid approach can compute better approximations to the maximum likelihood estimator with less computation than the state-of-the-art methods by themselves.
Biological monitoring logs for Swainson's and Red-tailed Hawk's nesting behaviors during construction at SNL. Logs also include information on other observed wildlife. Three different monitoring logs are present and include images and buffer map.
Aeroseismometery is a novel, cutting edge capability that involves balloon based systems for detecting and geolocating sources of infrasound. The incident infrasound from a range of sources such as volcanos, earthquakes, explosions, supersonic aircraft impinges upon the balloon system causing it to respond dynamically. The dynamic response is post-processed to locate the infrasound source. This report documents the derivation of an analytical model that predicts the balloon dynamics. Governing equations for the system are derived as well as a transfer function relating the infrasound signal to the net force on the balloon components. Experimental measurements of the infrasound signals are convolved with the transfer function and the governing equations numerically time integrated to obtain predictions of the displacement, velocity and acceleration of the balloon system. The predictions are compared to the experimental measurements with good agreement observed. The derivation focuses only on the vertical dynamics of the balloon system. Future work will develop governing equations for the swinging response of the balloon to the incident infrasound.
QOOH radicals are key intermediates in the chain of reactions leading to the autoignition of hydrocarbons and oxygenated organic compounds. They are thought to undergo two main reactions: OH elimination to form a cyclic ether and HO2 elimination to form an alkene. However, theoretical analysis of various substituted hydroperoxyalkyl radicals has found two new pathways: OH transfer and internal H abstraction assisted OH elimination. To determine the importance of these new pathways, their barrier heights for several substituted alkanes were calculated using various quantum chemical theories and compared to those of the well-known pathways. Several cases revealed possible competition with the well-known pathways. Rate coefficients were calculated for propyl systems but further studies will need to complete rate coefficients and branching fractions for all systems analyzed to understand these new reactions’ role in autoignition.
Electrode-scale heterogeneity can combine with complex electrochemical interactions to impede lithium-ion battery performance, particularly during fast charging. This study investigates the influence of electrode heterogeneity at different scales on the lithium-ion battery electrochemical performance under operational extremes. We employ image-based mesoscale simulation in conjunction with a three-dimensional electrochemical model to predict performance variability in 14 graphite electrode X-ray computed tomography data sets. Our analysis reveals that the tortuous anisotropy stemming from the variable particle morphology has a dominating influence on the overall cell performance. Cells with platelet morphology achieve lower capacity, higher heat generation rates, and severe plating under extreme fast charge conditions. On the contrary, the heterogeneity due to the active material clustering alone has minimal impact. Our work suggests that manufacturing electrodes with more homogeneous and isotropic particle morphology will improve electrochemical performance and improve safety, enabling electromobility.
Drinking water has and will continue to be at the foundation of our nation’s well-being and there is a growing interest in United States (US) drinking water quality. Nearly 30% of the United States population obtained their water from community water systems that did not meet federal regulations in 2019. Given the heavy interactions between society and drinking water quality, this study integrates social constructionism, environmental injustice, and sociohydrological systems to evaluate local awareness of drinking water quality issues. By employing text analytics, we explore potential drivers of regional water quality narratives within 25 local news sources across the United States. Specifically, we assess the relationship between printed local newspapers and water quality violations in communities as well as the influence of social, political, and economic factors on the coverage of drinking water quality issues. Results suggest that the volume and/or frequency of local drinking water violations is not directly reflected in local news coverage. Additionally, news coverage varied across sociodemographic features, with a negative relationship between Hispanic populations and news coverage of Lead and Copper Rule, and a positive relationship among non-Hispanic white populations. These findings extend current understanding of variations in local narratives to consider nuances of water quality issues and indicate opportunities for increasing equity in environmental risk communication.
An in situ counted ion implantation experiment improving the error on the number of ions required to form a single optically active silicon vacancy (SiV) defect in diamond 7-fold compared to timed implantation is presented. Traditional timed implantation relies on a beam current measurement followed by implantation with a preset pulse duration. It is dominated by Poisson statistics, resulting in large errors for low ion numbers. Instead, our in situ detection, measuring the ion number arriving at the substrate, results in a 2-fold improvement of the error on the ion number required to generate a single SiV compared to timed implantation. Through postimplantation analysis, the error is improved 7-fold compared to timed implantation. SiVs are detected by photoluminescence spectroscopy, and the yield of 2.98% is calculated through the photoluminescence count rate. Hanbury-Brown-Twiss interferometry is performed on locations potentially hosting single-photon emitters, confirming that 82% of the locations exhibit single photon emission statistics.
Hu, Jian Z.; Jaegers, Nicholas R.; Hahn, Nathan H.; Hu, Wenda; Han, Kee S.; Chen, Ying; Sears, Jesse A.; Murugesan, Vijayakumar; Zavadil, Kevin R.; Mueller, Karl T.
Efforts to expand the technological capability of batteries have generated increased interest in divalent cationic systems. Electrolytes used for these electrochemical applications often incorporate cyclic ethers as electrolyte solvents; however, the detailed solvation environments within such systems are not well-understood. To foster insights into the solvation structures of such electrolytes, Ca(TFSI)2and Zn(TFSI)2dissolved in tetrahydrofuran (THF) and 2-methyl-tetrahydrofuran were investigated through multi-nuclear magnetic resonance spectroscopy (17O, 43Ca, and 67Zn NMR) combined with quantum chemistry modeling of NMR chemical shifts. NMR provides spectroscopic fingerprints that readily couple with quantum chemistry to identify a set of most probable solvation structures based on the best agreement between the theoretically predicted and experimentally measured values of chemical shifts. The multi-nuclear approach significantly enhances confidence that the correct solvation structures are identified due to the required simultaneous agreement between theory and experiment for multiple nuclear spins. Furthermore, quantum chemistry modeling provides a comparison of the solvation cluster formation energetics, allowing further refinement of the preferred solvation structures. It is shown that a range of solvation structures coexist in most of these electrolytes, with significant molecular motion and dynamic exchange among the structures. This level of solvation diversity correlates with the solubility of the electrolyte, with Zn(TFSI)2/THF exhibiting the lowest degree of each. Comparisons of analogous Ca2+and Zn2+solvation structures reveal a significant cation size effect that is manifested in significantly reduced cation-solvent bond lengths and thus stronger solvent bonding for Zn2+relative to Ca2+. The strength of this bonding is further reduced by methylation of the cyclic ether ring. Solvation shells containing anions are energetically preferred in all the studied electrolytes, leading to significant quantities of contact ion pairs and consequently neutrally charged clusters. It is likely that the transport and interfacial de-solvation/re-solvation properties of these electrolytes are directed by these anion interactions. These insights into the detailed solvation structures, cation size, and solvent effects, including the molecular dynamics, are fundamentally important for the rational design of electrolytes in multivalent battery electrolyte systems.
Nuclear magnetic resonance (NMR) and nuclear quadrupole resonance (NQR) spectroscopy of bulk quantum materials have provided insight into phenomena, such as quantum phase criticality, magnetism, and superconductivity. With the emergence of nanoscale 2D materials with magnetic phenomena, inductively detected NMR and NQR spectroscopy are not sensitive enough to detect the smaller number of spins in nanomaterials. The nitrogen-vacancy (NV) center in diamond has shown promise in bringing the analytic power of NMR and NQR spectroscopy to the nanoscale. However, due to depth-dependent formation efficiency of the defect centers, noise from surface spins, band bending effects, and the depth dependence of the nuclear magnetic field, there is ambiguity regarding the ideal NV depth for surface NMR of statistically polarized spins. In this work, we prepared a range of shallow NV ensemble layer depths and determined the ideal NV depth by performing NMR spectroscopy on statistically polarized 19F in Fomblin oil on the diamond surface. We found that the measurement time needed to achieve a signal-to-noise ratio of 3 using XY8-N noise spectroscopy has a minimum at an NV ensemble depth of 5.5 ± 1.5 nm for ensembles activated from 100 ppm nitrogen concentration. To demonstrate the sensing capabilities of NV ensembles, we perform NQR spectroscopy on the 11B of hexagonal boron nitride flakes. We compare our best diamond to previous work with a single NV and find that this ensemble provides a shorter measurement time with excitation diameters as small as 4 μm. This analysis provides ideal conditions for further experiments involving NMR/NQR spectroscopy of 2D materials with magnetic properties.
Lithium-sulfur is a "beyond-Li-ion" battery chemistry attractive for its high energy density coupled with low-cost sulfur. Expanding to the MWh required for grid scale energy storage, however, requires a different approach for reasons of safety, scalability, and cost. Here we demonstrate the marriage of the redox-targeting scheme to the engineered Li solid electrolyte interphase (SEI), enabling a scalable, high efficiency, membrane-less Li-S redox flow battery. In this hybrid flow battery architecture, the Li anode is housed in the electrochemical cell, while the solid sulfur is safely kept in a separate catholyte reservoir and electrolyte is pumped over the sulfur and into the electrochemical cell. Electrochemically facile decamethylferrocene and cobaltocene are chosen as redox mediators to kick-start the initial reduction of solid S into soluble polysulfides and final reduction of polysulfides into solid Li2S, precluding the need for conductive carbons. On the anode side, a LiI and LiNO3pretreatment strategy encourages a stable SEI and lessens capacity fade, avoiding use of ion-selective separators. Complementary materials characterization confirms the uniform distribution of LiI in the SEI, while SEM confirms the presence of lower surface area globular Li deposition and UV-vis corroborates evolution of the polysulfide species. Equivalent areal loadings of up to 50 mgScm-2(84 mAh cm-2) are demonstrated, with high capacity and voltage efficiency at 1-2 mgScm-2(973 mAh gS-1and 81.3% VE in static cells and 1142 mAh gS-1and 86.9% VE in flow cells). These results imply that the fundamental Li-S chemistry and SEI engineering strategies can be adapted to the hybrid redox flow battery architecture, obviating the need for ion-selective membranes or flowing carbon additives, and offering a potential pathway for inexpensive, scalable, and safe MWh scale Li-S energy storage.
Aqueous aluminum (Al) batteries are posited to be a cheap and energy dense alternative to conventional Li-ion chemistries, but an aqueous electrolyte mediating trivalent aluminum cations (Al3+) warrants greater scrutiny. This study provides a rigorous examination of aqueous Al electrolytes, with the first compelling evidence for a dynamic octahedral solvation structure around the Al3+, without Al-OTf contact ion pairs, even at high concentrations. This solvation behavior and the concomitant, transient electrostatic hydrolysis of Al-OH2 ligands contrasts strongly with previously reported water-in-salt electrolytes, and occurs due to the high charge density of the Lewis acidic Al3+. Nuclear magnetic resonance spectroscopy and other physicochemical measurements quantitatively reveal how species activity evolves with concentration and temperature. This new understanding exposes practical concerns related to the corrosiveness of the acidic aqueous solutions, the degree of hydration of aluminum trifluoromethanesulfonate (Al(OTf)3) salt, and the grossly insufficient reductive stability of the proposed electrolytes (>1 V between HER onset and Al3+/Al). Collectively, these factors constitute multiple fundamental barriers to the feasibility of rechargeable aqueous Al batteries.
We demonstrate that stimulated microwave optical sideband generation using parametric frequency conversion can be utilized as a powerful technique for coherent state detection in atomic physics experiments. The technique has advantages over traditional absorption or polarization rotation-based measurements and enables the isolation of signal photons from probe photons. We outline a theoretical framework that accurately models sideband generation using a density matrix formalism. Using this technique, we demonstrate a novel intrinsic magnetic gradiometer that detects magnetic gradient fields between two spatially separated vapor cells by measuring the frequency of the beat note between sidebands generated within each cell. The sidebands are produced with high efficiency using parametric frequency conversion of a probe beam interacting with Rb87 atoms in a coherent superposition of magnetically sensitive hyperfine ground states. Interference between the sidebands generates a low-frequency beat note whose frequency is determined by the magnetic field gradient between the two vapor cells. In contrast to traditional gradiometers the intermediate step of measuring the magnetic field experienced by the two vapor cells is unnecessary. We show that this technique can be readily implemented in a practical device by demonstrating a compact magnetic gradiometer sensor head with a sensitivity of 25 fT/cm/Hz with a 4.4 cm baseline, while operating in a noisy laboratory environment unshielded from Earth's field.
Finite-temperature Kohn-Sham density functional theory (KS-DFT) is a widely-used method in warm dense matter (WDM) simulations and diagnostics. Unfortunately, full KS-DFT-molecular dynamics models scale unfavourably with temperature and there remains uncertainty regarding the performance of existing approximate exchange-correlation (XC) functionals under WDM conditions. Of particular concern is the expected explicit dependence of the XC functional on temperature, which is absent from most approximations. Average-atom (AA) models, which significantly reduce the computational cost of KS-DFT calculations, have therefore become an integral part of WDM modeling. In this paper, we present a derivation of a first-principles AA model from the fully-interacting many-body Hamiltonian, carefully analyzing the assumptions made and terms neglected in this reduction. We explore the impact of different choices within this model—such as boundary conditions and XC functionals—on common properties in WDM, for example equation-of-state data, ionization degree and the behavior of the frontier energy levels. Furthermore, drawing upon insights from ground-state KS-DFT, we discuss the likely sources of error in KS-AA models and possible strategies for mitigating such errors.
All-solid-state batteries are often assumed to be safer than conventional Li-ion ones. In this work, we present the first thermodynamic models to quantitatively evaluate solid-state and Li-ion battery heat release under several failure scenarios. The solid-state battery analysis is carried out with an Li7La3Zr2O12 solid electrolyte but can be extended to other configurations using the accompanying spreadsheet. We consider solid-state batteries that include a relatively small amount of liquid electrolyte, which is often added at the cathode to reduce interfacial resistance. While the addition of small amounts of liquid electrolyte increases heat release under specific failure scenarios, it may be small enough that other considerations, such as manufacturability and performance, are more important commercially. We show that short-circuited all-solid-state batteries can reach temperatures significantly higher than conventional Li-ion, which could lead to fire through flammable packaging and/or nearby materials. Our work highlights the need for quantitative safety analyses of solid-state batteries.
2,4,dimethyloxetane is an important cyclic ether intermediate that is produced from hydroperoxyalkyl (QOOH) radicals in the low-temperature combustion of n-pentane. However, the reaction mechanisms and rates of consumption pathways remain unclear. In the present work, the pressure- and temperature-dependent kinetics of seven cyclic ether peroxy radicals, which stem from 2,4,dimethyloxetane via H-abstraction and O2 addition, were determined. The automated kinetic workflow code, KinBot, was used to model the complexity of the chemistry in a stereochemically resolved manner and solve the resulting master equations from 300-1000 K and from 0.01-100 atm. The main conclusions from the calculations include (i) diastereomeric cyclic ether peroxy radicals show significantly different reactivities, (ii) the stereochemistry of the peroxy radical determines which QOOH isomerization steps are possible, (iii) conventional QOOH decomposition pathways, such as cyclic ether formation and HO2 elimination, compete with ring-opening reactions, which primarily produce OH radicals, the outcome of which is sensitive to stereochemistry. Ring-opening reactions lead to unique products, such as unsaturated, acyclic peroxy radicals, that form direct connections with species present in other chemical kinetics mechanisms through "cross-over" reactions that may complicate the interpretation of experimental results from combustion of n-pentane and, by extension, other alkanes. For example, one cross-over reaction involving 1-hydroperoxy-4-pentanone-2-yl produces 2-(hydroperoxymethyl)-3-butanone-1-yl, which is an iso-pentane-derived ketohydroperoxide (KHP). At atmospheric pressure, the rate of chemical reactions of all seven peroxy radicals compete with that of collisional stabilization, resulting in well-skipping reactions. However, at 100 atm, only one out of seven peroxy radicals undergoes significant well-skipping reactions. The rates produced from the master equation calculations provide the first foundation for the development of detailed sub-mechanisms for cyclic ether intermediates. In addition, analysis of the complex reaction mechanisms of 2,4-dimethyloxetane-derived peroxy radicals provides insights into the effects of stereoisomers on reaction pathways and product yields.
The goal of this project is to reconsider core market and reliability processes that can potentially yield to transformative advances in power grid security, reliability, and efficiency. Current electric power market designs are strongly a function of computing capabilities and limitations that were available in the mid-to-late 1990s, circa deregulation. This includes constructs such as: (1) a 2-tiered day-ahead/real-time market construct; and (2) linearized (“DC”) real power flow approximations in dispatch and pricing. At that time, state-of-the-art computational capabilities could at the limit address deterministic mixed-integer programming formulations of unit commitment (UC) and linear programming formulations of economic dispatch (ED) at limited fidelity and scale. Such constraints forced limited look-ahead time-horizons, crude approximations of AC power flow physics and operations, and artificial partitioning between day-ahead markets, hour(s)-ahead reliability processes, and real-time markets. Consequently, these limitations have resulted in limited security and reliability with increasing out-of-market payments, particularly as uncertainty associated with renewables and distributed energy resources grows.
This guide is meant to assist communities – from residents to energy experts to decision makers – in developing a conceptual microgrid design that meets site-specific energy resilience goals. Using the framework described in this guidebook, stakeholders can come together and start to quantify site-specific vulnerabilities, identify the most significant risks to delivery of electricity, and establish electric outage tolerances across the community. In addition to establishing minimum service needs, this framework encourages communities to consider broader sustainability goals and policy constraints and begin to estimate up-front costs associated with the installation of alternative microgrid solutions. The framework guides a community through data collection and a high-level assessment of its needs, constraints, and priorities, prior to engaging engineers, vendors, and contractors. The first sections of this guidebook provide a high-level primer on electric systems. The latter sections include guidance for step-by-step data gathering and analysis of site conditions. The ultimate product resulting from the stepwise approach is a conceptual microgrid design. A conceptual design is defined as an initial design (10%-20% complete) that considers the specific threats, needs, limitations, and investment options for a given location. Going through this exercise and developing the conceptual microgrid design as a community ensures the same community members who will ultimately live with the solution are the developers of its foundational design. Often, these are also the very same people who understand system tolerances and needs the best and are therefore the ideal candidates for establishing these criteria. Especially when it comes to evaluating critical infrastructure, it is the community that best understands the most critical services. The framework is intended to facilitate a systematic approach to planning for resilience and provide a deeper understanding of how to use a framework to make decisions around microgrid solutions. Like many processes where tradeoffs need to be considered, this is often an iterative process. If this guide serves to help educate and empower communities who are beginning the process of deploying a microgrid, it has met the goal of its authors.
High-pressure storage and cyclic (de)pressurization of hydrogen gas is known to result in degradation and failure of gas canisters, hoses, linings, and O-rings as the relatively small hydrogen molecule can readily permeate most materials. Hence, identifying material compositions that are less susceptible to hydrogen-induced damage is of significant importance to the hydrogen energy infrastructure. Here, we use classical atomistic molecular dynamics simulations to study hydrogen exposed ethylene-propylene-diene monomer (EPDM) rubber, an elastomer typically used in O-rings. We make chemical modifications to the model by adjusting the crosslink density and report on gas solubility, diffusivity, and molecular restructuring in response to rapid decompression. Our simulations indicate that increases in crosslink density can reduce volumetric expansion during decompression and result in smaller free volume pore sizes. However, these favorable properties for sealing materials come with a tradeoff. At pressure, crosslinks introduce extra free volume, providing potential sites for gas localization, the precursor to cavitation-induced failure.