The first-principles computation of the surfaces of metals is typically accomplished through slab calculations of finite thickness. The extraction of a convergent surface formation energy from slab calculations is dependent upon defining an appropriate bulk reference energy. I describe a method for an independently computed, slab-consistent bulk reference that leads to convergent surface formation energies from slab calculations that also provides realistic uncertainties for the magnitude of unavoidable nonlinear divergence in the surface formation energy with slab thickness. The accuracy is demonstrated on relaxed, unreconstructed low-index aluminum surfaces with slabs with up to 35 layers.
The stability of low-index platinum surfaces and their electronic properties is investigated with density functional theory, toward the goal of understanding the surface structure and electron emission, and identifying precursors to electrical breakdown, on nonideal platinum surfaces. Propensity for electron emission can be related to a local work function, which, in turn, is intimately dependent on the local surface structure. The (1×N) missing row reconstruction of the Pt(110) surface is systematically examined. The (1×3) missing row reconstruction is found to be the lowest in energy, with the (1×2) and (1×4) slightly less stable. In the limit of large (1×N) with wider (111) nanoterraces, the energy accurately approaches the asymptotic limit of the infinite Pt(111) surface. This suggests a local energetic stability of narrow (111) nanoterraces on free Pt surfaces that could be a common structural feature in the complex surface morphologies, leading to work functions consistent with those on thermally grown Pt substrates.
Ashbaugh, Henry S.; Asthagiri, Dilipkumar; Beck, Thomas L.; Rempe, Susan R.
Lawrence Pratt’s career following completion of his Ph.D. at the University of Illinois Urbana Champaign has taken him from Harvard University, to the University of California, Berkeley, and Los Alamos National Laboratory. Most recently, he joined the faculty of the Department of Chemical and Biomolecular Engineering at Tulane University in 2008. Over his career Lawrence has been a leader in theoretical physical chemistry, making influential contributions to a number of areas including the theory of the hydrophobic effect, the development of transition path sampling, contributions to orbital free density functional theory, and the theory of liquids and solutions.
Blast traumatic brain injury is ubiquitous in modern military conflict with significant morbidity and mortality. Yet the mechanism by which blast overpressure waves cause specific intracranial injury in humans remains unclear. Reviewing of both the clinical experience of neurointensivists and neurosurgeons who treated service members exposed to blast have revealed a pattern of injury to cerebral blood vessels, manifested as subarachnoid hemorrhage, pseudoaneurysm, and early diffuse cerebral edema. Additionally, a seminal neuropathologic case series of victims of blast traumatic brain injury (TBI) showed unique astroglial scarring patterns at the following tissue interfaces: subpial glial plate, perivascular, periventricular, and cerebral gray-white interface. The uniting feature of both the clinical and neuropathologic findings in blast TBI is the co-location of injury to material interfaces, be it solid-fluid or solid-solid interface. This motivates the hypothesis that blast TBI is an injury at the intracranial mechanical interfaces. In order to investigate the intracranial interface dynamics, we performed a novel set of computational simulations using a model human head simplified but containing models of gyri, sulci, cerebrospinal fluid (CSF), ventricles, and vasculature with high spatial resolution of the mechanical interfaces. Simulations were performed within a hybrid Eulerian—Lagrangian simulation suite (CTH coupled via Zapotec to Sierra Mechanics). Because of the large computational meshes, simulations required high performance computing resources. Twenty simulations were performed across multiple exposure scenarios—overpressures of 150, 250, and 500 kPa with 1 ms overpressure durations—for multiple blast exposures (front blast, side blast, and wall blast) across large variations in material model parameters (brain shear properties, skull elastic moduli). All simulations predict fluid cavitation within CSF (where intracerebral vasculature reside) with cavitation occurring deep and diffusely into cerebral sulci. These cavitation events are adjacent to high interface strain rates at the subpial glial plate. Larger overpressure simulations (250 and 500kPa) demonstrated intraventricular cavitation—also associated with adjacent high periventricular strain rates. Additionally, models of embedded intraparenchymal vascular structures—with diameters as small as 0.6 mm—predicted intravascular cavitation with adjacent high perivascular strain rates. The co-location of local maxima of strain rates near several of the regions that appear to be preferentially damaged in blast TBI (vascular structures, subpial glial plate, perivascular regions, and periventricular regions) suggest that intracranial interface dynamics may be important in understanding how blast overpressures leads to intracranial injury.
High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.
We analyze the dislocation content of grain boundary (GB) phase junctions, i.e., line defects separating two different GB phases coexisting on the same GB plane. While regular GB disconnections have been characterized for a variety of interfaces, GB phase junctions formed by GBs with different structures and different numbers of excess atoms have not been previously studied. We apply a general Burgers circuit analysis to calculate the Burgers vectors b of junctions in two ς5 Cu boundaries previously simulated with molecular dynamics. The Burgers vectors of these junctions cannot be described by the displacement shift complete (DSC) lattice alone. We show that, in general, the normal component of b is not equal to the difference in the GB excess volumes but contains another contribution from the numbers of GB atoms per unit area ΔN∗ required to transform one GB phase into another. In the boundaries studied, the latter component dominates and even changes the sign of b. We derive expressions for the normal and tangential components of b in terms of the DSC lattice vectors and the non-DSC part due to ΔN∗ and additional GB excess properties, including excess volume and shears. These expressions provide a connection between GB phase transformations driven by the GB free energy difference and the motion of GB junctions under applied normal and shear stresses. The proposed analysis quantifies b and therefore makes it possible to calculate the elastic part of the energy of these defects, evaluate their contribution to the nucleation barrier during GB phase transformations, and treat elastic interactions with other defects.
Multivariate time series are used in many science and engineering domains, including health-care, astronomy, and high-performance computing. A recent trend is to use machine learning (ML) to process this complex data and these ML-based frameworks are starting to play a critical role for a variety of applications. However, barriers such as user distrust or difficulty of debugging need to be overcome to enable widespread adoption of such frameworks in production systems. To address this challenge, we propose a novel explainability technique, CoMTE, that provides counterfactual explanations for supervised machine learning frameworks on multivariate time series data. Using various machine learning frameworks and data sets, we compare CoMTE with several state-of-the-art explainability methods and show that we outperform existing methods in comprehensibility and robustness. We also show how CoMTE can be used to debug machine learning frameworks and gain a better understanding of the underlying multivariate time series data.
High-performance computing (HPC) researchers have long envisioned scenarios where application workflows could be improved through the use of programmable processing elements embedded in the network fabric. Recently, vendors have introduced programmable Smart Network Interface Cards (SmartNICs) that enable computations to be offloaded to the edge of the network. There is great interest in both the HPC and high-performance data analytics (HPDA) communities in understanding the roles these devices may play in the data paths of upcoming systems. This paper focuses on characterizing both the networking and computing aspects of NVIDIA’s new BlueField-2 SmartNIC when used in a 100Gb/s Ethernet environment. For the networking evaluation we conducted multiple transfer experiments between processors located at the host, the SmartNIC, and a remote host. These tests illuminate how much effort is required to saturate the network and help estimate the processing headroom available on the SmartNIC during transfers. For the computing evaluation we used the stress-ng benchmark to compare the BlueField-2 to other servers and place realistic bounds on the types of offload operations that are appropriate for the hardware. Our findings from this work indicate that while the BlueField-2 provides a flexible means of processing data at the network’s edge, great care must be taken to not overwhelm the hardware. While the host can easily saturate the network link, the SmartNIC’s embedded processors may not have enough computing resources to sustain more than half the expected bandwidth when using kernel-space packet processing. From a computational perspective, encryption operations, memory operations under contention, and on-card IPC operations on the SmartNIC perform significantly better than the general-purpose servers used for comparisons in our experiments. Therefore, applications that mainly focus on these operations may be good candidates for offloading to the SmartNIC.
Plasmas formed in microscale gaps at DC and plasmas formed at radiofrequency (RF) both deviate in behavior compared to the classical Paschen curve, requiring lower voltage to achieve breakdown due to unique processes and dynamics, such as field emission and controlled rates of electron/ion interactions. Both regimes have been investigated independently, using high precision electrode positioning systems for microscale gaps or large, bulky emitters for RF. However, no comprehensive study of the synergistic phenomenon between the two exists. The behavior in such a combined system has the potential to reach sub-10 V breakdown, which combined with the unique electrical properties of microscale plasmas could enable a new class of RF switches, limiters and tuners.This work describes the design and fabrication of novel on-wafer microplasma devices with gaps as small as 100 nm to be operated at GHz frequencies. We used a dual-sacrificial layer process to create devices with microplasma gaps integrated into RF compatible 50 Ω coplanar waveguide transmission lines, which will allow this coupled behaviour to be studied for the first time. These devices are modelled using conventional RF simulations as well as the Sandia code, EMPIRE, which is capable of modelling the breakdown and formation of plasma in microscale gaps driven by high frequencies. Synchronous evaluation of the modelled electrical and breakdown behaviour is used to define device structures, predict behaviour and corroborate results. We further report preliminary independent testing of the microscale gap and RF behaviour. DC testing shows modified-Paschen curve behaviour for plasma gaps at and below four microns, demonstrating decreased breakdown voltage with reduced gap size. Additionally, preliminary S-parameter measurements of as-prepared and connectorized devices have elucidated RF device behaviour. Together, these results provide baseline data that enables future experiments as well as discussion of projected performance and applications for these unique devices.
Studies of size effects on thermal conductivity typically necessitate the fabrication of a comprehensive film thickness series. In this Letter, we demonstrate how material fabricated in a wedged geometry can enable similar, yet higher-throughput measurements to accelerate experimental analysis. Frequency domain thermoreflectance (FDTR) is used to simultaneously determine the thermal conductivity and thickness of a wedged silicon film for thicknesses between 100 nm and 17 μm by considering these features as fitting parameters in a thermal model. FDTR-deduced thicknesses are compared to values obtained from cross-sectional scanning electron microscopy, and corresponding thermal conductivity measurements are compared against several thickness-dependent analytical models based upon solutions to the Boltzmann transport equation. Our results demonstrate how the insight gained from a series of thin films can be obtained via fabrication of a single sample.
The rapidly increasing use of electronics in high-radiation environments and the continued evolution in transistor architectures and materials demand improved methods to characterize the potential damaging effects of radiation on device performance. Here, electron-beam-induced current is used to map hot-carrier transport in model metal-oxide semiconductor field-effect transistors irradiated with a 300 KeV focused He+ beam as a localized line spanning across the gate and bulk Si. By correlating the damage to the electronic properties and combining these results with simulations, the contribution of spatially localized radiation damage on the device characteristics is obtained. This identified damage, caused by the He+ beam, is attributed to localized interfacial Pb centers and delocalized positive fixed-charges, as surmised from simulations. Comprehension of the long-term interaction and mobility of radiation-induced damage are key for future design of rad-hard devices.
Using the local moment counter charge (LMCC) method to accurately represent the asymptotic electrostatic boundary conditions within density functional theory supercell calculations, we present a comprehensive analysis of the atomic structure and energy levels of point defects in cubic silicon carbide (3C-SiC). Finding that the classical long-range dielectric screening outside the supercell induced by a charged defect is a significant contributor to the total energy. we describe and validate a modified Jost screening model to evaluate this polarization energy. This leads to bulk-converged defect levels in finite size supercells. With the LMCC boundary conditions and a standard Perdew-Burke-Ernzerhof (PBE) exchange correlation functional, the computed defect level spectrum exhibits no band gap problem: the range of defect levels spans ∼2.4eV, an effective defect band gap that agrees with the experimental band gap. Comparing with previous literature, our LMCC-PBE defect results are in consistent agreement with the hybrid-exchange functional results of Oda et al. [J. Chem. Phys. 139, 124707 (2013)JCPSA60021-960610.1063/1.4821937] rather than their PBE results. The difference with their PBE results is attributed to their use of a conventional jellium approximation rather than the more rigorous LMCC approach for handling charged supercell boundary conditions. The difference between standard dft and hybrid functional results for defect levels lies not in a band gap problem but rather in solving a boundary condition problem. The LMCC-PBE entirely mitigates the effect of the band gap problem on defect levels. The more computationally economical PBE enables a systematic exploration of 3C-SiC defects, where, most notably, we find that the silicon vacancy undergoes Jahn-Teller-induced distortions from the previously assumed Td symmetry, and that the divacancy, like the silicon vacancy, exhibits a site-shift bistability in p-type conditions.
Inspired by the parallelism and efficiency of the brain, several candidates for artificial synapse devices have been developed for neuromorphic computing, yet a nonlinear and asymmetric synaptic response curve precludes their use for backpropagation, the foundation of modern supervised learning. Spintronic devices - which benefit from high endurance, low power consumption, low latency, and CMOS compatibility - are a promising technology for memory, and domain-wall magnetic tunnel junction (DW-MTJ) devices have been shown to implement synaptic functions such as long-term potentiation and spike-timing dependent plasticity. In this work, we propose a notched DW-MTJ synapse as a candidate for supervised learning. Using micromagnetic simulations at room temperature, we show that notched synapses ensure the non-volatility of the synaptic weight and allow for highly linear, symmetric, and reproducible weight updates using either spin transfer torque (STT) or spin-orbit torque (SOT) mechanisms of DW propagation. We use lookup tables constructed from micromagnetics simulations to model the training of neural networks built with DW-MTJ synapses on both the MNIST and Fashion-MNIST image classification tasks. Accounting for thermal noise and realistic process variations, the DW-MTJ devices achieve classification accuracy close to ideal floating-point updates using both STT and SOT devices at room temperature and at 400 K. Our work establishes the basis for a magnetic artificial synapse that can eventually lead to hardware neural networks with fully spintronic matrix operations implementing machine learning.
TinMan is the first technology to continuously measure thermal neutron intensity during aircraft flight and to define this environment, an important achievement since changes to semiconductors have led electronic parts to become more sensitive to thermal nuetrons that may lead to disturbances in their operation.
Carrasco, Rigo A.; Morath, Christian P.; Grant, Perry C.; Ariyawansa, Gamini; Reyner, C.J.; Stephenson, Chad A.; Kadlec, Clark N.; Hawkins, Samuel D.; Klem, John F.; Steenbergen, Elizabeth H.; Schaefer, Stephen T.; Johnson, Shane R.; Zollner, S.; Webster, Preston T.
Gallium is incorporated into the strain-balanced In(Ga)As/InAsSb superlattice system to achieve the same mid-wave infrared cutoff tunability as conventional Ga-free InAs/InAsSb type-II superlattices, but with an additional degree of design freedom to enable optimization of absorption and transport properties. Time-resolved photoluminescence measurements of InGaAs/InAsSb superlattice characterization- and doped device structures are reported from 77 to 300 K and compared to InAs/InAsSb. The low-injection photoluminescence decay yields the minority carrier lifetime, which is analyzed with a recombination rate model, enabling the determination of the temperature-dependent Shockley-Read-Hall, radiative, and Auger recombination lifetimes and extraction of defect energy levels and capture cross section defect concentration products. The Shockley-Read-Hall-limited lifetime of undoped InGaAs/InAsSb is marginally reduced from 2.3 to 1.4 μs due to the inclusion of Ga; however, given that Ga improves the vertical hole mobility by a factor of >10×, a diffusion-limited InGaAs/InAsSb superlattice nBn could expect a lower bound of 2.5× improvement in diffusion length with significant impact on photodetector quantum efficiency and radiation hardness. At temperatures below 120 K, the doped device structures are Shockley-Read-Hall limited at 0.5 μs, which shows promise for detector applications.
This project is focused on developing advanced combustion strategies for mixing-controlled compression ignition (MCCI, i.e., diesel-cycle) engines that are synergistic with renewable and/or unconventional fuels in a manner that enhances domestic energy security, economic competitiveness, and environmental quality. During this reporting period, the two focus areas were ducted fuel injection (DFI) and surrogate diesel fuels.
Pulsed laser irradiation is used to investigate the local initiation of rapid, self-propagating formation reactions in Al/Pt multilayers. The single pulse direct laser ignition of these 1.6 μm thick freestanding foils was characterized over 10 decades of pulse duration (10 ms to 150 fs). Finite element, reactive heat transport modeling of the near-threshold conditions has identified three distinct ignition pathways. For milli- to microsecond pulses, ignition occurs following sufficient absorption of laser energy to enable diffusion of Al and Pt between layers such that the heat released from the corresponding exothermic reaction overcomes conductive losses outside the laser-irradiated zone. When pulse duration is decreased into the nanosecond regime, heat is concentrated near the surface such that the Al locally melts, and a portion of the top-most bilayers react initially. The favorable kinetics and additional heat enable ignition. Further reducing pulse duration to hundreds of femtoseconds leads to a third ignition pathway. While much of the energy from these pulses is lost to ablation, the remaining heat beneath the crater can be sufficiently concentrated to drive a transverse self-propagating reaction, wherein the heat released from mixing at each interface occurs under kinetic conditions capable of igniting the subsequent layer.
Multisensor networks deployed at nuclear facilities can be leveraged to collect data used as inputs to machine learning models predicting nuclear safeguard relevant information. This work demonstrates an application of this idea by regressing nuclear reactor power levels, a key indicator for nuclear safeguard verification, at the McClellan Nuclear Research Center using data collected by five Merlyn multisensor platforms with LASSO and LSTM models. This work also demonstrates the use of Leave One Node Out to measure the importance of each multisensor for this regression problem providing insight into model explainability and allowing inferential hypotheses about the nuclear facility to be made. This work can be used as a starting point for future development of methods for regression on reactor power levels at nuclear facilities using multisensor network data.
Following the 1986 disastrous accident at Chernobyl, gaining independence following the fall of the former Soviet Union, two revolutions, and on-going Russian intervention, Ukrainea is seeking to expand its use of commercial nuclear energy and to further reduce its dependence on Russia for energy. Nuclear energy in Ukraine has made a significant contribution toward achieving sustainable development and social goals. Ukraine is very vulnerable to the effects of climate change and nuclear is a cornerstone of the country’s effort to combat the effects. With broad support from the U.S. government, Ukraine is cooperating with the U.S. on fresh nuclear fuel supply, civilian nuclear security, outage optimization, spent fuel storage, and future nuclear technology. And is looking to tap U.S. expertise on plant management, and electric grid operations and expansion. NEIc hosted this virtual event to provide an update on nuclear energy projects and plans in Ukraine. Featured speakers included Yaroslav Demchenkov, Deputy Minister of Energy, Taras Kachka, Deputy Minister – Trade Representative, Petro Kotin, CEO, Energoatom,d and Ann K. Ganzer, Senior Bureau Official, Bureau of International Security and Nonproliferation, U.S. Dept. of State. Maria Korsnick, NEI CEO, moderated the discussion. About forty individuals attended representing the Ukraine and U.S. governments, and U.S. suppliers.
This paper serves as the Interface Control Document (ICD) for the Seascape automated test harness developed at Sandia National Laboratories. The primary purposes of the Seascape system are: (1) provide a place for accruing large, curated, labeled data sets useful for developing and evaluating detection and classification algorithms (including, but not limited to, supervised machine learning applications) (2) provide an automated structure for specifying, running and generating reports on algorithm performance. Seascape uses GitLab, Nexus, Solr, and Banana, open source codes, together with code written in the Python language, to automatically provision and configure computational nodes, queue up jobs to accomplish algorithms test runs against the stored data sets, gather the results and generate reports which are then stored in the Nexus artifact server.
Plastics, with their ubiquitous presence in our daily lives and environment, pose an uncomfortable conundrum. Producers and consumers are aware of the value of these organic ingredients in material flow, yet their persistence and disruption to the ecological milieu desperately stipulate a shift in the status quo. Biodegradable plastics-as the name suggests-has its appeal in ensuring the safe return of carbon to ecosystems by complete assimilation of the degraded product as a food source for soil or aquatic microorganisms. However, despite more than a decade of commercial presence, these plastics are still far from replacing the demand for fossil-fuel-based commodity plastics. We discuss this apparent disconnect herein through a material value chain perspective. We review the current state of commercial biodegradable plastics and contrast it against the desired state of the zero-waste-focused circular economy. To close the gap, we suggest critical research needs concerning the structure and properties of biodegradable plastics, testing standards, application development, and waste management. The ultimate success in displacing conventional plastics with biodegradable alternatives will be predicated on collaboration between all stakeholders across the product value chain.
Reactive gas formation in pores of metal–organic frameworks (MOFs) is a known mechanism of framework destruction; understanding those mechanisms for future durability design is key to next generation adsorbents. Herein, an extensive set of ab initio molecular dynamics (AIMD) simulations are used for the first time to predict competitive adsorption of mixed acid gases (NO2 and H2O) and the in-pore reaction mechanisms for a series of rare earth (RE)-DOBDC MOFs. Spontaneous formation of nitrous acid (HONO) is identified as a result of deprotonation of the MOF organic linker, DOBDC. The unique DOBDC coordination to the metal clusters allows for proton transfer from the linker to the NO2 without the presence of H2O and may be a factor in DOBDC MOF durability. This is a previously unreported mechanisms of HONO formation in MOFs. With the presented methodology, prediction of future gas interactions in new nanoporous materials can be achieved.
This paper describes the verification and validation (V&V) framework developed for the stochastic Particle-in-Cell, Direct Simulation Monte Carlo code Aleph. An ideal framework for V&V from the viewpoint of the authors is described where a physics problem is defined, and relevant physics models and parameters to the defined problem are assessed and captured in a Phenomena Identification and Ranking Table (PIRT). Furthermore, numerous V&V examples guided by the PIRT for a simple gas discharge are shown to demonstrate the V&V process applied to a real-world simulation tool with the overall goal to demonstrably increase the confidence in the results for the simulation tool and its predictive capability. Although many examples are provided here to demonstrate elements of the framework, the primary goal of this work is to introduce this framework and not to provide a fully complete implementation, which would be a much longer document. Comparisons and contrasts are made to more usual approaches to V&V, and techniques new to the low-temperature plasma community are introduced. Specific challenges relating to the sufficiency of available data (e.g., cross sections), the limits of ad hoc validation approaches, the additional difficulty of utilizing a stochastic simulation tool, and the extreme cost of formal validation are discussed.
The use of evidence theory and associated cumulative plausibility functions (CPFs), cumulative belief functions (CBFs), cumulative distribution functions (CDFs), complementary cumulative plausibility functions (CCPFs), complementary cumulative belief functions (CCBFs), and complementary cumulative distribution functions (CCDFs) in the analysis of time and temperature margins associated with loss of assured safety (LOAS) for one weak link (WL)/two strong link (SL) systems is illustrated. Article content includes cumulative and complementary cumulative belief, plausibility, and probability for (i) SL/WL failure time margins defined by (time at which SL failure potentially causes LOAS) – (time at which WL failure potentially prevents LOAS), (ii) SL/WL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) – (the temperature at which WL failure potentially prevents LOAS), and (iii) SL/SL failure temperature margins defined by (the temperature at which SL failure potentially causes LOAS) – (the temperature of SL whose failure potentially causes LOAS at the time at which WL failure potentially prevents LOAS).
Liquid organic hydrogen carriers such as alcohols and polyols are a high-capacity means of transporting and reversibly storing hydrogen that demands effective catalysts to drive the (de)hydrogenation reactions under mild conditions. We employed a combined theory/experiment approach to develop MOF-74 catalysts for alcohol dehydrogenation and examine the performance of the open metal sites (OMS), which have properties analogous to the active sites in high-performance single-site catalysts and homogeneous catalysts. Methanol dehydrogenation was used as a model reaction system for assessing the performance of five monometallic M-MOF-74 variants (M = Co, Cu, Mg, Mn, Ni). Co-MOF-74 and Ni-MOF-74 give the highest H2 productivity. However, Ni-MOF-74 is unstable under reaction conditions and forms metallic nickel particles. To improve catalyst activity and stability, bimetallic (NixMg1-x)-MOF-74 catalysts were developed that stabilize the Ni OMS and promote the dehydrogenation reaction. An optimal composition exists at (Ni0.32Mg0.68)-MOF-74 that gives the greatest H2 productivity, up to 203 mL gcat-1 min-1 at 300 °C, and maintains 100% selectivity to CO and H2 between 225-275 °C. The optimized catalyst is also active for the dehydrogenation of other alcohols. DFT calculations reveal that synergistic interactions between the open metal site and the organic linker lead to lower reaction barriers in the MOF catalysts compared to the open metal site alone. This work expands the suite of hydrogen-related reactions catalyzed by MOF-74 which includes recent work on hydroformulation and our earlier reports of aryl-ether hydrogenolysis. Moreover, it highlights the use of bimetallic frameworks as an effective strategy for stabilizing a high density of catalytically active open metal sites. This journal is
This interim report is an update of ongoing experimental and modeling work on bentonite material described in Jové Colón et al. (2019, 2020) from past international collaboration activities. As noted in Jové Colón et al. (2020), work on international repository science activities such as FEBEX-DP and DECOVALEX19 is either no longer continuing by the international partners. Nevertheless, research activities on the collected sample materials and field data are still ongoing. Descriptions of these underground research laboratory (URL) R&D activities are described elsewhere (Birkholzer et al. 2019; Jové Colón et al. 2020) but will be explained here when needed. The current reports recent reactive-transport modeling on the leaching of sedimentary rock.
The use of evidence theory and associated cumulative plausibility functions (CPFs), cumulative belief functions (CBFs), cumulative distribution functions (CDFs), complementary cumulative plausibility functions (CCPFs), complementary cumulative belief functions (CCBFs), and complementary cumulative distribution functions (CCDFs) in the analysis of loss of assured safety (LOAS) for weak link (WL)/strong link (SL) systems is introduced and illustrated. Article content includes cumulative and complementary cumulative belief, plausibility, and probability for (i) time at which LOAS occurs for a one WL/two SL system, (ii) time at which a two-link system fails, (iii) temperature at which a two-link system fails, and (iv) temperature at which LOAS occurs for a one WL/two SL system. The presented results can be generalized to systems with more than one WL and two SLs.
In this paper we analyze the noise in macro-particle methods used in plasma physics and fluid dynamics, leading to approaches for minimizing the total error, focusing on electrostatic models in one dimension. We begin by describing kernel density estimation for continuous values of the spatial variable x, expressing the kernel in a form in which its shape and width are represented separately. The covariance matrix of the noise in the density is computed, first for uniform true density. The bandwidth of the covariance matrix C(x,y) is related to the width of the kernel. A feature that stands out is the presence of constant negative terms in the elements of the covariance matrix both on and off-diagonal. These negative correlations are related to the fact that the total number of particles is fixed at each time step; they also lead to the property ∫ C(x,y)dy = 0. We investigate the effect of these negative correlations on the electric field computed by Gauss's law, finding that the noise in the electric field is related to a process called the Ornstein-Uhlenbeck bridge, leading to a covariance matrix of the electric field with variance significantly reduced relative to that of a Brownian process. For non-constant density, p(x), still with continuous x, we analyze the total error in the density estimation and discuss it in terms of bias-variance optimization (BVO). For some characteristic length l, determined by the density and its second derivative, and kernel width h, having too few particles within h leads to too much variance; for h that is large relative to l, there is too much smoothing of the density. The optimum between these two limits is found by BVO. For kernels of the same width, it is shown that this optimum (minimum) is weakly sensitive to the kernel shape. Next, we repeat the analysis for x discretized on a grid. In this case the charge deposition rule is determined by a particle shape. An important property to be respected in the discrete system is the exact preservation of total charge on the grid; this property is necessary to ensure that the electric field is equal at both ends, consistent with periodic boundary conditions. Here, we find that if the particle shapes satisfy a partition of unity property, the particle charge deposited on the grid is conserved exactly. Further, if the particle shape is expressed as the convolution of a kernel with another kernel that satisfies the partition of unity, then the particle shape obeys the partition of unity. This property holds for kernels of arbitrary width, including widths that are not integer multiples of the grid spacing. Furthermore, we show results relaxing the approximations used to do BVO optimization analytically, by doing numerical computations of the total error as a function of the kernel width, on a grid in x. The comparison between numerical and analytical results shows good agreement over a range of particle shapes. We discuss the practical implications of our results, including the criteria for design and implementation of computationally efficient particle shapes that take advantage of the developed theory.
Alternative candidate precursors to [Hf(BH4)4] for low-temperature chemical vapor deposition of hafnium diboride (HfB2) films were identified using density functional theory simulations of molecules with the composition [Hf(BH4)2L2], where L = -OH, -OMe, -O-t-Bu, -NH2, -N═C═O, -N(Me)2, and -N(CH2)5NH2 (1-piperidin-2-amine referred to as Pip2A). Disassociation energies (ED), potential energy surface (PES) scans, ionization potentials, and electron affinities were all calculated to identify the strength of the Hf-L bond and the potential reactivity of the candidate precursor. Ultimately, the low ED (2.07 eV) of the BH4 ligand removal from the Hf atom in [Hf(BH4)4] was partially attributed to an intermediate state where [Hf(BH4)3(H)] and BH3 is formed. Of the candidate precursors investigated, three exhibited a similar mechanism, but only -Pip2A had a PES scan that indicated binding competitive with [Hf(BH4)4], making it a viable candidate for further study.
Rempe, Susan R.; Vangordon, Monika R.; Prignano, Lindsey A.; Dempski, Robert E.; Rick, Steven W.
Channelrhodopsins (ChR) are light-sensitive cation channels used in optogenetics, a technique that applies light to control cells (e.g., neurons) that have been modified genetically to express those channels. Although mutations are known to affect pore kinetics, little is known about how mutations induce changes at the molecular scale. To address this issue, we first measured channel opening and closing rates of a ChR chimera (C1C2) and selected variants (N297D, N297V, and V125L). Then, we used atomistic simulations to correlate those rates with changes in pore structure, hydration, and chemical interactions among key gating residues of C1C2 in both closed and open states. Overall, the experimental results show that C1C2 and its mutants do not behave like ChR2 or its analogous variants, except V125L, making C1C2 a unique channel. Our atomistic simulations confirmed that opening of the channel and initial hydration of the gating regions between helices I, II, III, and VII of the channel occurs with 1) the presence of 13-cis retinal; 2) deprotonation of a glutamic acid gating residue, E129; and 3) subsequent weakening of the central gate hydrogen bond between the same glutamic acid E129 and asparagine N297 in the central region of the pore. Also, an aspartate (D292) is the unambiguous primary proton acceptor for the retinal Schiff base in the hydrated channel.
In this work, experimental results from a study on the evolution of gas jets ejected through the orifices of a pre-chamber in a heavy-duty optical engine are presented. The work examines conditions without fuel inside the main-chamber, which helps to describe the dynamics of the ejected gas jets without the interference of subsequent combustion in the main-chamber. Experimental diagnostics consist of high-speed visible intensified imaging and low-speed infrared imaging. Additionally a one-dimensional gas jet model is used to characterize the spatial distribution of the ejected flow, including parameters such as tip penetration, which are then validated based on experimental results. Different stages in the ejection of pre-chamber jets are identified, with chemical activity restricted to a maximum distance of 5 to 10 orifice diameters downstream of the orifice as indicated by the recorded visible radiation. Sensitivity of cycle-to-cycle variations in pre-chamber jet development to the air-to-fuel ratio in the pre-chamber observed in the experiments is in most part attributed to the variations in the timing of combustion initiation in the pre-chamber. The influence of the ejection flow on the penetration of the gas jet on a cycle-to-cycle basis is presented using the one-dimensional model. The one-dimensional model also indicates that the local flow exhibits highest sensitivity to operating conditions during the start of ejection until the timing when maximum flow is attained. Differences that exist during the decreasing mass-flow ejection time-period tend to smear out in part due to the transient slowdown of the ejection process.
Two-dimensional (2D) metal-boride-derived nanostructures have been a focus of intense research for the past decade, with an emphasis on new synthetic approaches, as well as on the exploration of possible applications in next-generation advanced materials and devices. Their unusual mechanical, electronic, optical, and chemical properties, arising from low dimensionality, present a new paradigm to the science of metal borides that has traditionally focused on their bulk properties. This Perspective discusses the current state of research on metal-boride-derived 2D nanostructures, highlights challenges that must be overcome, and identifies future opportunities to fully utilize their potential.
Interest in wave energy converters to provide autonomous power to various ocean-bound systems, such as autonomous underwater vehicles, sensor systems, and even aquaculture farms, has grown in recent years. The Monterey Bay Aquarium Research Institute has developed and deployed a small two-body point absorber wave energy device suitable to such needs. This paper provides a description of the system to support future open-source access to the device and further the general development of similar wave energy systems. Additionally, to support future control design and system modification efforts, a set of hydrodynamic models are presented and cross-compared. To test the viability of using a linear frequency-domain admittance model for controller tuning, the linear model is compared against four WEC-Sim models of increasing complexity. The linear frequency-domain model is found to be generally adequate for capturing system dynamics, as the model agreement is good and the degree of nonlinearity introduced in the WEC-Sim models is generally less than 2.5%.
We present the development of a pulsed power experimental technique to infer the electrical conductivity of metals from ambient to high energy density conditions. The method is implemented on Thor, a moderate scale (1-2 MA) pulsed power driver. The electrical conductivity of copper at elevated temperature (>4000 K) and pressure (>10 GPa) is determined, and a new tabular material model is developed, guided by density functional theory, which preserves agreement with existing experimental data. Minor modifications (<10%) are found to be necessary to the previous Lee-More-Desjarlais model isotherms in the vicinity of the melt transition in order to account for observed discrepancies with the new experimental data. An analytical model for magnetic direct drive flyer acceleration and Joule heating induced vaporization based on the Tsiolkovsky "rocket equation"is presented to assess sensitivity of the method to minor changes in electrical conductivity.
In this work, we investigate the critical coupling of a single gold disk antenna with a focused beam by evaluating its absorption and scattering using spectral interferometry microcopy.
This work describes the diagnostic implementation and image processing methods to quantitatively measure diesel spray mixing injected into a high-pressure, high-temperature environment. We used a high-repetition-rate pulse-burst laser developed in-house, a high-speed CMOS camera, and optimized the optical configuration to capture Rayleigh scattering images of the vaporized fuel jets inside a constant volume chamber. The experimental installation was modified to reduce reflections and flare levels to maximize the images’ signal-to-noise ratios by anti-reflection coatings on windows and surfaces, as well as series of optical baffles. Because of the specificities of the high-speed system, several image processing techniques had to be developed and implemented to provide quantitative fuel concentration measurements. These methods involve various correction procedures such as camera linearity, laser intensity fluctuation, dynamic background flare, as well as beam-steering effects. Image inpainting was also applied to correct the Rayleigh scattering signal from large scatterers (e.g. particulates). The experiments demonstrate that applying planar laser Rayleigh scattering at high repetition rate to quantitatively resolve the mixing of fuel and ambient gases in diesel jets is challenging, but possible. The thorough analysis of the experimental uncertainty and comparisons to past data prove that such measurements can be accurate, whilst providing valuable information about the mixing processes of high-pressure diesel jets.
This writing examines the DevOps practice from a new perspective, one of understanding its philosophical and scientific nature. DevOps has fundamentally changed the landscape for research and development based on guiding philosophical and scientific principles. Advanced computational technologies and domains have adopted DevOps to enable advanced solution engineering for efficient, quality-assured output. The author provides a concise account of how the philosophy and science of DevOps synergistically defines its essential disposition.
Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential memory and compute savings. These applications use data stored in a variety of compression formats. We demonstrate that both the compactness of different compression formats and the compute efficiency of the algorithms enabled by them vary across tensor dimensions and amount of sparsity. Since DL and scientific workloads span across all sparsity regions, there can be numerous format combinations for optimizing memory and compute efficiency. Unfortunately, many proposed accelerators operate on one or two fixed format combinations. This work proposes hardware extensions to accelerators for supporting numerous format combinations seamlessly and demonstrates ∼ 4 × speedup over performing format conversions in software.
Among Professor Arthur Gossard's many contributions to crystal growth are those resulting in important improvements in the quality and performance of quantum-well and quantum-dot semiconductor lasers. In celebration of his 85th birthday, we review the development of a semiconductor laser theory that is motivated and guided, in part, by those advances. This theory combines condensed matter theory and laser physics to provide understanding at a microscopic level, i.e., in terms of electrons and holes, and their interaction with the radiation field while influenced by the lattice.
We present a numerical framework for recovering unknown nonautonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the nonautonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances. The time-dependent inputs are then locally parameterized by using a proper model, for example, polynomial regression, in the pieces determined by the time instances. This transforms the original system into a piecewise parametric system that is locally time invariant. We then design a deep neural network structure to learn the local models. Once the network model is constructed, it can be iteratively used over time to conduct global system prediction. We provide theoretical analysis of our algorithm and present a number of numerical examples to demonstrate the effectiveness of the method.
Ajantiwalay, Tanvi; Nagel, Lauren; Maloy, Stuart; Hattar, Khalid M.; Mecholsky, John J.; Aitkaliyeva, Assel
Ferritic/martensitic steels, such as HT-9, are known for their complex microstructural features and mechanical properties. In this paper, in-situ micro-tensile tests and traditional fractography methods were utilized to study the fracture behavior of proton-irradiated HT-9 steels. First, to evaluate the viability of micro-tensile tests for nuclear material qualification process, meso‑tensile tests on as-received HT-9 steels were performed. Fracture mechanisms of unirradiated HT-9 steels at both length scales were compared and underlying mechanisms discussed. The direct comparison of micro- and meso‑scale data shows a distinctive size effect demonstrated by the increase in yield stress (YS). Upon completion of initial assessment, specimens were irradiated with 4 MeV+ protons to three fluences, all of which were lower than 0.01 displacements per atom (dpa). As expected, the YS increases with irradiation. However, at 7 × 10−3 dpa, the reversal of the trend was observed, and the YS exhibited sharp decline. We demonstrate that at lower length scales, grain structure has a more profound impact on the mechanical properties of irradiated materials, which provides information needed to fill in the gap in current understanding of the HT-9 fracture at different length scales.
Metamaterials, otherwise known as architected or programmable materials, enable designers to tailor mesoscale topology and shape to achieve unique material properties that are not present in nature. Additionally, with the recent proliferation of additive manufacturing tools across industrial sectors, the ability to readily fabricate geometrically complex metamaterials is now possible. However, in many high-performance applications involving complex multi-physics interactions, design of novel lattice metamaterials is still difficult. Design is primarily guided by human intuition or gradient optimization for simple problems. In this work, we show how machine learning guides discovery of new unit cells that are Pareto optimal for multiple competing objectives; specifically, maximizing elastic stiffness during static loading and minimizing wave speed through the metamaterial during an impact event. Additionally, we show that our artificial intelligence approach works with relatively few (3500) simulation calls.
This report will summarize the group's work to provide recommendations to secure nuclear facilities before, during and after an extreme weather event. Extreme weather events can have drastic impacts to nuclear facilities as seen by the earthquake and subsequent tsunami at the Fukushima Daiichi Nuclear Power Plant in 2011. Recent hurricanes in the United States including Hurricane Harvey demonstrate the devastating effects these storms can have on infrastructure and the surrounding communities. The group is attempting to identify the gaps that potential small modular reactor (SMR) facilities will need to address in order to provide adequate site security before, during and after extreme weather events. This effort proceeded in three parts to provide insights and recommendations to secure Small Modular Reactor facilities for extreme weather events:(1) a literature review of academic articles as well as relevant documents including the existing regulatory framework and recommendations from the IAEA, NRC, and DOE, (2) subject matter expert interviews from a wide variety of security backgrounds, and (3) modeling and simulation on a hypothetical SMR facility. Special attention was paid to the interactions between stakeholders and nuclear facility design considerations, particularly the topics of safety and security. Engineering design issues from safety and security perspectives were discussed and included in simulation. Each step informed the proceeding, with the result including full tabletop scenarios of EWE impacts to security system effectiveness on the hypothetical model. This systems-level analysis provides results to inform recommendations to secure SMR facilities.
Despite hitting major roadblocks in 2-D scaling, NAND flash continues to scale in the vertical direction and dominate the commercial nonvolatile memory market. However, several emerging nonvolatile technologies are under development by major commercial foundries or are already in small volume production, motivated by storage-class memory and embedded application drivers. These include spin-transfer torque magnetic random access memory (STT-MRAM), resistive random access memory (ReRAM), phase change random access memory (PCRAM), and conductive bridge random access memory (CBRAM). Emerging memories have improved resilience to radiation effects compared to flash, which is based on storing charge, and hence may offer an expanded selection from which radiation-tolerant system designers can choose from in the future. This review discusses the material and device physics, fabrication, operational principles, and commercial status of scaled 2-D flash, 3-D flash, and emerging memory technologies. Radiation effects relevant to each of these memories are described, including the physics of and errors caused by total ionizing dose, displacement damage, and single-event effects, with an eye toward the future role of emerging technologies in radiation environments.