Publications

We report real-time observations of a phase transition in the ionic solid CaF2, a model AB2 structure in high-pressure physics. Synchrotron x-ray diffraction coupled with dynamic loading to 27.7 GPa, and separately with static compression, follows, in situ, the fluorite to cotunnite structural phase transition, both on nanosecond and on minute time scales. Using Rietveld refinement techniques, we examine the kinetics and hysteresis of the transition. Our results give insight into the kinetic time scale of the fluorite-cotunnite phase transition under shock compression, which is relevant to a number of isomorphic compounds.

A Stillinger-Weber potential is computationally very efficient for molecular dynamics simulations. Despite its simple mathematical form, the Stillinger-Weber potential can be easily parameterized to ensure that crystal structures with tetrahedral bond angles (e.g., diamond-cubic, zinc-blende, and wurtzite) are stable and have the lowest energy. As a result, the Stillinger-Weber potential has been widely used to study a variety of semiconductor elements and alloys. When studying an A-B binary system, however, the Stillinger-Weber potential is associated with two major drawbacks. First, it significantly overestimates the elastic constants of elements A and B, limiting its use for systems involving both compounds and elements (e.g., an A/AB multilayer). Second, it prescribes equal energy for zinc-blende and wurtzite crystals, limiting its use for compounds with large stacking fault energies. Here in this paper, we utilize the polymorphic potential style recently implemented in LAMMPS to develop a modified Stillinger-Weber potential for InGaN that overcomes these two problems.

Attacks authored by state sponsored actors, criminal outfits, ideological enclaves and recreational hackers continue to trouble public and private cyber systems. In order to create and/or maintain an advantage over their adversaries, cyber defenders must pursue novel ways to detect, attribute and respond to offensive operations. Linkography is a topic that has been explored for decades that has found recent application to cyber security. Given the huge amounts of data available for cyber security applications of linkography, we favor semi-automated techniques to exploit this concept. In this paper, we propose a human supervised algorithm that will refine the abstractions used for this bulk approach to linkography. We found this algorithm resulted in automatically generated linkographs with higher accuracies than those derived from static abstractions. These findings suggest that linkography in general and abstraction refinement in particular are viable tools for cyber security practitioners.

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.

In numerous applications, scientists and engineers acquire varied forms of data that partially characterize the inputs to an underlying physical system. This data is then used to inform decisions such as controls and designs. Consequently, it is critical that the resulting control or design is robust to the inherent uncertainties associated with the unknown probabilistic characterization of the model inputs. Here in this work, we consider optimal control and design problems constrained by partial differential equations with uncertain inputs. We do not assume a known probabilistic model for the inputs, but rather we formulate the problem as a distributionally robust optimization problem where the outer minimization problem determines the control or design, while the inner maximization problem determines the worst-case probability measure that matches desired characteristics of the data. We analyze the inner maximization problem in the space of measures and introduce a novel measure approximation technique, based on the approximation of continuous functions, to discretize the unknown probability measure. Finally, we prove consistency of our approximated min-max problem and conclude with numerical results.

We derive an intrinsically temperature-dependent approximation to the correlation grand potential for many-electron systems in thermodynamical equilibrium in the context of finite-temperature reduced-density-matrix-functional theory (FT-RDMFT). We demonstrate its accuracy by calculating the magnetic phase diagram of the homogeneous electron gas. We compare it to known limits from highly accurate quantum Monte Carlo calculations as well as to phase diagrams obtained within existing exchange-correlation approximations from density functional theory and zero-temperature RDMFT.

Demand for pragmatic alternatives to carbon-intensive fossil fuels is growing more strident. Hydrogen represents an ideal zero-carbon clean energy carrier with high energy density. For hydrogen fuel to compete with alternatives, safe and high capacity storage materials that are readily cycled are imperative. Here, development of such a material, comprised of nickel-doped Mg nanocrystals encapsulated by molecular-sieving reduced graphene oxide (rGO) layers, is reported. While most work on advanced hydrogen storage composites to date endeavor to explore either nanosizing or addition of carbon materials as secondary additives individually, methods to enable both are pioneered: “dual-channel” doping combines the benefits of two different modalities of enhancement. Specifically, both external (rGO strain) and internal (Ni doping) mechanisms are used to efficiently promote both hydriding and dehydriding processes of Mg nanocrystals, simultaneously achieving high hydrogen storage capacity (6.5 wt% in the total composite) and excellent kinetics while maintaining robustness. Furthermore, hydrogen uptake is remarkably accomplished at room temperature and also under 1 bar—as observed during in situ measurements—which is a substantial advance for a reversible metal hydride material. The realization of three complementary functional components in one material breaks new ground in metal hydrides and makes solid-state materials viable candidates for hydrogen-fueled applications.

Ordering nanoparticles into a desired super-structure is often crucial for their technological applications. We use molecular dynamics simulations to study the assembly of nanoparticles in a polymer brush randomly grafted to a planar surface as the solvent evaporates. Initially, the nanoparticles are dispersed in a solvent that wets the polymer brush. After the solvent evaporates, the nanoparticles are either inside the brush or adsorbed at the surface of the brush, depending on the strength of the nanoparticle-polymer interaction. For strong nanoparticle-polymer interactions, a 2-dimensional ordered array is only formed when the brush density is finely tuned to accommodate a single layer of nanoparticles. When the brush density is higher or lower than this optimal value, the distribution of nanoparticles shows large fluctuations in space and the packing order diminishes. For weak nanoparticle-polymer interactions, the nanoparticles order into a hexagonal array on top of the polymer brush as long as the grafting density is high enough to yield a dense brush. An interesting healing effect is observed for a low-grafting-density polymer brush that can become more uniform in the presence of weakly adsorbed nanoparticles.

We outline ideas on desired properties for a new generation of effective core potentials (ECPs) that will allow valence-only calculations to reach the full potential offered by recent advances in many-body wave function methods. The key improvements include consistent use of correlated methods throughout ECP constructions and improved transferability as required for an accurate description of molecular systems over a range of geometries. The guiding principle is the isospectrality of all-electron and ECP Hamiltonians for a subset of valence states. We illustrate these concepts on a few first- and second-row atoms (B, C, N, O, S), and we obtain higher accuracy in transferability than previous constructions while using semi-local ECPs with a small number of parameters. In addition, the constructed ECPs enable many-body calculations of valence properties with higher (or same) accuracy than their all-electron counterparts with uncorrelated cores. This implies that the ECPs include also some of the impacts of core-core and core-valence correlations on valence properties. The results open further prospects for ECP improvements and refinements.

We present DAGSENS, a new approach to parametric transient sensitivity analysis of Differential Algebraic Equation systems (DAEs), such as SPICE-level circuits. The key ideas behind DAGSENS are, (1) to represent the entire sequence of computations from DAE parameters to the objective function (whose sensitivity is needed) as a Directed Acyclic Graph (DAG) called the 'sensitivity DAG', and (2) to compute the required sensitivites efficiently by using dynamic programming techniques to traverse the DAG. DAGSENS is simple, elegant, and easy-to-understand compared to previous approaches; for example, in DAGSENS, one can switch between direct and adjoint sensitivities simply by reversing the direction of DAG traversal. Also, DAGSENS is more powerful than previous approaches because it works for a more general class of objective functions, including those based on 'events' that occur during a transient simulation (e.g., a node voltage crossing a threshold, a phase-locked loop (PLL) achieving lock, a circuit signal reaching its maximum/minimum value, etc.). In this paper, we demonstrate DAGSENS on several electronic and biological applications, including high-speed communication, statistical cell library characterization, and gene expression.

Bilayer van der Waals (vdW) heterostructures such as MoS2/WS2 and MoSe2/WSe2 have attracted much attention recently, particularly because of their type II band alignments and the formation of interlayer exciton as the lowest-energy excitonic state. In this work, we calculate the electronic and optical properties of such heterostructures with the first-principles GW+Bethe-Salpeter Equation (BSE) method and reveal the important role of interlayer coupling in deciding the excited-state properties, including the band alignment and excitonic properties. Our calculation shows that due to the interlayer coupling, the low energy excitons can be widely tuned by a vertical gate field. In particular, the dipole oscillator strength and radiative lifetime of the lowest energy exciton in these bilayer heterostructures is varied by over an order of magnitude within a practical external gate field. We also build a simple model that captures the essential physics behind this tunability and allows the extension of the ab initio results to a large range of electric fields. Our work clarifies the physical picture of interlayer excitons in bilayer vdW heterostructures and predicts a wide range of gate-tunable excited-state properties of 2D optoelectronic devices.

A fully coupled three-dimensional surface and subsurface land model is developed and applied to a site along the Columbia River to simulate three-way interactions among river water, groundwater, and land surface processes. The model features the coupling of the Community Land Model version 4.5 (CLM4.5) and a massively parallel multiphysics reactive transport model (PFLOTRAN). The coupled model, named CP v1.0, is applied to a 400 m × 400 m study domain instrumented with groundwater monitoring wells along the Columbia River shoreline. CP v1.0 simulations are performed at three spatial resolutions (i.e., 2, 10, and 20 m) over a 5-year period to evaluate the impact of hydroclimatic conditions and spatial resolution on simulated variables. Results show that the coupled model is capable of simulating groundwater-river-water interactions driven by river stage variability along managed river reaches, which are of global significance as a result of over 30 000 dams constructed worldwide during the past half-century. Our numerical experiments suggest that the land-surface energy partitioning is strongly modulated by groundwater-river-water interactions through expanding the periodically inundated fraction of the riparian zone, and enhancing moisture availability in the vadose zone via capillary rise in response to the river stage change. Meanwhile, CLM4.5 fails to capture the key hydrologic process (i.e., groundwater-river-water exchange) at the site, and consequently simulates drastically different water and energy budgets. Furthermore, spatial resolution is found to significantly impact the accuracy of estimated the mass exchange rates at the boundaries of the aquifer, and it becomes critical when surface and subsurface become more tightly coupled with groundwater table within 6 to 7 meters below the surface. Inclusion of lateral subsurface flow influenced both the surface energy budget and subsurface transport processes as a result of river-water intrusion into the subsurface in response to an elevated river stage that increased soil moisture for evapotranspiration and suppressed available energy for sensible heat in the warm season. The coupled model developed in this study can be used for improving mechanistic understanding of ecosystem functioning and biogeochemical cycling along river corridors under historical and future hydroclimatic changes. The dataset presented in this study can also serve as a good benchmarking case for testing other integrated models.

Channeled spectropolarimetry measures the spectrally resolved Stokes parameters. A key aspect of this technique is to accurately reconstruct the Stokes parameters from a modulated measurement of the channeled spectropolarimeter. The state-of-the-art reconstruction algorithm uses the Fourier transform to extract the Stokes parameters from channels in the Fourier domain. While this approach is straightforward, it can be sensitive to noise and channel cross-talk, and it imposes bandwidth limitations that cut o high frequency details. To overcome these drawbacks, we present a reconstruction method called compressed channeled spectropolarimetry. In our proposed framework, reconstruction in channeled spectropolarimetry is an underdetermined problem, where we take N measurements and solve for 3N unknown Stokes parameters. We formulate an optimization problem by creating a mathematical model of the channeled spectropolarimeter with inspiration from compressed sensing. We show that our approach o ers greater noise robustness and reconstruction accuracy compared with the Fourier transform technique in simulations and experimental measurements. By demonstrating more accurate reconstructions, we push performance to the native resolution of the sensor, allowing more information to be recovered from a single measurement of a channeled spectropolarimeter.

With the rapid spread in use of Digital Image Correlation (DIC) globally, it is important there be some standard methods of verifying and validating DIC codes. To this end, the DIC Challenge board was formed and is maintained under the auspices of the Society for Experimental Mechanics (SEM) and the international DIC society (iDICs). The goal of the DIC Board and the 2D–DIC Challenge is to supply a set of well-vetted sample images and a set of analysis guidelines for standardized reporting of 2D–DIC results from these sample images, as well as for comparing the inherent accuracy of different approaches and for providing users with a means of assessing their proper implementation. This document will outline the goals of the challenge, describe the image sets that are available, and give a comparison between 12 commercial and academic 2D–DIC codes using two of the challenge image sets.

AlGaN-channel high electron mobility transistors (HEMTs) are among a class of ultra wide-bandgap transistors that are promising candidates for RF and power applications. Long-channel Al_xGa_1-xN HEMTs with x = 0.7 in the channel have been built and evaluated across the -50°C to +200°C temperature range. These devices achieved room temperature drain current as high as 46 mA/mm and were absent of gate leakage until the gate diode forward bias turn-on at ~2.8 V, with a modest -2.2 V threshold voltage. A very large I_on/I_off current ratio, of 8 × 10⁹ was demonstrated. A near ideal subthreshold slope that is just 35% higher than the theoretical limit across the temperature range was characterized. The ohmic contact characteristics were rectifying from -50°C to +50°C and became nearly linear at temperatures above 100°C. An activation energy of 0.55 eV dictates the temperature dependence of off-state leakage.

Atomic motion at grain boundaries is essential to microstructure development, growth and stability of catalysts and other nanostructured materials. However, boundary atomic motion is often too fast to observe in a conventional transmission electron microscope (TEM) and too slow for ultrafast electron microscopy. We report on the entire transformation process of strained Pt icosahedral nanoparticles (ICNPs) into larger FCC crystals, captured at 2.5 ms time resolution using a fast electron camera. Results show slow diffusive dislocation motion at nm/s inside ICNPs and fast surface transformation at μm/s. By characterizing nanoparticle strain, we show that the fast transformation is driven by inhomogeneous surface stress. And interaction with pre-existing defects led to the slowdown of the transformation front inside the nanoparticles. Particle coalescence, assisted by oxygen-induced surface migration at T ≥ 300°C, also played a critical role. Thus by studying transformation in the Pt ICNPs at high time and spatial resolution, we obtain critical insights into the transformation mechanisms in strained Pt nanoparticles.

A power converter capable of converting the 48 V DC output of a photovoltaic panel into 120 V AC at up to 400 W has been demonstrated in a 40 cu. cm. (2.4 cu. in.) module, for a power density of greater than 160 W/cu. in. The module is enabled by the use of GaN and AlGaN field effect transistors (FETs) and diodes operating at higher power densities and higher switching frequencies than conventional silicon power devices. Typical photovoltaic panel converter/inverters have power densities ranging from 3.5-5.0 W/cu. in. and often make use of bulky, low frequency transformers. By using wide- and ultra-wide-bandgap switching devices, the operating frequency has been increased to 150 kHz, eliminating the need to use low frequency charge and current storage elements. The resulting size reduction demonstrates the significant possibilities that the adoption of GaN and AlGaN devices housed in small, 3D printed packages offers in the field of power electronics.

In order to determine how material characteristics percolate up to system-level improvements in power dissipation for different material systems and device types, we have developed an optimization tool for power diodes. This tool minimizes power dissipation in a diode for a given system operational regime (reverse voltage, forward current density, frequency, duty cycle, and temperature) for a variety of device types and materials. We have carried out diode optimizations for a wide range of system operating points to determine the regimes for which certain power diode materials/devices are favored. In this work, we present results comparing state-of-the-art Si and SiC merged PiN Schottky (MPS) diodes to vertical GaN (v-GaN) PiN diodes and as-yet undeveloped v-GaN Schottky barrier diodes (SBDs). The results of this work show that for all conditions tested, SiC MPS and v-GaN PiN diodes are preferred over Si MPS diodes. v-GaN PiN diodes are preferred over SiC MPS diodes for high-voltage / moderate-frequency operation with the limits of the v-GaN PiN preferred regime, increasing with increasing forward current density. If a v-GaN SBD diode were available, it would be preferred over all other devices at low to moderate voltages, for all frequencies from 100 Hz to 1 MHz.

Sophisticated cyber attacks by state-sponsored and criminal actors continue to plague government and industrial infrastructure. Intuitively, partitioning cyber systems into survivable, intrusion tolerant compartments is a good idea. This prevents witting and unwitting insiders from moving laterally and reaching back to their command and control (C2) servers. However, there is a lack of artifacts that can predict the effectiveness of this approach in a realistic setting. We extend earlier work by relaxing simplifying assumptions and providing a new attacker-facing metric. In this article, we propose new closed-form mathematical models and a discrete time simulation to predict three critical statistics: probability of compromise, probability of external host compromise and probability of reachback. The results of our new artifacts agree with one another and with previous work, which suggests they are internally valid and a viable method to evaluate the effectiveness of cyber zone defense.

In order to determine how material characteristics percolate up to system-level improvements in power dissipation for different material systems and device types, we have developed an optimization tool for power diodes. This tool minimizes power dissipation in a diode for a given system operational regime (reverse voltage, forward current density, frequency, duty cycle, and temperature) for a variety of device types and materials. We have carried out diode optimizations for a wide range of system operating points to determine the regimes for which certain power diode materials/devices are favored. In this work, we present results comparing state-of-the-art Si and SiC merged PiN Schottky (MPS) diodes to vertical GaN (v-GaN) PiN diodes and as-yet undeveloped v-GaN Schottky barrier diodes (SBDs). The results of this work show that for all conditions tested, SiC MPS and v-GaN PiN diodes are preferred over Si MPS diodes. v-GaN PiN diodes are preferred over SiC MPS diodes for high-voltage / moderate-frequency operation with the limits of the v-GaN PiN preferred regime, increasing with increasing forward current density. If a v-GaN SBD diode were available, it would be preferred over all other devices at low to moderate voltages, for all frequencies from 100 Hz to 1 MHz.

Extensive all-atom molecular dynamics calculations on the water–squalane interface for nine different loadings with sorbitan monooleate (SPAN80), at T = 300 K, are analyzed for the surface tension equation of state, desorption free-energy profiles as they depend on loading, and to evaluate escape times for adsorbed SPAN80 into the bulk phases. These results suggest that loading only weakly affects accommodation of a SPAN80 molecule by this squalane–water interface. Specifically, the surface tension equation of state is simple through the range of high tension to high loading studied, and the desorption free-energy profiles are weakly dependent on loading here. The perpendicular motion of the centroid of the SPAN80 headgroup ring is well-described by a diffusional model near the minimum of the desorption free-energy profile. Lateral diffusional motion is weakly dependent on loading. Escape times evaluated on the basis of a diffusional model and the desorption free energies are 7 × 10^-2 s (into the squalane) and 3 × 10² h (into the water). Finally, the latter value is consistent with desorption times of related lab-scale experimental work.

This chapter describes the Sandia National Laboratories (SNL) processes for feedback and improvement of radiological operations, including triennial self-assessment (TSA) and radiological process improvement reports (RPIR). This chapter applies to all radiological activities performed by Members of the Workforce (MOW). Certain operations are outside the scope of this manual and do not require self-assessments or use of the RPIR process. See the "Introduction" for exemptions to the requirements of this manual.

This chapter presents requirements and guidance for radiological posting and labeling at Sandia National Laboratories (SNL). This chapter applies to all radiological posting and labeling practices at SNL.

The role of an external field on capillary waves at the liquid-vapor interface of a dipolar fluid is investigated using molecular dynamics simulations. For fields parallel to the interface, the interfacial width squared increases linearly with respect to the logarithm of the size of the interface across all field strengths tested. The value of the slope decreases with increasing field strength, indicating that the field dampens the capillary waves. With the inclusion of the parallel field, the surface stiffness increases with increasing field strength faster than the surface tension. For fields perpendicular to the interface, the interfacial width squared is linear with respect to the logarithm of the size of the interface for small field strengths, and the surface stiffness is less than the surface tension. Above a critical field strength that decreases as the size of the interface increases, the interface becomes unstable due to the increased amplitude of the capillary waves.

Deep learning techniques have demonstrated the ability to perform a variety of object recognition tasks using visible imager data; however, deep learning has not been implemented as a means to autonomously detect and assess targets of interest in a physical security system. We demonstrate the use of transfer learning on a convolutional neural network (CNN) to significantly reduce training time while keeping detection accuracy of physical security relevant targets high. Unlike many detection algorithms employed by video analytics within physical security systems, this method does not rely on temporal data to construct a background scene; targets of interest can halt motion indefinitely and still be detected by the implemented CNN. A key advantage of using deep learning is the ability for a network to improve over time. Periodic retraining can lead to better detection and higher confidence rates. We investigate training data size versus CNN test accuracy using physical security video data. Due to the large number of visible imagers, significant volume of data collected daily, and currently deployed human in the loop ground truth data, physical security systems present a unique environment that is well suited for analysis via CNNs. This could lead to the creation of algorithmic element that reduces human burden and decreases human analyzed nuisance alarms.

There is a desire to detect and assess unmanned aerial systems (UAS) with a high probability of detection and low nuisance alarm rates in numerous fields of security. Currently available solutions rely upon exploiting electronic signals emitted from the UAS. While these methods may enable some degree of security, they fail to address the emerging domain of autonomous UAS that do not transmit or receive information during the course of a mission. We examine frequency analysis of pixel fluctuation over time to exploit the temporal frequency signature present in imagery data of UAS. This signature is present for autonomous or controlled multirotor UAS and allows for lower pixels-on-target detection. The methodology also acts as a method of assessment due to the distinct frequency signatures of UAS when examined against the standard nuisance alarms such as birds or non-UAS electronic signal emitters. The temporal frequency analysis method is paired with machine learning algorithms to demonstrate a UAS detection and assessment method that requires minimal human interaction. The use of the machine learning algorithm allows each necessary human assess to increase the likelihood of autonomous assessment, allowing for increased system performance over time.

Conventional cyber defenses require continual maintenance: virus, firmware, and software updates; costly functional impact tests; and dedicated staff within a security operations center. The conventional defenses require access to external sources for the latest updates. The whitelisted system, however, is ideally a system that can sustain itself freed from external inputs. Cyber-Physical Systems (CPS), have the following unique traits: digital commands are physically observable and verifiable; possible combinations of commands are limited and finite. These CPS traits, combined with a trust anchor to secure an unclonable digital identity (i.e., digitally unclonable function [DUF] - Patent Application #15/183,454; CodeLock), offers an excellent opportunity to explore defenses built on whitelisting approach called 'Trustworthy Design Architecture (TDA).' There exist significant research challenges in defining what are the physically verifiable whitelists as well as the criteria for cyber-physical traits that can be used as the unclonable identity. One goal of the project is to identify a set of physical and/or digital characteristics that can uniquely identify an endpoint. The measurements must have the properties of being reliable, reproducible, and trustworthy. Given that adversaries naturally evolve with any defense, the adversary will have the goal of disrupting or spoofing this process. To protect against such disruptions, we provide a unique system engineering technique, when applied to CPSs (e.g., nuclear processing facilities, critical infrastructures), that will sustain a secure operational state without ever needing external information or active inputs from cybersecurity subject-matter experts (i.e., virus updates, IDS scans, patch management, vulnerability updates). We do this by eliminating system dependencies on external sources for protection. Instead, all internal communication is actively sealed and protected with integrity, authenticity and assurance checks that only cyber identities bound to the physical component can deliver. As CPSs continue to advance (i.e., IoTs, drones, ICSs), resilient-maintenance free solutions are needed to neutralize/reduce cyber risks. TDA is a conceptual system engineering framework specifically designed to address cyber-physical systems that can potentially be maintained and operated without the persistent need or demand for vulnerability or security patch updates.

Physical unclonable functions (PUFs) are devices which are easily probed but difficult to predict. Optical PUFs have been discussed within the literature, with traditional optical PUFs typically using spatial light modulators, coherent illumination, and scattering volumes; however, these systems can be large, expensive, and difficult to maintain alignment in practical conditions. We propose and demonstrate a new kind of optical PUF based on computational imaging and compressive sensing to address these challenges with traditional optical PUFs. This work describes the design, simulation, and prototyping of this computational optical PUF (COPUF) that utilizes incoherent polychromatic illumination passing through an additively manufactured refracting optical polymer element. We demonstrate the ability to pass information through a COPUF using a variety of sampling methods, including the use of compressive sensing. The sensitivity of the COPUF system is also explored. We explore non-traditional PUF configurations enabled by the COPUF architecture. The double COPUF system, which employees two serially connected COPUFs, is proposed and analyzed as a means to authenticate and communicate between two entities that have previously agreed to communicate. This configuration enables estimation of a message inversion key without the calculation of individual COPUF inversion keys at any point in the PUF life cycle. Our results show that it is possible to construct inexpensive optical PUFs using computational imaging. This could lead to new uses of PUFs in places where electrical PUFs cannot be utilized effectively, as low cost tags and seals, and potentially as authenticating and communicating devices.

Counterfeiting or surreptitious modification of electronic systems is of increasing concern, particularly for critical infrastructure and national security systems. Such systems include avionics, medical devices, military systems, and utility infrastructure. We present experimental results from an approach to uniquely identify printed circuit boards (PCBs) on the basis of device unique variations in surface mount passive components and wire trace patterns. We also present an innovative approach for combining measurements of each of these quantities to create unique, random identifiers for each PCB and report the observed entropy, reliability, and uniqueness of the signatures. These unique signatures can be used directly for verifying the integrity and authenticity of the PCBs, or can serve as the basis for generating cryptographic keys for more secure authentication of the devices during system acquisition or field deployment. Our results indicate that the proposed approaches for measuring and combining these quantities are capable of generating high-entropy, unique signatures for PCBs. The techniques explored do not require system designers to utilize specialized manufacturing processes and implementation is low-cost.

This paper describes versions of OPAL, the Occam-Plausibility Algorithm (Farrell et al. in J Comput Phys 295:189–208, 2015) in which the use of Bayesian model plausibilities is replaced with information-theoretic methods, such as the Akaike information criterion and the Bayesian information criterion. Applications to complex systems of coarse-grained molecular models approximating atomistic models of polyethylene materials are described. All of these model selection methods take into account uncertainties in the model, the observational data, the model parameters, and the predicted quantities of interest. A comparison of the models chosen by Bayesian model selection criteria and those chosen by the information-theoretic criteria is given.

Quark nuggets are theoretical objects composed of approximately equal numbers of up, down, and strange quarks and are also called strangelets and nuclearites. They have been proposed as a candidate for dark matter, which constitutes ~85% of the universe's mass and which has been a mystery for decades. Previous efforts to detect quark nuggets assumed that the nuclear-density core interacts directly with the surrounding matter so the stopping power is minimal. Tatsumi found that quark nuggets could well exist as a ferromagnetic liquid with a ∼1012-T magnetic field. We find that the magnetic field produces a magnetopause with surrounding plasma, as the earth's magnetic field produces a magnetopause with the solar wind, and substantially increases their energy deposition rate in matter. We use the magnetopause model to compute the energy deposition as a function of quark-nugget mass and to analyze testing the quark-nugget hypothesis for dark matter by observations in air, water, and land. We conclude the water option is most promising.

Quantum Hall ferromagnetic transitions are typically achieved by increasing the Zeeman energy through in-situ sample rotation, while transitions in systems with pseudo-spin indices can be induced by gate control. We report here a gate-controlled quantum Hall ferromagnetic transition between two real spin states in a conventional two-dimensional system without any in-plane magnetic field. We show that the ratio of the Zeeman splitting to the cyclotron gap in a Ge two-dimensional hole system increases with decreasing density owing to inter-carrier interactions. Below a critical density of ~2.4 × 1010 cm-2, this ratio grows greater than 1, resulting in a ferromagnetic ground state at filling factor ν = 2. At the critical density, a resistance peak due to the formation of microscopic domains of opposite spin orientations is observed. Such gate-controlled spin-polarizations in the quantum Hall regime opens the door to realizing Majorana modes using two-dimensional systems in conventional, low-spin-orbit-coupling semiconductors.

Optical metasurfaces are regular quasi-planar nanopatterns that can apply diverse spatial and spectral transformations to light waves. However, metasurfaces are no longer adjustable after fabrication, and a critical challenge is to realise a technique of tuning their optical properties that is both fast and efficient. We experimentally realise an ultrafast tunable metasurface consisting of subwavelength gallium arsenide nanoparticles supporting Mie-type resonances in the near infrared. Using transient reflectance spectroscopy, we demonstrate a picosecond-scale absolute reflectance modulation of up to 0.35 at the magnetic dipole resonance of the metasurfaces and a spectral shift of the resonance by 30 nm, both achieved at unprecedentedly low pump fluences of less than 400 μJ cm-2. Our findings thereby enable a versatile tool for ultrafast and efficient control of light using light.

Optical nonlinearities are intimately related to the spatial symmetry of the nonlinear media. For example, the second order susceptibility vanishes for centrosymmetric materials under the dipole approximation. The latter concept has been naturally extended to the metamaterials' realm, sometimes leading to the (erroneous) hypothesis that second harmonic (SH) generation is negligible in highly symmetric meta-atoms. In this work we aim to show that such symmetric meta-atoms can radiate SH light efficiently. In particular, we investigate in-plane centrosymmetric meta-atom designs where the approximation for meta-atoms breaks down. In a periodic array this building block allows us to control the directionality of the SH radiation. We conclude by showing that the use of symmetry considerations alone allows for the manipulation of the nonlinear multipolar response of a meta-atom, resulting in e.g. dipolar, quadrupolar, or multipolar emission on demand. This is because the size of the meta-atom is comparable with the free-space wavelength, thus invalidating the dipolar approximation for meta-atoms.

The removal of silica, ubiquitous in produced and industrial waters, by novel mixed oxides is investigated in this present study. We have combined the advantage of high selectivity hydrotalcite (HTC, (Mg6Al2(OH)16(CO3)·4H2O)), with large surface area of active alumina (AA, (Al2O3)) for effective removing of the dissolved silica from cooling tower water. The batch test results indicated the combined HTC/AA is a more effective method for removing silica from CTW than using each of HTC or AA separately. The silica uptake was confirmed by Fourier transform infrared (FTIR), and Energy dispersive spectroscopy (EDS). Results indicate HTC/AA effectively removes silica from cooling tower water (CTW), even in the presence of large concentrations of competing anions, such as Cl−, NO3− HCO3−, CO32− and SO42−. The Single Path Flow Through (SPFT) tests confirmed to rapid uptake of silica by combined HTC/AA during column filtration. The experimental data of silica adsorption fit best to Freundlich isotherm model.

For synthetic aperture radar systems, missing data samples can cause severe image distortion. When multiple, coherent data collections exist and the missing data samples do not overlap between collections, there exists the possibility of replacing data samples between collections. For airborne radar, the known and unknown motions of the aircraft prevent direct data sample replacement to repair image features. This paper presents a method to calculate the necessary phase corrections to enable data sample replacement using only the collected radar data.

All grain boundaries are not equal in their predisposition for fracture due to the complex coupling between lattice geometry, interfacial structure, and mechanical properties. The ability to understand these relationships is crucial to engineer materials resilient to grain boundary fracture. Here, a methodology is presented to isolate the role of grain boundary structure on interfacial fracture properties, such as the tensile strength and work of separation, using atomistic simulations. Instead of constructing sets of grain boundary models within the misorientation/structure space by simply varying the misorientation angle around a fixed misorientation axis, the proposed method creates sets of grain boundary models by means of isocurves associated with important fracture-related properties of the adjoining lattices. Such properties may include anisotropic elastic moduli, the Schmid factor for primary slip, and the propensity for simultaneous slip on multiple slip systems. This approach eliminates the effect of lattice properties from the comparative analysis of interfacial fracture properties and thus enables the identification of structure-property relationships for grain boundaries. As an example, this methodology is implemented to study crack propagation along Ni grain boundaries. Segregated H is used as a means to emphasize differences in the selected grain boundary structures while keeping lattice properties fixed.

Computer simulations at the atomistic scale play an increasing important role in understanding the structure features, and the structure–property relationships of glass and amorphous materials. In this paper, we reviewed atomistic simulation methods ranging from first principles calculations and ab initio molecular dynamics (AIMD) simulations, to classical molecular dynamics (MD), and meso-scale kinetic Monte Carlo (KMC) simulations and their applications to study the reactions and interactions of inorganic glasses with water and the dissolution behaviors of inorganic glasses. Particularly, the use of these simulation methods in understanding the reaction mechanisms of water with oxide glasses, water–glass interfaces, hydrated porous silica gels formation, the structure and properties of multicomponent glasses, and microstructure evolution are reviewed. The advantages and disadvantageous of these simulation methods are discussed and the current challenges and future direction of atomistic simulations in glass dissolution presented.

Atomic motion at grain boundaries is essential to microstructure development, growth and stability of catalysts and other nanostructured materials. However, boundary atomic motion is often too fast to observe in a conventional transmission electron microscope (TEM) and too slow for ultrafast electron microscopy. Here, we report on the entire transformation process of strained Pt icosahedral nanoparticles (ICNPs) into larger FCC crystals, captured at 2.5 ms time resolution using a fast electron camera. Results show slow diffusive dislocation motion at nm/s inside ICNPs and fast surface transformation at μm/s. By characterizing nanoparticle strain, we show that the fast transformation is driven by inhomogeneous surface stress. And interaction with pre-existing defects led to the slowdown of the transformation front inside the nanoparticles. Particle coalescence, assisted by oxygen-induced surface migration at T ≥ 300 °C, also played a critical role. Thus by studying transformation in the Pt ICNPs at high time and spatial resolution, we obtain critical insights into the transformation mechanisms in strained Pt nanoparticles.

An experimental study of migration of tungsten in the DIII-D divertor is described, in which the outer strike point of L-mode plasmas was positioned on a toroidal ring of tungsten-coated metal inserts. Net deposition of tungsten on the divertor just outside the strike point was measured on graphite samples exposed to various plasma durations using the divertor materials evaluation system. Tungsten coverage, measured by Rutherford backscattering spectroscopy (RBS), was found to be low and nearly independent of both radius and exposure time closer to the strike point, whereas farther from the strike point the W coverage was much larger and increased with exposure time. Depth profiles from RBS show this was due to accumulation of thicker mixedmaterial deposits farther from the strike point where the plasma temperature is lower. These results are consistent with a low near-surface steady-state coverage on graphite undergoing net erosion, and continuing accumulation in regions of net deposition. This experiment provides data needed to validate, and further improve computational simulations of erosion and deposition of material on plasma-facing components and transport of impurities in magnetic fusion devices. Such simulations are underway and will be reported later.

In-cylinder reforming of injected fuel during a negative valve overlap (NVO) recompression period can be used to optimize main-cycle combustion phasing for low-load low-temperature gasoline combustion (LTGC). The objective of this work is to examine the effects of reformate composition on main-cycle engine performance. An alternate-fire sequence was used to generate a common exhaust temperature and composition boundary condition for a cycle-of-interest, with performance metrics measured for these custom cycles. NVO reformate was also separately collected using a dump-valve apparatus and characterized by both gas chromatography (GC) and photoionization mass spectroscopy (PIMS). To facilitate gas sample analysis, sampling experiments were conducted using a five-component gasoline surrogate (iso-octane, n-heptane, ethanol, 1-hexene, and toluene) that matched the molecular composition, 50% boiling point, and ignition characteristics of the research gasoline. For the gasoline, it was found that an advance of the NVO start-of-injection (SOI) led to a corresponding advance in main-period combustion phasing as the combination of longer residence times and lower amounts of liquid spray piston impingement led to a greater degree of fuel decomposition. The effect was more pronounced as the fraction of total fuel injected in the NVO period increased. Main-period combustion phasing was also found to advance as the main-period fueling decreased. Slower kinetics for leaner mixtures were offset by a combination of increased bulk-gas temperature from higher charge specific heat ratios and increased fuel reactivity due to higher charge reformate fractions.

With the end of Dennard scaling and the ever-increasing need for more efficient, faster computation, resistive switching devices (ReRAM), often referred to as memristors, are a promising candidate for next generation computer hardware. These devices show particular promise for use in an analog neuromorphic computing accelerator as they can be tuned to multiple states and be updated like the weights in neuromorphic algorithms. Modeling a ReRAM-based neuromorphic computing accelerator requires a compact model capable of correctly simulating the small weight update behavior associated with neuromorphic training. These small updates have a nonlinear dependence on the initial state, which has a significant impact on neural network training. Consequently, we propose the piecewise empirical model (PEM), an empirically derived general purpose compact model that can accurately capture the nonlinearity of an arbitrary two-terminal device to match pulse measurements important for neuromorphic computing applications. By defining the state of the device to be proportional to its current, the model parameters can be extracted from a series of voltages pulses that mimic the behavior of a device in an analog neuromorphic computing accelerator. This allows for a general, accurate, and intuitive compact circuit model that is applicable to different resistance-switching device technologies. In this work, we explain the details of the model, implement the model in the circuit simulator Xyce, and give an example of its usage to model a specific Ta / TaO x device.

We have developed and built a small porous-plug burner based on the original McKenna burner design. The new burner generates a laminar premixed flat flame for use in studies of combustion chemistry and soot formation. The size is particularly relevant for space-constrained, synchrotron-based X-ray diagnostics. In this paper, we present details of the design, construction, operation, and supporting infrastructure for this burner, including engineering attributes that enable its small size. We also present data for charactering the flames produced by this burner. These data include temperature profiles for three premixed sooting ethylene/air flames (equivalence ratios of 1.5, 1.8, and 2.1); temperatures were recorded using direct one-dimensional coherent Raman imaging. We include calculated temperature profiles, and, for one of these ethylene/air flames, we show the carbon and hydrogen content of heavy hydrocarbon species measured using an aerosol mass spectrometer coupled with vacuum ultraviolet photoionization (VUV-AMS) and soot-volume-fraction measurements obtained using laser-induced incandescence. In addition, we provide calculated mole-fraction profiles of selected gas-phase species and characteristic profiles for seven mass peaks from AMS measurements. Using these experimental and calculated results, we discuss the differences between standard McKenna burners and the new miniature porous-plug burner introduced here.

Individual donors in silicon chips are used as quantum bits with extremely low error rates. However, physical realizations have been limited to one donor because their atomic size causes fabrication challenges. Quantum dot qubits, in contrast, are highly adjustable using electrical gate voltages. This adjustability could be leveraged to deterministically couple donors to quantum dots in arrays of qubits. In this work, we demonstrate the coherent interaction of a 31P donor electron with the electron of a metal-oxide-semiconductor quantum dot. We form a logical qubit encoded in the spin singlet and triplet states of the two-electron system. We show that the donor nuclear spin drives coherent rotations between the electronic qubit states through the contact hyperfine interaction. This provides every key element for compact two-electron spin qubits requiring only a single dot and no additional magnetic field gradients, as well as a means to interact with the nuclear spin qubit.

Previous observation of EPDM and FEPM materials aged in thermo-oxidative and thermo-oxidative plus hydrolytic environments revealed an unusual trend: the degradation and disintegration of these polymers in the former case but the ability to maintain mechanical performance and shape in the latter [1]. No abnormalities were observed in the chemical (oxidation rates, FTIR spectra, solvent uptake, gel content, and weight loss vs. temperature) or physical (modulus profile) measurements that could explain these empirically observed aging differences. A second controlled aging test was conducted to verify this trend using only EPDM. Once again it was shown that thermo-oxidative conditions appear to cause more degradative damage (enhanced embrittlement) than observed for the combined thermo-oxidative plus hydrolytic environments. From these data we conclude that water may favorably interfere with normal thermo-oxidative degradation processes. This interference may occur via some combination of chemical and physical property changes in the presence of steam such as: oxidation rate and O2 permeability changes, additional sensitivity to hydrolytic damage, and/or mechanistic changes in relation to pH and hydroperoxide formation.

The high-Affinity receptor for IgE expressed on the surface of mast cells and basophils interacts with antigens, via bound IgE antibody, and triggers secretion of inflammatory mediators that contribute to allergic reactions. To understand how past inputs (memory) influence future inflammatory responses in mast cells, a microfluidic device was used to precisely control exposure of cells to alternating stimulatory and non-stimulatory inputs. We determined that the response to subsequent stimulation depends on the interval of signaling quiescence. For shorter intervals of signaling quiescence, the second response is blunted relative to the first response, whereas longer intervals of quiescence induce an enhanced second response. Through an iterative process of computational modeling and experimental tests, we found that these memory-like phenomena arise from a confluence of rapid, short-lived positive signals driven by the protein tyrosine kinase Syk; slow, long-lived negative signals driven by the lipid phosphatase Ship1; and slower degradation of Ship1 co-factors. This work advances our understanding of mast cell signaling and represents a generalizable approach for investigating the dynamics of signaling systems.

An experiment was conducted in DIII-D to compare gross tungsten (W) erosion on samples exposed to outer strike point (OSP) sweeps in L-mode plasmas for three conditions. These included two phases of resonant magnetic perturbations (RMPs), and a set with no perturbations. Upon RMP application, lobe structures indicative of strike point splitting of the OSP were evident in divertor camera data and on Langmuir probes. Gross W erosion flux, GW, inferred spectroscopically using the S/XB method applied to the 400.9 nm W-I line, was generally in the range ΓW/ΓD +,⊥ = 2 × 10-4 referenced to incident deuterium ion flux ΓD+,⊥, and was increased in the RMP cases by no more than 30% of the level observed in unperturbed discharges. A large reduction in gross erosion (50%) was observed in the private flux region at the W sample for one specific toroidal phase of the RMP field.

Strong stability preserving (SSP) time discretizations preserve the monotonicity properties satisfied by the spatial discretization when coupled with the first order forward Euler, under a certain time-step restriction. The search for high order strong stability preserving time-stepping methods with high order and large allowable time-step has been an active area of research. It is known that implicit SSP Runge–Kutta methods exist only up to sixth order; however, if we restrict ourselves to solving only linear autonomous problems, the order conditions simplify and we can find implicit SSP Runge–Kutta methods of any linear order. In the current work we find implicit SSP Runge–Kutta methods with high linear order pl i n≤ 9 and nonlinear orders p= 2, 3, 4, that are optimal in terms of allowable SSP time-step. Next, we formulate a novel optimization problem for implicit–explicit (IMEX) SSP Runge–Kutta methods and find optimized IMEX SSP Runge–Kutta pairs that have high linear order pl i n≤ 7 and nonlinear orders up to p= 4. We also find implicit methods with large linear stability regions that pair with known explicit SSP Runge–Kutta methods. These methods are then tested on sample problems to demonstrate the sharpness of the SSP coefficient and the typical behavior of these methods on test problems.

We generate a wide range of models of proppant-packed fractures using discrete element simulations, and measure fracture conductivity using finite element flow simulations. This allows for a controlled computational study of proppant structure and its relationship to fracture conductivity and stress in the proppant pack. For homogeneous multi-layered packings, we observe the expected increase in fracture conductivity with increasing fracture aperture, while the stress on the proppant pack remains nearly constant. This is consistent with the expected behavior in conventional proppant-packed fractures, but the present work offers a novel quantitative analysis with an explicit geometric representation of the proppant particles. In single-layered packings (i.e. proppant monolayers), there is a drastic increase in fracture conductivity as the proppant volume fraction decreases and open flow channels form. However, this also corresponds to a sharp increase in the mechanical stress on the proppant pack, as measured by the maximum normal stress relative to the side crushing strength of typical proppant particles. We also generate a variety of computational geometries that resemble highly heterogeneous proppant packings hypothesized to form during channel fracturing. In some cases, these heterogeneous packings show drastic improvements in conductivity with only moderate increase in the stress on the proppant particles, suggesting that in certain applications these structures are indeed optimal. We also compare our computer-generated structures to micro computed tomography imaging of a manually fractured laboratory-scale shale specimen, and find reasonable agreement in the geometric characteristics.

Analog quantum simulators (AQS) will likely be the first nontrivial application of quantum technology for predictive simulation. However, there remain questions regarding the degree of confidence that can be placed in the results of AQS since they do not naturally incorporate error correction. Specifically, how do we know whether an analog simulation of a quantum model will produce predictions that agree with the ideal model in the presence of inevitable imperfections? At the same time there is a widely held expectation that certain quantum simulation questions will be robust to errors and perturbations in the underlying hardware. Resolving these two points of view is a critical step in making the most of this promising technology. In this work we formalize the notion of AQS reliability by determining sensitivity of AQS outputs to underlying parameters, and formulate conditions for robust simulation. Our approach naturally reveals the importance of model symmetries in dictating the robust properties. To demonstrate the approach, we characterize the robust features of a variety of quantum many-body models.

Algebraic multigrid (AMG) preconditioners are considered for discretized systems of partial differential equations (PDEs) where unknowns associated with different physical quantities are not necessarily colocated at mesh points. Specifically, we investigate a Q2−Q1 mixed finite element discretization of the incompressible Navier–Stokes equations where the number of velocity nodes is much greater than the number of pressure nodes. Consequently, some velocity degrees of freedom (DOFs) are defined at spatial locations where there are no corresponding pressure DOFs. Thus, AMG approaches leveraging this colocated structure are not applicable. This paper instead proposes an automatic AMG coarsening that mimics certain pressure/velocity DOF relationships of the Q2−Q1 discretization. The main idea is to first automatically define coarse pressures in a somewhat standard AMG fashion and then to carefully (but automatically) choose coarse velocity unknowns so that the spatial location relationship between pressure and velocity DOFs resembles that on the finest grid. To define coefficients within the intergrid transfers, an energy minimization AMG (EMIN-AMG) is utilized. EMIN-AMG is not tied to specific coarsening schemes and grid transfer sparsity patterns, and so it is applicable to the proposed coarsening. Numerical results highlighting solver performance are given on Stokes and incompressible Navier–Stokes problems.

A large body of experimental work has established that athermal colloid/polymer mixtures undergo a sequence of transitions from a disordered fluid state to a colloidal crystal to a second disordered phase with increasing polymer concentration. These transitions are driven by polymer-mediated interparticle attraction, which is a function of both the polymer density and size. It has been posited that the disordered state at high polymer density is a consequence of strong interparticle attractions that kinetically inhibit the formation of the colloidal crystal, i.e., the formation of a non-equilibrium gel phase interferes with crystallization. Here we use molecular dynamics simulations and density functional theory on polymers and nanoparticles (NPs) of comparable size and show that the crystal-disordered phase coexistence at high polymer density for sufficiently long chains corresponds to an equilibrium thermodynamic phase transition. While the crystal is, indeed, stabilized at intermediate polymer density by polymer-induced intercolloid attractions, it is destabilized at higher densities because long chains lose significant configurational entropy when they are forced to occupy all of the crystal voids. Our results are in quantitative agreement with existing experimental data and show that, at least in the nanoparticle limit of sufficiently small colloidal particles, the crystal phase only has a modest range of thermodynamic stability.

Molecular structures of kerogen control hydrocarbon production in unconventional reservoirs. Significant progress has been made in developing model representations of various kerogen structures. These models have been widely used for the prediction of gas adsorption and migration in shale matrix. However, using density functional perturbation theory (DFPT) calculations and vibrational spectroscopic measurements, we here show that a large gap may still remain between the existing model representations and actual kerogen structures, therefore calling for new model development. Using DFPT, we calculated Fourier transform infrared (FTIR) spectra for six most widely used kerogen structure models. The computed spectra were then systematically compared to the FTIR absorption spectra collected for kerogen samples isolated from Mancos, Woodford and Marcellus formations representing a wide range of kerogen origin and maturation conditions. Limited agreement between the model predictions and the measurements highlights that the existing kerogen models may still miss some key features in structural representation. A combination of DFPT calculations with spectroscopic measurements may provide a useful diagnostic tool for assessing the adequacy of a proposed structural model as well as for future model development. This approach may eventually help develop comprehensive infrared (IR)-fingerprints for tracing kerogen evolution.

A system is presented that is capable of measuring subnanosecond reverse recovery times of diodes in wide-bandgap materials over a wide range of forward biases (0 - 1 A) and reverse voltages (0 - 10 kV). The system utilizes the step recovery technique and comprises a cable pulser based on a silicon (Si) Photoconductive Semiconductor Switch (PCSS) triggered with an Ultrashort Pulse Laser, a pulse charging circuit, a diode biasing circuit, and resistive and capacitive voltage monitors. The PCSS-based cable pulser transmits a 130 ps rise time pulse down a transmission line to a capacitively coupled diode, which acts as the terminating element of the transmission line. The temporal nature of the pulse reflected by the diode provides the reverse recovery characteristics of the diode, measured with a high bandwidth capacitive probe integrated into the cable pulser. This system was used to measure the reverse recovery times (including the creation and charging of the depletion region) for two Avogy gallium nitride diodes; the initial reverse recovery time was found to be 4 ns and varied minimally over reverse biases of 50-100 V and forward current of 1-100 mA.

In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic Amplitude Versus Angle and Controlled-Source Electromagnetic data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis and Adaptive Metropolis samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and Controlled-Source Electromagnetic data. The multi-chain Markov-chain Monte Carlo is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic Amplitude Versus Angle and Controlled-Source Electromagnetic joint inversion provides better estimation of reservoir saturations than the seismic Amplitude Versus Angle only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated — reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability.

We demonstrate a capability of deterministic doping at the single atom level using a combination of direct write focused ion beam and solid-state ion detectors. The focused ion beam system can position a single ion to within 35 nm of a targeted location and the detection system is sensitive to single low energy heavy ions. This platform can be used to deterministically fabricate single atom devices in materials where the nanostructure and ion detectors can be integrated, including donor-based qubits in Si and color centers in diamond.

The porosity of clay aggregates is an important property governing chemical reactions and fluid flow in low-permeability geologic formations and clay-based engineered barrier systems. Pore spaces in clays include interlayer and interparticle pores. Under compaction and dewatering, the size and geometry of such pore spaces may vary significantly (sub-nanometer to microns) depending on ambient physical and chemical conditions. Here we report a molecular dynamics simulation method to construct a complex and realistic clay-like nanoparticle aggregate with interparticle pores and grain boundaries. The model structure is then used to investigate the effect of dewatering and water content on micro-porosity of the aggregates. The results suggest that slow dewatering would create more compact aggregates compared to fast dewatering. Furthermore, the amount of water present in the aggregates strongly affects the particle-particle interactions and hence the aggregate structure. Detailed analyses of particle-particle and water-particle interactions provide a molecular-scale view of porosity and texture development of the aggregates. The simulation method developed here may also aid in modeling the synthesis of nanostructured materials through self-assembly of nanoparticles.

This paper presents a review of the main electrical components that are expected to be present in marine renewable energy arrays. The review is put in context by appraising the current needs of the industry and identifying the key components required in both device and array-scale developments. For each component, electrical, mechanical and cost considerations are discussed; with quantitative data collected during the review made freely available for use by the community via an open access online repository. This data collection updates previous research and addresses gaps specific to emerging offshore technologies, such as marine and floating wind, and provides a comprehensive resource for the techno-economic assessment of offshore energy arrays.

Image processing and stereological techniques were used to characterize the heterogeneity of composite propellant and inform a predictive burn rate model. Composite propellant samples made up of ammonium perchlorate (AP), hydroxyl-terminated polybutadiene (HTPB), and aluminum (Al) were faced with an ion mill and imaged with a scanning electron microscope (SEM) and x-ray tomography (micro-CT). Properties of both the bulk and individual components of the composite propellant were determined from a variety of image processing tools. An algebraic model, based on the improved Beckstead-Derr-Price model developed by Cohen and Strand, was used to predict the steady-state burning of the aluminized composite propellant. In the presented model the presence of aluminum particles within the propellant was introduced. The thermal effects of aluminum particles are accounted for at the solid-gas propellant surface interface and aluminum combustion is considered in the gas phase using a single global reaction. Properties derived from image processing were used directly as model inputs, leading to a sample-specific predictive combustion model.

Anodic stripping voltammetry (ASV) is an analysis technique that permits the selective and quantitative analysis of metal ion species in solution. It is most commonly applied in neutral to acidic electrolyte largely due to inherent metal ion solubility. Bismuth (Bi) is a common film used for ASV due to its good sensitivity, overall stability and insensitivity to O2. ASV, utilizing a Bi film, along with cadmium (Cd) and lead (Pb) as the plating mediators, has recently been adapted to determine zinc (Zn) concentrations in highly alkaline environments (30 % NaOH or 35 % M KOH). Successful analysis of Zn in alkaline relies on the ability of the hydroxide to form soluble metal anion species, such as Bi(OH)4 − and Zn(OH)4 2−. Here, we look to extend this technique to detect and quantify copper (Cu) ions in these highly basic electrolytes. However, in general, the use of ASV to detect and quantify Cu ion concentrations is notoriously difficult as the Cu stripping peak potential overlays with that of Bi from the common Bi film electrode. Here, an ASV method for determining Cu concentration in alkaline solutions is developed utilizing Pb as a deposition mediator. As such, it was found that when analyzing Cu solutions in the presence of Pb, the stripping voltammetry curves present separate and defined Cu stripping peaks. Different analyzes were made to find the best stripping voltammetry performance conditions. As such, an accumulation time of 5 minutes, an accumulation potential of≤−1.45 V vs. Hg/HgO, and a concentration of 35 wt% KOH were determined to be the conditions that presented the best ASV results. Utilizing these conditions, calibration curves in the presence of 5.0 ppm Pb showed the best linear stripping signal correlation with an r-squared value of 0.991 and a limit of detection (LOD) of 0.67 ppm. These results give way to evaluating Cu concentrations using ASV in aqueous alkaline solutions.

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In this study, we employ a numerical model to compare the performance of a number of wave energy converter control strategies. The controllers selected for evaluation span a wide range in their requirements for implementation. Each control strategy is evaluated using a single numerical model with a set of sea states to represent a deployment site off the coast of Newport, OR. A number of metrics, ranging from power absorption to kinematics, are employed to provide a comparison of each control strategy's performance that accounts for both relative benefits and costs. The results show a wide range of performances from the different controllers and highlight the need for a holistic design approach which considers control design as a parallel component within the larger process WEC design.

This document contains 31 use cases generated from the model contained in Rational Software Architect.

In this paper, the experimental results from long-term solubility experiments on micro crystalline neodymium hydroxide, Nd(OH)₃(micro cr), in high ionic strength solutions at 298.15 K under well-constrained conditions are presented. The starting material was synthesized according to a well-established method in the literature. In contrast with the previous studies in which hydrogen ion concentrations in experiments were adjusted with addition of either an acid or a base, the hydrogen ion concentrations in our experiments are controlled by the dissolution of Nd(OH)₃(micro cr), avoiding the possibility of phase change.

We have developed an ambient temperature, SiO₂/Si wafer - scale process for Josephson junctions based on Nb electrodes and Ta x N barriers with tunable electronic properties. The films are fabricated by magnetron sputtering. The electronic properties of the Ta_xN barriers are controlled by adjusting the nitrogen flow during sputtering. This technology offers a scalable alternative to the more traditional junctions based on AlO_x barriers for low - power, high - performance computing.

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This paper describes Sandia National Laboratories' structure, mission, and other directives.

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This report assesses seismic interference generated by a tethered aerostat. The study was motivated by a planned aerostat deployment within the footprint of the Dry Alluvium Geology seismic network. No evidence was found for seismic interference generated by the aerostat, and thus the e ects on the Dry Alluvium Geology sensors will be negligible.

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Supercritical CO₂ (sCO₂) is a fluid of interest for advanced power cycles that can reach thermal to electric energy conversion efficiencies of 50% or higher. Of particular interest for fossil-fired natural gas is the Allam cycle that captures nearly all CO₂ emissions and exports it as a fluid stream where it may be of value. The combustion process conditions are unlike any before realized with 90-95% CO₂ concentration, temperatures around 1000°C, and pressures near 300 bar. This work outlines the experimental feasibility of flow measurements to acquire the first known data in pure sCO₂ at similar but reduced temperature and pressure conditions.

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The Aquifer Pumping Test Report for the Burn Site Groundwater (BSG) Area of Concern is being submitted by National Technology and Engineering Solutions of Sandia, LLC and the U.S. Department of Energy (DOE)/National Nuclear Security Administration to describe the results of the aquifer pumping test program and related field activities that were completed at the BSG Area of Concern. This report summarizes the results of the field work and data analyses, and is being submitted to the New Mexico Environment Department (NMED) Hazardous Waste Bureau, as required by the April 14, 2016 letter, Summary of Agreements and Proposed Milestones Pursuant to the Meeting of July 20, 2015, (NMED April 2016).

The purpose of the work presented in this memo was to calibrate the Sierra material model Multilinear Elastic-Plastic Hardening Model with Failure, (MLEP-Fail), for 1/8 inch thick plate of 13-8 PH steel in the H1150 condition. Material testing was pursued first, followed by the actual calibration.

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Cyber-secure, resilient energy is paramount to the prosperity of the United States. As the experience and sophistication of cyber adversaries grow, so too must the US power system’s defenses, situational awareness, and response and recovery strategies. Traditionally, power systems were operated with dedicated communication channels to large generators and utility-owned assets but now there is greater reliance on photovoltaic (PV) systems to provide power generation. PV systems often communicate to utilities, aggregators, and other grid operators over the public internet so the power system attack surface has significantly expanded. At the same time, solar energy systems are equipped with a range of grid-support functions, that—if controlled or programmed improperly—present a risk of power system disturbances. This document is a five-year roadmap intended to chart a path for improving cyber security for communication-enabled PV systems with clear roles and responsibilities for government, standards development organizations, PV vendors, and grid operators.

Analysis with the ParaChoice model addresses three barriers from the VTO Multi-Year Program Plan: availability of alternative fuels and electric charging station infrastructure, availability of AFVs and electric drive vehicles, and consumer reluctance to purchase new technologies. In this fiscal year, we first examined the relationship between the availability of alternative fuels and station infrastructure. Specifically, we studied how electric vehicle charging infrastructure affects the ability of EVs to compete with vehicles that rely on mature, conventional petroleum-based fuels. Second, we studied how the availability of less costly AFVs promotes their representation in the LDV fleet. Third, we used ParaChoice trade space analyses to help inform which consumers are reluctant to purchase new technologies. Last, we began analysis of impacts of alternative energy technologies on Class 8 trucks to isolate those that may most efficaciously advance HDV efficiency and petroleum use reduction goals.

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This report summarizes collaborative efforts between Secure Scalable Microgrid and Korean Institute of Energy Research team members . The efforts aim to advance microgrid research and development towards the efficient utilization of networked microgrids . The collaboration resulted in the identification of experimental and real time simulation capabilities that may be leveraged for networked microgrids research, development, and demonstration . Additional research was performed to support the demonstration of control techniques within real time simulation and with hardware in the loop for DC microgrids .

This report describes a new single-station analysis method to estimate the dispersion and uncer- tainty of seismic surface waves using the multiwavelet transform. Typically, when estimating the dispersion of a surface wave using only a single seismic station, the seismogram is decomposed into a series of narrow-band realizations using a bank of narrow-band filters. By then enveloping and normalizing the filtered seismograms and identifying the maximum power as a function of frequency, the group velocity can be estimated if the source-receiver distance is known. However, using the filter bank method, there is no robust way to estimate uncertainty. In this report, I in- troduce a new method of estimating the group velocity that includes an estimate of uncertainty. The method is similar to the conventional filter bank method, but uses a class of functions, called Slepian wavelets, to compute a series of wavelet transforms of the data. Each wavelet transform is mathematically similar to a filter bank, however, the time-frequency tradeoff is optimized. By taking multiple wavelet transforms, I form a population of dispersion estimates from which stan- dard statistical methods can be used to estimate uncertainty. I demonstrate the utility of this new method by applying it to synthetic data as well as ambient-noise surface-wave cross-correlelograms recorded by the University of Nevada Seismic Network.

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High-mobility two-dimensional (2D) holes residing in a Ge quantum well are a new electronic system with potentials in quantum computing and spintronics. Since for any electronic material, the effective mass and the g factor are two fundamental material parameters that determine the material response to electric and magnetic fields, measuring these two parameters in this material system is thus an important task that needs to be completed urgently. Because of the quantum confinement in the crystal growth direction (z), the biaxial strain of epitaxial Ge on SiGe, and the valance band nature, both the effective mass and the g factor can show very strong anisotropy. In particular, the in-plane g factor (g_ip) can be vanishingly small while the perpendicular g factor (g_z) can be much larger than 2. Here we report the measurement of g_ip at very low hole densities using in-plane magneto-resistance measurement performed at the NHMFL.

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In the California Industrial General Permit (IGP) 2014-0057-DWQ for storm water monitoring, effective July 1, 2015, there are 21 contaminants that have been assigned NAL (Numeric Action Level) values, both annual and instantaneous.

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An investigation of polyurethane foam filled with known flame retardant fillers including hydroxides, melamine, phosphate-containing compounds, and melamine phosphates was carried out to produce a low-cost material with high flame retardant efficiency. The impact of flame retardant fillers on the physical properties such a s composite foam density, glass transition temperature, storage modulus, and thermal expansion of composite foams was investigated with the goal of synthesizing a robust rigid foam with excellent flame retardant properties.

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Simulating HPC systems is a difficult task and the emergence of “Beyond CMOS” architectures and execution models will increase that difficulty. This document presents a “tutorial” on some of the simulation challenges faced by conventional and non-conventional architectures (Section 1) and goals and requirements for simulating Beyond CMOS systems (Section 2). These provide background for proposed short- and long-term roadmaps for simulation efforts at Sandia (Sections 3 and 4). Additionally, a brief explanation of a proof-of-concept integration of a Beyond CMOS architectural simulator is presented (Section 2.3).

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The goal of this session is to introduce five security system design concepts that will enable the development of an integrated security system and will increase the likelihood of effective security management. As a regulator, it’s important to be aware of these design concepts so that when you inspect or evaluate a facility’s security system, you can ensure that these concepts have been applied.

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