Publications

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Pyrolyzed carbon as a mechanical material is promising for applications in harsh environments. In this work, we characterized the material and developed novel processes for fabricating carbon composite micro-electromechanical systems (CMEMS) structures. A novel method of increasing Young's modulus and the conductivity of pyrolyzed AZ 4330 was demonstrated by loading the films with graphene oxide prior to pyrolysis. By incorporating 2 wt.% graphene stiffeners into the film, a 65% increase in Young's modulus and 11% increase in conductivity were achieved. By reactive ion etching pyrolyzed blanket AZ 50XT thick film photoresist, a high aspect ratio process was demonstrated with films >7.5um thick. Two novel multi-level, volume-scalable CMEMS processes were developed on 6" diameter wafers. Young's modulus of 23 GPa was extracted from nanoindentation measurements of pyrolyzed AZ 50XT films. The temperature-dependent resistance was characterized from room temperature to 500C and found to be nearly linear over this range. By fitting the results of self-heated bridges in an inert ambient, we calculated that the bridges survived to 1000C without failure. Transmission electron microscopy (TEM) results showed the film to be largely amorphous, containing some sub-micrometer sized graphite crystallites. This was consistent with our Raman analysis, which also showed the film to be largely sp² bonded. The calculated average density of pyrolyzed AZ 4330 films was 1.32 g/cm². Thin level of disorder and the conductivity of thin film resistors were found to unchanged by 2Mrad gamma irradiation from a Co₆₀ source. Thin film pyrolyzed carbon resistors were hermetically sealed in a nitrogen ambient in 24-pin dual in-line packages (DIP's). The resistance was measured periodically and remained constant over 6 months' time.

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Silicon chips hosting a single donor can be used to store and manipulate one bit of quantum information. However, a central challenge for realizing quantum logic operations is to couple donors to one another in a controllable way. To achieve this, several proposals rely on using nearby quantum dots (QDs) to mediate an interaction. In this work, we demonstrate the coherent coupling of electron spins between a single 31 P donor and an enriched 28 Si metal-oxide-semiconductor few-electron QD. We show that the electron-nuclear spin interaction on the donor can drive coherent rotations between singlet and triplet electron spin states of the QD-donor system. Moreover, we are able to tune electrically the exchange interaction between the QD and donor electrons. Furthermore, the combination of single-nucleus-driven rotations and voltage-tunable exchange provides every key element for future all-electrical control of spin qubits, while requiring only a single QD and no additional magnetic field gradients

Laser-based failure analysis techniques demonstrate the ability to quickly and non-intrusively screen deep ultraviolet light-emitting diodes (LEDs) for electrically-active defects. In particular, two laser-based techniques, light-induced voltage alteration and thermally-induced voltage alteration, generate applied voltage maps (AVMs) that provide information on electrically-active defect behavior including turn-on bias, density, and spatial location. Here, multiple commercial LEDs were examined and found to have dark defect signals in the AVM indicating a site of reduced resistance or leakage through the diode. The existence of the dark defect signals in the AVM correlates strongly with an increased forward-bias leakage current. This increased leakage is not present in devices without AVM signals. Transmission electron microscopy analysis of a dark defect signal site revealed a dislocation cluster through the pn junction. The cluster included an open core dislocation. Even though LEDs with few dark AVM defect signals did not correlate strongly with power loss, direct association between increased open core dislocation densities and reduced LED device performance has been presented elsewhere [M. W. Moseley et al., J. Appl. Phys. 117, 095301 (2015)].

We report on a new technique for obtaining off-Hugoniot pressure vs. density data for solid metals compressed to extreme pressure by a magnetically driven liner implosion on the Z-machine (Z) at Sandia National Laboratories. In our experiments, the liner comprises inner and outer metal tubes. The inner tube is composed of a sample material (e.g., Ta and Cu) whose compressed state is to be inferred. The outer tube is composed of Al and serves as the current carrying cathode. Another aluminum liner at much larger radius serves as the anode. A shaped current pulse quasi-isentropically compresses the sample as it implodes. The iterative method used to infer pressure vs. density requires two velocity measurements. Photonic Doppler velocimetry probes measure the implosion velocity of the free (inner) surface of the sample material and the explosion velocity of the anode free (outer) surface. These two velocities are used in conjunction with magnetohydrodynamic simulation and mathematical optimization to obtain the current driving the liner implosion, and to infer pressure and density in the sample through maximum compression. This new equation of state calibration technique is illustrated using a simulated experiment with a Cu sample. Monte Carlo uncertainty quantification of synthetic data establishes convergence criteria for experiments. Results are presented from experiments with Al/Ta, Al/Cu, and Al liners. Symmetric liner implosion with quasi-isentropic compression to peak pressure ∼1000 GPa is achieved in all cases. These experiments exhibit unexpectedly softer behavior above 200 GPa, which we conjecture is related to differences in the actual and modeled properties of aluminum.

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This report describes the progress of the CONTAIN-LMR sodium physics and chemistry models to be implemented in to MELCOR 2.1. It also describes the progress to implement these models into CONT AIN 2 as well. In the past two years, the implementation included the addition of sodium equations of state and sodium properties from two different sources. The first source is based on the previous work done by Idaho National Laborat ory by modifying MELCOR to include liquid lithium equation of state as a working fluid to mode l the nuclear fusion safety research. The second source uses properties generated for the SIMMER code. Testing and results from this implementation of sodium pr operties are given. In addition, the CONTAIN-LMR code was derived from an early version of C ONTAIN code. Many physical models that were developed sin ce this early version of CONTAIN are not captured by this early code version. Therefore, CONTAIN 2 is being updated with the sodium models in CONTAIN-LMR in or der to facilitate verification of these models with the MELCOR code. Although CONTAIN 2, which represents the latest development of CONTAIN, now contains ma ny of the sodium specific models, this work is not complete due to challenges from the lower cell architecture in CONTAIN 2, which is different from CONTAIN- LMR. This implementation should be completed in the coming year, while sodi um models from C ONTAIN-LMR are being integrated into MELCOR. For testing, CONTAIN decks have been developed for verification and validation use. In terms of implementing the sodium m odels into MELCOR, a separate sodium model branch was created for this document . Because of massive development in the main stream MELCOR 2.1 code and the require ment to merge the latest code version into this branch, the integration of the s odium models were re-directed to implement the sodium chemistry models first. This change led to delays of the actual implementation. For aid in the future implementation of sodium models, a new sodium chemistry package was created. Thus reporting for the implementation of the sodium chemistry is discussed in this report.

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In this work we have presented a particle resuspension model implemented in the SNL code SIERRA/Fuego, which can be used to model particle dispersal and resuspension from surfaces. The method demonstrated is applicable to a class of particles, but would require additional parametric fits or physics models for extension to other applications, such as wetted particles or walls. We have demonstrated the importance of turbulent variations in the wall shear stress when considering resuspension, and implemented both shear stress variation models and stochastic resuspension models (not shown in this work). These models can be used in simulations with of physically realistic scenarios to augment lab-scale DOE Handbook data for airborne release fractions and respirable fractions in order to provide confidences for safety analysts and facility designers to apply in their analyses at DOE sites. Future work on this topic will involve validation of the presented model against experimental data and extension of the empirical models to be applicable to different classes of particles and surfaces.

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We present here an example of how a large,multi-dimensional unstructured data set, namely aircraft trajectories over the United States, can be analyzed using relatively straightforward unsupervised learning techniques. We begin by adding a rough structure to the trajectory data using the notion of distance geometry. This provides a very generic structure to the data that allows it to be indexed as an n-dimensional vector. We then do a clustering based on the HDBSCAN algorithm to both group flights with similar shapes and find outliers that have a relatively unique shape. Next, we expand the notion of geometric features to more specialized features and demonstrate the power of these features to solve specific problems. Finally, we highlight not just the power of the technique but also the speed and simplicity of the implementation by demonstrating them on very large data sets.

We present experimental and simulation results for a laboratory-based forward-scattering environment, where 1 μm diameter polystyrene spheres are suspended in water to model the optical scattering properties of fog. Circular polarization maintains its degree of polarization better than linear polarization as the optical thickness of the scattering environment increases. Both simulation and experiment quantify circular polarization's superior persistence, compared to that of linear polarization, and show that it is much less affected by variations in the field of view and collection area of the optical system. Our experimental environment's lateral extent was physically finite, causing a significant difference between measured and simulated degree of polarization values for incident linearly polarized light, but not for circularly polarized light. Through simulation we demonstrate that circular polarization is less susceptible to the finite environmental extent as well as the collection optic's limiting configuration.

We present a resilient domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. We discuss an implementation based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Servers are assumed to be “sandboxed”, while no assumption is made on the reliability of the clients. We explore the scalability of the algorithm up to ∼12k cores, build an SST/macro skeleton to extrapolate to∼50k cores, and show the resilience under simulated hard and soft faults for a 2D linear Poisson equation.