Projection-based reduced-order models (pROMs) show great promise as a means to accelerate many-query applications such as forward error propagation, solving inverse problems, and design optimization. In order to deploy pROMs in the context of high-consequence decision making, accurate error estimates are required to determine the region(s) of applicability in the parameter space. The following paper considers the dual-weighted residual (DWR) error estimate for pROMs and compares it to another promising pROM error estimate, machine learned error models (MLEM). In this paper, we show how DWR can be applied to ROMs and then evaluate DWR on two partial differential equations (PDEs): a two-dimensional linear convection–reaction–diffusion equation, and a three-dimensional static hyper-elastic beam. It is shown that DWR is able to estimate errors for pROMs extrapolating outside of their training set while MLEM is best suited for pROMs used to interpolate within the pROM training set.
Characterizing explosion sources and differentiating between earthquake and underground explosions using distributed seismic networks becomes non-trivial when explosions are detonated in cavities or heterogeneous ground material. Moreover, there is little understanding of how changes in subsurface physical properties affect the far-field waveforms we record and use to infer information about the source. Simulations of underground explosions and the resultant ground motions can be a powerful tool to systematically explore how different subsurface properties affect far-field waveform features, but there are added variables that arise from how we choose to model the explosions that can confound interpretation. To assess how both subsurface properties and algorithmic choices affect the seismic wavefield and the estimated source functions, we ran a series of 2-D axisymmetric non-linear numerical explosion experiments and wave propagation simulations that explore a wide array of parameters. We then inverted the synthetic far-field waveform data using a linear inversion scheme to estimate source–time functions (STFs) for each simulation case. We applied principal component analysis (PCA), an unsupervised machine learning method, to both the far-field waveforms and STFs to identify the most important factors that control variance in the waveform data and differences between cases. For the far-field waveforms, the largest variance occurs in the shallower radial receiver channels in the 0–50 Hz frequency band. For the STFs, both peak amplitude and rise times across different frequencies contribute to the variance. We find that the ground equation of state (i.e. lithology and rheology) and the explosion emplacement conditions (i.e. tamped versus cavity) have the greatest effect on the variance of the far-field waveforms and STFs, with the ground yield strength and fracture pressure being secondary factors. Differences in the PCA results between the far-field waveforms and STFs could possibly be due to near-field non-linearities of the source that are not accounted for in the estimation of STFs and could be associated with yield strength, fracture pressure, cavity radius and cavity shape parameters. Other algorithmic parameters are found to be less important and cause less variance in both the far-field waveforms and STFs, meaning algorithmic choices in how we model explosions are less important, which is encouraging for the further use of explosion simulations to study how physical Earth properties affect seismic waveform features and estimated STFs.
Interval Assignment (IA) is the problem of selecting the number of mesh edges (intervals) for each curve for conforming quad and hex meshing. The intervals x is fundamentally integer-valued. Many other approaches perform numerical optimization then convert a floating-point solution into an integer solution, which is slow and error prone. We avoid such steps: we start integer, and stay integer. Incremental Interval Assignment (IIA) uses integer linear algebra (Hermite normal form) to find an initial solution to the meshing constraints, satisfying the integer matrix equation Ax=b. Solving for reduced row echelon form provides integer vectors spanning the nullspace of A. We add vectors from the nullspace to improve the initial solution, maintaining Ax=b. Heuristics find good integer linear combinations of nullspace vectors that provide strict improvement towards variable bounds or goals. IIA always produces an integer solution if one exists. In practice we usually achieve solutions close to the user goals, but there is no guarantee that the solution is optimal, nor even satisfies variable bounds, e.g. has positive intervals. We describe several algorithmic changes since first publication that tend to improve the final solution. The software is freely available.
CuCr2O4 spinel is a candidate coating material for central receivers in concentrating solar power to protect structural alloys against high temperature oxidation and related degradation. Coating performance and microstructure of dip-coated and sintered coatings is dictated by the initial particle size of the CuCr2O4 and sintering temperature, but can be compromised by particle agglomeration. Here in this study, sub-micron particles were synthesised through the Pechini and modified Pechini sol–gel methods. Phase composition was confirmed via X-ray diffraction. Particle growth during calcination of the nanoparticles at different temperatures (650°C, 750°C, 850°C) and times (between 1 and 24 h) was measured via laser diffraction and scanning electron microscopy. The modified Pechini method displayed evidence of smaller particle sizes and greater agglomeration. The kinetics of particle growth observed are consistent with a diffusion limited inhibited grain growth model.
The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations.
Induced seismicity is an inherent risk associated with geologic carbon storage (GCS) in deep rock formations that could contain undetected faults prone to failure. Modeling-based risk assessment has been implemented to quantify the potential of injection-induced seismicity, but typically simplified multiscale geologic features or neglected multiphysics coupled mechanisms because of the uncertainty in field data and computational cost of field-scale simulations, which may limit the reliable prediction of seismic hazard caused by industrial-scale CO2 storage. The degree of lateral continuity of the stratigraphic interbedding below the reservoir and depth-dependent fault permeability can enhance or inhibit pore-pressure diffusion and corresponding poroelastic stressing along a basement fault. This study presents a rigorous modeling scheme with optimal geological and operational parameters needed to be considered in seismic monitoring and mitigation strategies for safe GCS.
Protecting against multi-step attacks of uncertain start times and duration forces the defenders into indefinite, always ongoing, resource-intensive response. To allocate resources effectively, the defender must analyze and respond to an uncertain stream of potentially undetected multiple multi-step attacks and take measures of attack and response intensity over time into account. Such response requires estimation of overall attack success metrics and evaluating effect of defender strategies and actions associated with specific attack steps on overall attack metrics. We present a novel game-theoretic approach GPLADD to attack metrics estimation and demonstrate it on attack data derived from MITRE's ATT&CK Framework and other sources. In GPLADD, the time to complete attack steps is explicit; the attack dynamics emerges from attack graph and attacker-defender capabilities and strategies and therefore reflects 'physics' of attacks. The time the attacker takes to complete an attack step is drawn from a probability distribution determined by attacker and defender strategies and capabilities. This makes time a physical constraint on attack success parameters and enables comparing different defender resource allocation strategies across different attacks. We solve for attack success metrics by approximating attacker-defender games as discrete-time Markov chains and show evaluation of return on detection investments associated with different attack steps. We apply GPLADD to MITRE's APT3 data from ATT&CK Framework and show that there are substantial and un-intuitive differences in estimated real-world vendor performance against a simplified APT3 attack. We focus on metrics that reflect attack difficulty versus attacker ability to remain hidden in the system after gaining control. This enables practical defender optimization and resource allocation against multi-step attacks.
A new non-neutral generalized Ohm's law (GOL) model for atomic plasmas is presented. This model differs from previous models of this type in that quasi-neutrality is not assumed at any point. Collisional effects due to ionization, recombination, and elastic scattering are included, and an expression for the associated plasma conductivity is derived. An initial set of numerical simulations are considered that compare the GOL model to a two-fluid model in the ideal (collisionless) case. The results demonstrate that solutions obtained from the two models are essentially indistinguishable in most cases when the ion-electron mass ratio is within the range of physical values for atomic plasmas. Additionally, some limitations of the model are discussed.
A large body of work has demonstrated that parameterized artificial neural networks (ANNs) can efficiently describe ground states of numerous interesting quantum many-body Hamiltonians. However, the standard variational algorithms used to update or train the ANN parameters can get trapped in local minima, especially for frustrated systems and even if the representation is sufficiently expressive. We propose a parallel tempering method that facilitates escape from such local minima. This methods involves training multiple ANNs independently, with each simulation governed by a Hamiltonian with a different “driver” strength, in analogy to quantum parallel tempering, and it incorporates an update step into the training that allows for the exchange of neighboring ANN configurations. We study instances from two classes of Hamiltonians to demonstrate the utility of our approach using Restricted Boltzmann Machines as our parameterized ANN. The first instance is based on a permutation-invariant Hamiltonian whose landscape stymies the standard training algorithm by drawing it increasingly to a false local minimum. The second instance is four hydrogen atoms arranged in a rectangle, which is an instance of the second quantized electronic structure Hamiltonian discretized using Gaussian basis functions. We study this problem in a minimal basis set, which exhibits false minima that can trap the standard variational algorithm despite the problem’s small size. We show that augmenting the training with quantum parallel tempering becomes useful to finding good approximations to the ground states of these problem instances.
The Z Machine at Sandia National Laboratories is a pulsed power facility for high-energy density physics experiments that can shock materials to extreme temperatures and pressures through a focused energy release of up to ∼ 25 MJ in < 100 nanoseconds. It has been in operation for more than two decades and conducts up to ∼ 100 experiments, or “shots,” per year. Based on a set of 74 known shot times from 2018, we determined that Z Machine shots produce detectable ∼ 3–17 Hz ground motion 12 km away at the Albuquerque Seismological Laboratory, New Mexico (ANMO), borehole seismograph, with peak signal at ∼ 7 Hz. The known shot waveforms were used to create a three-component template, leading to the detection of 2339 Z Machine shots since 1998 through single-station cross-correlation. Local seismic magnitude estimates range from local magnitude (ML) -2 to -1.3 and indicate that only a small fraction of the shot energy is transmitted by seismic phases observable at 12 km distance. The most recent major facility renovation, which was intended to decrease mechanical dissipation, is associated with an abrupt decrease in observed seismic amplitudes at ANMO despite stable maximum shot energy. The highly repetitive impulsive sources are well suited to coda-wave interferometry to investigate time-dependent velocity structures. Relative velocity variations (dv/v) show an annual cycle with amplitude of ∼ 0.2%. Local minima are observed in the late spring, and dv/v increases through the summer monsoon rainfall, possibly reflecting patchy saturation as rainfall infiltrates near the eastern edge of the Albuquerque basin. The cumulative results demonstrate that forensic seismology can provide insight into long-term operation of facilities such as pulsed-power laboratories, and that their recurring signals may be valuable for studies of time-dependent structure.
This document provides the instructions for participating in the 2021 blind photovoltaic (PV) modeling intercomparison organized by the PV Performance Modeling Collaborative (PVPMC). It describes the system configurations, metadata, and other information necessary for the modeling exercise. The practical details of the validation datasets are also described. The datasets were published online in open access in April 2023, after completing the analysis of the results.
Compressible wall-modeled large-eddy simulations of Mach 8 turbulent boundary-layer flows over a flat plate were carried out for the conditions of the hypersonic wind tunnel at Sandia National Laboratories. The simulations provide new insight into the effect of wall cooling on the aero-optical path distortions for hypersonic turbulent boundary-layer flows. Four different wall-to-recovery temperature ratios, 0.3, 0.48, 0.71, and 0.89, are considered. Despite the much lower grid resolution, the mean velocity, temperature, and resolved Reynolds stress profiles from the simulation for a temperature ratio of 0.48 are in good agreement with those from a reference direct numerical simulation. The normalized root-mean-square optical path difference obtained from the present simulations is compared with that from reference direct numerical simulations, Sandia experiments, as well as predictions obtained with a semi-analytical model by Notre Dame University. The present analysis focuses on the effect of wall cooling on the wall-normal density correlations, on key underlying assumptions of the aforementioned model such as the strong Reynolds analogy, and on the elevation angle effect on the optical path difference. Wall cooling is found to increase the velocity fluctuations and decrease the density fluctuations, resulting in an overall reduction of the normalized optical path distortion. Compared to the simulations, the basic strong Reynolds analogy overpredicts the temperature fluctuations for cooled walls. Also different from the strong Reynolds analogy, the velocity and temperature fluctuations are not perfectly anticorrelated. Finally, as the wall temperature is raised, the density correlation length, away from the wall but inside the boundary layer, increases significantly for beam paths tilted in the downstream direction.
In magneto-inertial fusion, the ratio of the characteristic fuel length perpendicular to the applied magnetic field R to the α-particle Larmor radius Q α is a critical parameter setting the scale of electron thermal-conduction loss and charged burn-product confinement. Using a previously developed deep-learning-based Bayesian inference tool, we obtain the magnetic-field fuel-radius product B R ∝ R / Q α from an ensemble of 16 magnetized liner inertial fusion (MagLIF) experiments. Observations of the trends in BR are consistent with relative trade-offs between compression and flux loss as well as the impact of mix from 1D resistive radiation magneto-hydrodynamics simulations in all but two experiments, for which 3D effects are hypothesized to play a significant role. Finally, we explain the relationship between BR and the generalized Lawson parameter χ. Our results indicate the ability to improve performance in MagLIF through careful tuning of experimental inputs, while also highlighting key risks from mix and 3D effects that must be mitigated in scaling MagLIF to higher currents with a next-generation driver.
In typical carbonyl-containing molecules, bond dissociation events follow initial excitation to $nπ_{C=O}$$^*$ states. However, in acetyl iodide, the iodine atom gives rise to electronic states with mixed $nπ_{C=O}$$^*$ and $nπ_{C–I}$$^*$ character, leading to complex excited-state dynamics, ultimately resulting in dissociation. Using ultrafast extreme ultraviolet (XUV) transient absorption spectroscopy and quantum chemical calculations, we present an investigation of the primary photodissociation dynamics of acetyl iodide via time-resolved spectroscopy of core-to-valence transitions of the I atom after 266 nm excitation. The probed I 4d-to-valence transitions show features that evolve on sub-100-fs time scales, reporting on excited-state wavepacket evolution during dissociation. These features subsequently evolve to yield spectral signatures corresponding to free iodine atoms in their spin–orbit ground and excited states with a branching ratio of 1.1:1 following dissociation of the C–I bond. Calculations of the valence excitation spectrum via equation-of-motion coupled cluster with single and double substitutions (EOM-CCSD) show that initial excited states are of spin-mixed character. From the initially pumped spin-mixed state, we use a combination of time-dependent density functional theory (TDDFT)-driven nonadiabatic ab initio molecular dynamics and EOM-CCSD calculations of the N$_{4,5}$ edge to reveal a sharp inflection point in the transient XUV signal that corresponds to rapid C–I homolysis. Here, by examining the molecular orbitals involved in the core-level excitations at and around this inflection point, we are able to piece together a detailed picture of C–I bond photolysis in which d → σ* transitions give way to d → p excitations as the bond dissociates. We also report theoretical predictions of short-lived, weak 4d → 5d transitions in acetyl iodide, validated by weak bleaching in the experimental transient XUV spectra. This joint experimental–theoretical effort has thus unraveled the detailed electronic structure and dynamics of a strongly spin–orbit coupled system.
We investigate the potential of liquid hydrogen storage (LH2) on-board Class-8 heavy duty trucks to resolve many of the range, weight, volume, refueling time and cost issues associated with 350 or 700-bar compressed H2 storage in Type-3 or Type-4 composite tanks. We present and discuss conceptual storage system configurations capable of supplying H2 to fuel cells at 5-bar with or without on-board LH2 pumps. Structural aspects of storing LH2 in double walled, vacuum insulated, and low-pressure Type-1 tanks are investigated. Structural materials and insulation methods are discussed for service at cryogenic temperatures and mitigation of heat leak to prevent LH2 boil-off. Failure modes of the liner and shell are identified and analyzed using the regulatory codes and detailed finite element (FE) methods. The conceptual systems are subjected to a failure modes and effects analysis (FMEA) and a safety, codes, and standards (SCS) review to rank failures and identify safety gaps. The results indicate that the conceptual systems can reach 19.6% useable gravimetric capacity, 40.9 g-H2/L useable volumetric capacity and $174–183/kg-H2 cost (2016 USD) when manufactured 100,000 systems annually.
The mechanical behavior of partial-penetration laser welds exhibits significant variability in engineering quantities such as strength and apparent ductility. Understanding the root cause of this variability is important when using such welds in engineering designs. In Part II of this work, we develop finite element simulations with geometry derived from micro-computed tomography (μCT) scans of partial-penetration 304L stainless steel laser welds that were analyzed in Part I. We use these models to study the effects of the welds’ small-scale geometry, including porosity and weld depth variability, on the structural performance metrics of weld ductility and strength under quasi-static tensile loading. We show that this small-scale geometry is the primary cause of the observed variability for these mechanical response quantities. Additionally, we explore the sensitivity of model results to the conversion of the μCT data to discretized model geometry using different segmentation algorithms, and to the effect of small-scale geometry simplifications for pore shape and weld root texture. The modeling approach outlined and results of this work may be applicable to other material systems with small-scale geometric features and defects, such as additively manufactured materials.
Recent studies on off-stoichiometric thermosets reveal unique viscoelastic behavior derived from increased free volume and physical interactions between chain ends. To understand structural characteristics arising from cure and its effect on properties, we developed a Monte Carlo model based on step-growth polymerization. Our model accurately predicted structure-property trends for a two-component system of EPON 828 (EPON) and ethylenediamine. A second epoxy monomer, D.E.R. 732 (DER), was investigated to modulate Tg. Binary mixtures of EPON and DER in off-stoichiometric, amine-rich formulations resulted in nonlinear evolution of thermomechanical properties with respect to initial formulation stoichiometry. Modifying our model with kinetic parameters allowing for differential epoxide/amine reaction kinetics only partially accounted for trends in Tg, suggesting that spatiotemporal contributions─not captured by our model─were significant determinants of material properties compared to polymer architecture for three-component systems. These findings underpin the importance of spatial awareness in modeling to inform the development of dynamic thermosets.
Recent studies of power flow and particle transport in multi-MA pulsed-power accelerators demonstrate that electrode plasmas may reduce accelerator efficiency by shunting current upstream from the load. The detailed generation and evolution of these electrode plasmas are examined here using fully relativistic, Monte Carlo particle-in-cell (PIC) and magnetohydrodynamic (MHD) simulations over a range of peak currents (8–48 MA). The PIC calculations, informed by vacuum science, describe the electrode surface breakdown and particle transport prior to electrode melt. The MHD calculations show the bulk electrode evolution during melt. The physical description provided by this combined study begins with the rising local magnetic field that increases the local electrode surface temperature. This initiates the thermal desorption of contaminants from the electrode surface, with contributions from atoms outgassing from the bulk metal. The contaminants rapidly ionize forming a 1015-1018 cm-3 plasma that is effectively resistive while weakly collisional because it is created within, and rapidly penetrated by, a strong magnetic field (> 30 T). Prior to melting, the density of this surface plasma is limited by the concentration of absorbed contaminants in the bulk (~1019 cm-3 for hydrogen), its diffusion, and ionization. Eventually, the melting electrodes form a conducting plasma (1021-1023 cm-3) that experiences j × B compression and a typical decaying magnetic diffusion profile. This physical sequence ignores the transport of collisional plasmas of 1019 cm-3 which may arise from electrode defects and associated instabilities. Nonetheless, this picture of plasma formation and melt may be extrapolated to higher-energy pulsed-power systems.
This paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems and then deployed for potential energy prediction for C5H5 using B2PLYP-D3/6-311++G(d,p) for high-fidelity simulation data and Hartree-Fock 6-31G for low-fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error.
Multimodal datasets of materials are rich sources of information which can be leveraged for expedited discovery of process–structure–property relationships and for designing materials with targeted structures and/or properties. For this data descriptor article, we provide a multimodal dataset of magnetron sputter-deposited molybdenum (Mo) thin films, which are used in a variety of industries including high temperature coatings, photovoltaics, and microelectronics. In this dataset we explored a process space consisting of 27 unique combinations of sputter power and Ar deposition pressure. Here, the phase, structure, surface morphology, and composition of the Mo thin films were characterized by x-ray diffraction, scanning electron microscopy, atomic force microscopy, and Rutherford backscattering spectrometry. Physical properties—namely, thickness, film stress and sheet resistance—were also measured to provide additional film characteristics and behaviors. Additionally, nanoindentation was utilized to obtain mechanical load-displacement data. The entire dataset consists of 2072 measurements including scalar values (e.g., film stress values), 2D linescans (e.g., x-ray diffractograms), and 3D imagery (e.g., atomic force microscopy images). An additional 1889 quantities, including film hardness, modulus, electrical resistivity, density, and surface roughness, were derived from the experimental datasets using traditional methods. Minimal analysis and discussion of the results are provided in this data descriptor article to limit the authors’ preconceived interpretations of the data. Overall, the data modalities are consistent with previous reports of refractory metal thin films, ensuring that a high-quality dataset was generated. The entirety of this data is committed to a public repository in the Materials Data Facility.
A cloud of very fast, O(km/s), and very fine, O(µm), particles may be ejected when a strong shock impacts and possibly melts the free surface of a solid metal. To quantify these dynamics, this work develops an ultraviolet, long-working distance, two-pulse Digital Holographic Microscopy (DHM) configuration and is the first to replace film recording with digital sensors for this challenging application. A proposed multi-iteration DHM processing algorithm is demonstrated for automated measures of the sizes, velocities, and three-dimensional positions of non-spherical particles. Ejecta as small as 2 µm diameter are successfully tracked, while uncertainty simulations indicate that particle size distributions are accurately quantified for diameters ≥4 µm. These techniques are demonstrated on three explosively driven experiments. Measured ejecta size and velocity statistics are shown to be consistent with prior film-based recording, while also revealing spatial variations in velocities and 3D positions that have yet to be widely investigated. Having eliminated time-consuming analog film processing, the methodologies proposed here are expected to significantly accelerate future experimental investigation of ejecta physics.
Focused ion beam implantation is ideally suited for placing defect centers in wide bandgap semiconductors with nanometer spatial resolution. However, the fact that only a few percent of implanted defects can be activated to become efficient single photon emitters prevents this powerful capability to reach its full potential in photonic/electronic integration of quantum defects. Here an industry adaptive scalable technique is demonstrated to deterministically create single defects in commercial grade silicon carbide by performing repeated low ion number implantation and in situ photoluminescence evaluation after each round of implantation. An array of 9 single defects in 13 targeted locations is successfully created—a ≈70% yield which is more than an order of magnitude higher than achieved in a typical single pass ion implantation. The remaining emitters exhibit non-classical photon emission statistics corresponding to the existence of at most two emitters. This approach can be further integrated with other advanced techniques such as in situ annealing and cryogenic operations to extend to other material platforms for various quantum information technologies.
Schiffman, Zachary R.; Fernanders, Marium S.; Davis, Ryan D.; Tolbert, Margaret A.
Mineral dust can indirectly impact climate by nucleation of atmospheric solids, for example, by heterogeneously nucleating ice in mixed-phase clouds or by impacting the phase of aerosols and clouds through contact nucleation. The effectiveness toward nucleation of individual components of mineral dust requires further study. Here, the nucleation behavior of metal oxide nanoparticle components of atmospheric mineral dust is investigated. A long-working-distance optical trap is used to study contact and immersion nucleation of ammonium sulfate by transition-metal oxides, and an environmental chamber is used to probe depositional ice nucleation on metal oxide particles. Previous theory dictates that ice nucleation and heterogeneous nucleation of atmospheric salts can be impacted by several factors including morphology, lattice match, and surface area. Here, we observe a correlation between the cationic oxidation states of the metal oxide heterogeneous nuclei and their effectiveness in causing nucleation in both contact efflorescence mode and depositional freezing mode. In contrast to the activity of contact efflorescence, the same metal oxide particles did not cause a significant increase in efflorescence relative humidity when immersed in the droplet. These experiments suggest that metal speciation, possibly as a result of cationic charge sites, may play a role in the effectiveness of nucleation that is initiated at particle surfaces.
Lithium dendrite growth hinders the use of lithium metal anodes in commercial batteries. We present a 3D model to study the mechanical and electrochemical mechanisms that drive microscale plating. With this model, we investigate electrochemical response across a lithium protrusion characteristic of rough anode surfaces, representing the separator as a porous polymer in non-conformal contact with a lithium anode. The impact of pressure on separator morphology and electrochemical response is of particular interest, as external pressure can improve cell performance. We explore the relationships between plating propensity, stack pressure, and material properties. External pressure suppresses lithium plating due to interfacial stress and separator pore closure, leading to inhomogeneous plating rates. For moderate pressures, dendrite growth is completely suppressed, as plating will occur in the electrolyte-filled gaps between anode and separator. In fast-charging conditions and systems with low electrolyte diffusivities, the benefits of pressure are overridden by ion transport limitations.
Song, Yueming; Bhargava, Bhuvsmita; Stewart, David M.; Talin, A.A.; Rubloff, Gary W.; Albertus, Paul
Lithium metal solid-state batteries (LiSSBs) present new challenges in the measurement of material, component, and cell mechanical behaviors and in the measurement and theory of fundamental mechanical-electrochemical (thermodynamics, transport, and kinetics) couplings. Here, we classify the major mechanical and electrochemical-mechanical (ECM) studies underway and provide an overview of major mechanical testing platforms. We emphasize key distinctions among testing platforms, including tip- vs. platen-based sample compression, surface- vs. volume-based analysis, ease of integration with a vacuum or inert atmosphere environment, the ability to control and measure force/displacement over long periods of time, ranges of force and contact area, and others. Among the techniques we review, nanoindentation platforms offer some unique benefits associated with being able to use both tip-based nanoindentation techniques as well as platen-based compression over areas approaching 1 mm2. Sample design is also important: while most efforts are particle-based (i.e., using particles of solid electrolyte and cathode-active materials and densifying them using sintering or pressure), the resulting electrochemical response is from the overall collection of particles present. In contrast, thin-film (<1 μm) solid-state battery materials (e.g., Li, LiPON, LCO) provide well defined and uniform structures well suited for fundamental electrochemical-mechanical studies and offer an important opportunity to drive underlying scientific advances in LiSSB and other areas. We believe there are exciting opportunities to advance the measurement of both mechanical properties and electrochemical-mechanical couplings through the careful and novel co-design of test structures and experimental approaches for LiSSB materials, components, and cells.
Network intrusion detection systems (NIDS) are commonly used to detect malware communications, including command-and-control (C2) traffic from botnets. NIDS performance assessments have been studied for decades, but mathematical modeling has rarely been used to explore NIDS performance. This paper details a mathematical model that describes a NIDS performing packet inspection and its detection of malware's C2 traffic. Here, the paper further describes an emulation testbed and a set of cyber experiments that used the testbed to validate the model. These experiments included a commonly used NIDS (Snort) and traffic with contents from a pervasive malware (Emotet). Results are presented for two scenarios: a nominal scenario and a “stressed” scenario in which the NIDS cannot process all incoming packets. Model and experiment results match well, with model estimates mostly falling within 95 % confidence intervals on the experiment means. Model results were produced 70-3000 times faster than the experimental results. Consequently, the model's predictive capability could potentially be used to support decisions about NIDS configuration and effectiveness that require high confidence results, quantification of uncertainty, and exploration of large parameter spaces. Furthermore, the experiments provide an example for how emulation testbeds can be used to validate cyber models that include stochastic variability.
See, Judi E.; Rosenfeld, Robert B.; Taylor, Sylvester; Wedic, K.M.
An analogy is drawn between the study of human behavior and the study of plutonium to demonstrate that soft and hard sciences are more similar than different, making the distinction moot and unproductive. The studies of human behavior and plutonium follow a common scientific research cycle that aligns with Thomas Kuhn’s views of scientific change. This common research cycle provides evidence that the thought processes and methodologies required for success are congruent in the soft and hard sciences. The primary implication from this analogy is that scientists in all disciplines should eradicate the distinction between soft and hard sciences. Focusing on similarities rather than differences among researchers from different disciplines is necessary to enhance collective intelligence and the type of transdisciplinary collaboration required to tackle difficult sociotechnical problems.
Khalatpour, Ali; Tam, Man C.; Addamane, Sadhvikas J.; Reno, John; Wasilewski, Zbignew; Hu, Qing
Room temperature operation of terahertz quantum cascade lasers (THz QCLs) has been a long-pursued goal to realize compact semiconductor THz sources. In this paper, we report on improving the maximum operating temperature of THz QCLs to ∼261 K as a step toward the realization of this goal.
Herein, we report the synthesis of a novel, tetraphenylethylene-based ligand for metal-organic frameworks (MOFs). Incorporation of this ligand into a Zn- or Eu-based MOF increased the quantum yield (QY) by almost 2.5× compared to the linker alone. Furthermore, the choice of guest solvent impacted the QY and solvatochromatic response. These shifts are consistent with solvent dielectric constant as well as molecular polarizability.
The resurgence of interest in hydrogen-related technologies has stimulated new studies aimed at advancing lesser-developed water-splitting processes, such as solar thermochemical hydrogen production (STCH). Progress in STCH has been largely hindered by a lack of new materials able to efficiently split water at a rate comparable to ceria under identical experimental conditions. BaCe0.25Mn0.75O3 (BCM) recently demonstrated enhanced hydrogen production over ceria and has the potential to further our understanding of two-step thermochemical cycles. A significant feature of the 12R hexagonal perovskite structure of BCM is the tendency to, in part, form a 6H polytype at high temperatures and reducing environments (i.e., during the first step of the thermochemical cycle), which may serve to mitigate degradation of the complex oxide. An analogous compound, namely BaNb0.25Mn0.75O3 (BNM) with a 12R structure was synthesized and displays nearly complete conversion to the 6H structure under identical reaction conditions as BCM. The structure of the BNM-6H polytype was determined from Rietveld refinement of synchrotron powder X-ray diffraction data and is presented within the context of the previously established BCM-6H structure.
Strong and ultrastrong coupling between intersubband transitions in quantum wells and cavity photons have been realized in mid-infrared and terahertz spectral regions. However, most previous works employed a large number of quantum wells on rigid substrates to achieve coupling strengths reaching the strong or ultrastrong coupling regime. In this work, we experimentally demonstrate ultrastrong coupling between the intersubband transition in a single quantum well and the resonant mode of photonic nanocavity at room temperature. We also observe strong coupling between the nanocavity resonance and the second-order intersubband transition in a single quantum well. Furthermore, we implement for the first time such intersubband cavity polariton systems on soft and flexible substrates and demonstrate that bending of the single quantum well does not significantly affect the characteristics of the cavity polaritons. This work paves the way to broaden the range of potential applications of intersubband cavity polaritons including soft and wearable photonics.
Engineering the transition metal dichalcogenide (TMD)-metal interface is critical for the development of two-dimensional semiconductor devices. By directly probing the electronic structures of WS2-Au and WSe2-Au interfaces with high spatial resolution, we delineate nanoscale heterogeneities in the composite systems that give rise to local Schottky barrier height modulations. Photoelectron spectroscopy reveals large variations (>100 meV) in TMD work function and binding energies for the occupied electronic states. Characterization of the composite systems with electron backscatter diffraction and scanning tunneling microscopy leads us to attribute these heterogeneities to differing crystallite orientations in the Au contact, suggesting an inherent role of the metal microstructure in contact formation. We then leverage our understanding to develop straightforward Au processing techniques to form TMD-Au interfaces with reduced heterogeneity. Our findings illustrate the sensitivity of TMDs’ electronic properties to metal contact microstructure and the viability of tuning the interface through contact engineering.
Ruiz-Gomez, Sandra; Perez, Lucas; Mascaraque, Arantzazu; Santos, Benito; El Gabaly Marquez, Farid E.; Schmid, Andreas K.; De La Figuera, Juan
The magnetization patterns on three atomic layers thick islands of Co on Ru(0001) are studied by spin-polarized low-energy electron microscopy (SPLEEM). In-plane magnetized micrometer wide triangular Co islands are grown on Ru(0001). They present two different orientations correlated with two different stacking sequences which differ only in the last layer position. The stacking sequence determines the type of magnetization pattern observed: the hcp islands present very wide domain walls, while the fcc islands present domains separated by much narrower domain walls. The former is an extremely low in-plane anisotropy system. We estimate the in-plane magnetic anisotropy of the fcc regions to be 1.96 × 104 J m−3 and of the hcp ones to be 2.5 × 102 J m−3
Approximation algorithms for computationally complex problems are of significant importance in computing as they provide computational guarantees of obtaining practically useful results for otherwise computationally intractable problems. The demonstration of implementing formal approximation algorithms on spiking neuromorphic hardware is a critical step in establishing that neuromorphic computing can offer cost-effective solutions to significant optimization problems while retaining important computational guarantees on the quality of solutions. Here, we demonstrate that the Loihi platform is capable of effectively implementing the Goemans-Williamson (GW) approximation algorithm for MAXCUT, an NP-hard problem that has applications ranging from VLSI design to network analysis. We show that a Loihi implementation of the approximation step of the GW algorithm obtains equivalent maximum cuts of graphs as conventional algorithms, and we describe how different aspects of architecture precision impacts the algorithm performance.
Mixture formation in a hydrogen-fueled heavy-duty engine with direct injection and a nearly-quiescent top-hat combustion chamber was investigated using laser-induced fluorescence imaging, with 1,4-difluorobenzene serving as a fluorescent tracer seeded into hydrogen. The engine was motored at 1200 rpm, 1.0 bar intake pressure, and 335 K intake temperature. An outward opening medium-pressure hollow-cone injector was operated at two different injection pressures and five different injection timings from early injection during the intake stroke to late injection towards the end of compression stroke. Fuel fumigation upstream of the intake provided a well-mixed reference case for image calibration. This paper presents the evolution of in-cylinder equivalence ratio distribution evaluated during the injection event itself for the cylinder-axis plane and during the compression stroke at different positions of the light sheet within the swirl plane. During the injection event, the originally annular jet collapses onto the jet axis within 1°CA after jet emergence and within 10 mm downstream of the nozzle. Multiple shock cells are visible - their size decreases with decreasing pressure ratio. The results of the equivalence ratio distribution show high cyclic variability of mixing for all injection timings during the compression stroke, but only minor variability with early injection during the intake stroke. The ensemble-mean fuel distribution shows that fuel-rich zones shift from the intake side to the exhaust side of the combustion chamber as the injection is advanced. Probability density functions of global equivalence ratio and equivalence ratio at potential spark locations suggest that retarded fuel injection might significantly increase NO emissions and the cyclic variability of early flame kernel development.
Liquefied Petroleum Gas (LPG), as a common alternative fuel for internal combustion engines is currently widespread in use for fleet vehicles. However, a current majority of the LPG-fueled engines, uses port-fuel injection that offers lower power density when compared to a gasoline engine of equivalent displacement volume. This is due to the lower molecular weight and higher volatility of LPG components that displaces more air in the intake charge due to the larger volume occupied by the gaseous fuel. LPG direct-injection during the closed-valve portion of the cycle can avoid displacement of intake air and can thereby help achieve comparable gasoline-engine power densities. However, under certain engine operating conditions, direct-injection sprays can collapse and lead to sub-optimal fuel-air mixing, wall-wetting, incomplete combustion, and increased pollutant emissions. Direct-injection LPG, owing to its thermo-physical properties is more prone to spray collapse than gasoline sprays. However, the impact of spray collapse for high-volatility LPG on mixture preparation and subsequent combustion is not fully understood. To this end, direct-injection, laser-spark ignition experiments using propane as a surrogate for LPG under lean and stoichiometric engine operating conditions were carried out in an optically accessible, single cylinder, heavy-duty, diesel engine. A quick-switching parallel propane and iso-octane fuel system allows for easy comparison between the two fuels. Fuel temperature, operating equivalence ratio and injection timing are varied for a parametric study. In addition to combustion characterization using conventional cylinder pressure measurements, optical diagnostics are employed. These include infrared (IR) imaging for quantifying fuel-air mixture homogeneity and high-speed natural luminosity imaging for tracking the spatial and temporal progression of combustion. Imaging of infrared emission from compression-heated fuel does not reveal any significant differences in the signal distribution between collapsing and non-collapsing sprays at the spark timing. Irrespective of coolant temperatures, early injection timing resulted in a homogeneous mixture that lead to repeatable flame evolution with minimal cycle-to-cycle variability for both LPG and iso-octane. However, late injection timing resulted in mixture inhomogeneity and non-isotropic turbulence distribution. Under lean operation with late injection timing, LPG combustion is shown to benefit from a more favorable mixture distribution and flow properties induced by spray collapse. On the other hand, identical operating conditions proved to be detrimental for iso-octane combustion most likely caused by distribution of lean mixtures near the spark location that negatively impact initial flame kernel growth leading to increased cycle-to-cycle variability.
Coordinate transformations are a fundamental operation that must be performed by any animal relying upon sensory information to interact with the external world. We present a neural network model that performs a coordinate transformation from the dragonfly eye's frame of reference to the body's frame of reference while hunting. We demonstrate that the model successfully calculates turns required for interception, and discuss how future work will compare our model with biological dragonfly neural circuitry and guide neural-inspired neuromorphic implementations of coordinate transformations.
Shunting inhibition is a potential mechanism by which biological systems multiply two time-varying signals, most recently proposed in single neurons of the fly visual system. Our work demonstrates this effect in a biological neuron model and the equivalent circuit in neuromorphic hardware modeling dendrites. We present a multi-compartment neuromorphic dendritic model that produces a multiplication-like effect using the shunting inhibition mechanism by varying leakage along the dendritic cable. Dendritic computation in neuromorphic architectures has the potential to increase complexity in single neurons and reduce the energy footprint for neural networks by enabling computation in the interconnect.
Coordinate transformations are a fundamental operation that must be performed by any animal relying upon sensory information to interact with the external world. We present a neural network model that performs a coordinate transformation from the dragonfly eye's frame of reference to the body's frame of reference while hunting. We demonstrate that the model successfully calculates turns required for interception, and discuss how future work will compare our model with biological dragonfly neural circuitry and guide neural-inspired neuromorphic implementations of coordinate transformations.
Oscillatory devices have gained significant interest recently as key components of computing systems based on biomimetic neuronal spiking. An understanding of the time scales underlying the spiking is essential for engineering fast, controllable, low energy devices. However, we find that the intrinsic dynamics of these devices are difficult to properly characterize, as they can be heavily influenced by the external circuitry used to measure them. Here we demonstrate these challenges using a VO2 Mott oscillator with a sub-100 nm effective size, achieved using a nanogap cut in a metallic carbon nanotube electrode. Given the nanoscale thermal volume of this device, it would be expected to exhibit rapid oscillations. However, due to external parasitics present within commonly used current sources, we see orders of magnitude slower dynamics. Here, we outline methods for determining when measurements are dominated by extrinsic factors and discuss the operating conditions under which intrinsic oscillation frequencies may be observed.
Rare-earth terephthalic acid (BDC)-based metal-organic frameworks (MOFs) are promising candidate materials for acid gas separation and adsorption from flue gas streams. However, previous simulations have shown that acid gases (H2O, NO2, and SO2) react with the hydroxyl on the BDC linkers to form protonated acid gases as a potential degradation mechanism. Herein, gas-phase computational approaches were used to identify the formation energies of these secondary protonated acid gases across multiple BDC linker molecules. Formation energies for secondary protonated acid gases were evaluated using both density functional theory (DFT) and correlated wave function methods for varying BDC-gas reaction mechanisms. Upon validation of DFT to reproduce wave function calculation results, rotated conformational linkers and chemically functionalized BDC linkers with −OH, −NH2, and −SH were investigated. The calculations show that the rotational conformation affects the molecule stability. Double-functionalized BDC linkers, where two functional groups are substituted onto BDC, showed varied reaction energies depending on whether the functional groups donate or withdraw electrons from the aromatic system. Based on these results, BDC linker design must balance adsorption performance with degradation via linker dehydrogenation for the design of stable MOFs for acid gas separations.
Chalcogenide thin films that undergo reversible phase changes show promise for use in next-generation nanophotonics, microelectronics, and other emerging technologies. One of the many studied compounds, Ge 2 Sb 2 Te 5 , has demonstrated several useful properties and performance characteristics. However, the efficacy of benchmark Ge 2 Sb 2 Te 5 is restricted by amorphous phase thermal stability below ∼150 °C, limiting its potential use in high-temperature applications. In response, previous studies have added a fourth species (e.g., C) to sputter-deposited Ge 2 Sb 2 Te 5 , demonstrating improved thermal stability. Our current research confirms reported thermal stability enhancements and assesses the effects of carbon on crystalline phase radiation response. Through in situ transmission electron microscope irradiation studies, we examine the effect of C addition on the amorphization behavior of initially cubic and trigonal polycrystalline films irradiated using 2.8 MeV Au to various doses up to 1 × 10 15 cm −2 . It was found that increased C content reduces radiation tolerance of both cubic and trigonal phases.
Discharge of sodium coolant into containment from a sodium-cooled fast reactor vessel can occur in the event of a pipe leak or break. In this situation, some of the liquid sodium droplets discharged from the coolant system will react with oxygen in the air before reaching the containment. This phase of the event is normally termed the sodium spray fire phase. Unreacted sodium droplets pool on the containment floor where continued reaction with containment atmospheric oxygen occurs. This phase of the event is normally termed the sodium pool fire phase. Both phases of these sodium-oxygen reactions (or fires) are important to model because of the heat addition and aerosol generation that occur. Any fission products trapped in the sodium coolant may also be released during this progression of events, which if released from containment could pose a health risk to workers and the public. The paper describes progress of an international collaborative research in the area of the sodium fire modeling in the sodium-cooled fast reactors between the United States and Japan under the framework of the Civil Nuclear Energy Research and Development Working Group. In this collaboration between Sandia National Laboratories and Japan Atomic Energy Agency, the validation basis for and modeling capabilities of sodium spray and pool fires in MELCOR of Sandia National Laboratories and SPHINCS of Japan Atomic Energy Agency are being enhanced. Here this study documents MELCOR and SPHINCS sodium pool fire model validation exercises against the JAEA’s sodium pool fire experiments, F7-1 and F7-2. The proposed enhancement of the sodium pool fire models in MELCOR through addition of thermal hydraulic and sodium spreading models that enable a better representation of experimental results is also described.
Parke, Tyler; Silva-Quinones, Dhamelyz; Wang, George T.; Teplyakov, Andrew V.
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. 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.