Publications

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The influence of He ion radiation on GaAs thermal conductivity was investigated using TDTR and the PGM. We found that damage in the shallow defect only regions of the radiation profile scattering phonons with a frequency to the fourth dependence due to randomly distributed Frankel pairs. Damage near the end of range however, scatters phonons with a second order frequency dependence due to the cascading defects caused by the rapid radiation energy loss at the end of range resulting in defect clusters. Using the PGM and experimental thermal conductivity trends it was then possible to estimate the defect recombination rate and size of defect clusters. The methodology developed here results in a powerful tool for interrogating radiation damage in semiconductors.

The deeply depleted graphene-oxide-semiconductor (D2GOS) junction detector provides an effective architecture for photodetection, enabling direct readout of photogenerated charge. Because of an inherent gain mechanism proportional to graphene's high mobility (μ), this detector architecture exhibits large responsivities and signal-to-noise ratios (SNR). The ultimate sensitivity of the D2GOS junction detector may be limited, however, because of the generation of dark charge originating from interface states at the semiconductor/dielectric junction. Here, we examine the performance limitations caused by dark charge and demonstrate its mitigation via the creation of low interface defect junctions enabled by surface passivation. The resulting devices exhibit responsivities exceeding 10 000 A/W - a value which is 10× greater than that of analogous devices without the passivating thermal oxide. With cooling of the detector, the responsivity further increases to over 25 000 A/W, underscoring the impact of surface generation on performance and thus the necessity of minimizing interfacial defects for this class of photodetector.

The morphology of the stagnated plasma resulting from Magnetized Liner Inertial Fusion (MagLIF) is measured by imaging the self-emission x-rays coming from the multi-keV plasma, and the evolution of the imploding liner is measured by radiographs. Equivalent diagnostic response can be derived from integrated rad-MHD simulations from programs such as Hydra and Gorgon. There have been only limited quantitative ways to compare the image morphology, that is the texture, of simulations and experiments. We have developed a metric of image morphology based on the Mallat Scattering Transformation (MST), a transformation that has proved to be effective at distinguishing textures, sounds, and written characters. This metric has demonstrated excellent performance in classifying ensembles of synthetic stagnation images. We use this metric to quantitatively compare simulations to experimental images, cross experimental images, and to estimate the parameters of the images with uncertainty via a linear regression of the synthetic images to the parameter used to generate them. This coordinate space has proved very adept at doing a sophisticated relative back-ground subtraction in the MST space. This was needed to compare the experimental self emission images to the rad-MHD simulation images. We have also developed theory that connects the transformation to the causal dynamics of physical systems. This has been done from the classical kinetic perspective and from the field theory perspective, where the MST is the generalized Green's function, or S-matrix of the field theory in the scale basis. From both perspectives the first order MST is the current state of the system, and the second order MST are the transition rates from one state to another. An efficient, GPU accelerated, Python implementation of the MST was developed. Future applications are discussed.

Microstructure reconstruction problems are usually limited to the representation with finitely many number of phases, e.g. binary and ternary. However, images of microstructure obtained through experimental, for example, using microscope, are often represented as a RGB or grayscale image. Because the phase-based representation is discrete, more rigid, and provides less flexibility in modeling the microstructure, as compared to RGB or grayscale image, there is a loss of information in the conversion. In this paper, a microstructure reconstruction method, which produces images at the fidelity of experimental microscopy, i.e. RGB or grayscale image, is proposed without introducing any physics-based microstructure descriptor. Furthermore, the image texture is preserved and the microstructure image is represented with continuous variables (as in RGB or grayscale images), instead of binary or categorical variables, which results in a high-fidelity image of microstructure reconstruction. The advantage of the proposed method is its quality of reconstruction, which can be applied to any other binary or multiphase 2D microstructure. The proposed method can be thought of as a subsampling approach to expand the microstructure dataset, while preserving its image texture. Moreover, the size of the reconstructed image is more flexible, compared to other machine learning microstructure reconstruction method, where the size must be fixed beforehand. In addition, the proposed method is capable of joining the microstructure images taken at different locations to reconstruct a larger microstructure image. A significant advantage of the proposed method is to remedy the data scarcity problem in materials science, where experimental data is scare and hard to obtain. The proposed method can also be applied to generate statistically equivalent microstructures, which has a strong implication in microstructure-related uncertainty quantification applications. The proposed microstructure reconstruction method is demonstrated with the UltraHigh Carbon Steel micrograph DataBase (UHCSDB).

This study investigates the mechanical and corrosion properties of as-built and annealed equiatomic CoCrFeMnNi alloy produced by laser-based directed energy deposition (DED) Additive Manufacturing (AM). The high cooling rates of DED produced a single-phase, cellular microstructure with cells on the order of 4 μm in diameter and inter-cellular regions that were enriched in Mn and Ni. Annealing created a chemically homogeneous recrystallized microstructure with a high density of annealing twins. The average yield strength of the as-built condition was 424 MPa and exceeded the annealed condition (232 MPa), however; the strain hardening rate was lower for the as-built material stemming from higher dislocation density associated with DED parts and the fine cell size. In general, the yield strength, ultimate tensile strength, and elongation-to-failure for the as-built material exceeded values from previous studies that explored other AM techniques to produce the CoCrFeMnNi alloy. Ductile fracture occurred for all specimens with dimple initiation associated with nanoscale oxide inclusions. The breakdown potential (onset of pitting corrosion) was similar for the as-built and annealed conditions at 0.40 VAg/AgCl when immersed in 0.6 M NaCl. Pit morphology/propagation for the as-built condition exhibited preferential corrosion of inter-cellular Ni/Mn regions leading to a tortuous pit bottom and cover, while the annealed conditions pits resembled lacy pits similar to 304 L steel. A passive oxide film depleted in Cr cations with substantial incorporation of Mn cations is proposed as the primary mechanism for local corrosion susceptibility of the CoCrFeMnNi alloy.

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The cybersecurity research community has focused primarily on the analysis and automation of intrusion detection systems by examining network traffic behaviors. Expanding on this expertise, advanced cyber defense analysis is turning to host-based data to use in research and development to produce the next generation network defense tools. The ability to perform deep packet inspection of network traffic is increasingly harder with most boundary network traffic moving to HTTPS. Additionally, network data alone does not provide a full picture of end-to-end activity. These are some of the reasons that necessitate looking at other data sources such as host data. We outline our investigation into the processing, formatting, and storing of the data along with the preliminary results from our exploratory data analysis. In writing this report, it is our goal to aid in guiding future research by providing foundational understanding for an area of cybersecurity that is rich with a variety of complex, categorical, and sparse data, with a strong human influence component. Including suggestions for guiding potential directions for future research.

The Waste Isolation Pilot Plant (WIPP) is an operating geologic repository in southeastern New Mexico for transuranic (TRU) waste from nuclear defense activities. Past nuclear criticality concerns have generally been low at the WIPP due to the low initial concentration of fissile material and the natural tendency of fissile solute to disperse during fluid transport in porous media (Rechard et al. 2000). On the other hand, the list of acceptable WIPP waste types has expanded over the years to include Criticality Control Overpack (CCO) containers and Pipe Overpack (POP) containers. Containers bound for WIPP are bundled together in hexagon shaped 7-packs (six containers surround one container in the center). Two 7-packs are often combined into a TRUPACT-II package for a total of 14 containers. Most TRUPACT-II packages are restricted to a maximum fissile mass equivalent to plutonium (FMEP) between 0.1 and 0.38 kg, but a CCO TRUPACT-II package and a POP TRUPACT-II package are respectively permitted to have 5.32 kg and 2.80 kg FMEP (see Section 3 of US DOE (2013)). Consequently, CCO container criticality after emplacement at the WIPP was evaluated in Saylor and Scaglione (2018), and Oak Ridge National Laboratories is currently at work on POP container criticality analyses.

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The catastrophic nuclear reactor accident at Fukushima damaged public confidence in nuclear energy and a demand for new engineered safety features that could mitigate or prevent radiation releases to the environment in the future. We have developed a novel use of sacrificial material (SM) to prevent the molten corium from breaching containment during accidents as well as a validated, novel, high-fidelity modeling capability to design and optimize the proposed concept. Some new reactor designs employ a core catcher and a SM, such as ceramic or concrete, to slow the molten corium and avoid the breach of the containment. However, existing reactors cannot easily be modified to include these SMs but could be modified to allow injectable cooling materials (current designs are limited to water). The SM proposed in this Laboratory Development Research and Development (LDRD) project is based on granular carbonate minerals that can be used in existing light water reactor plants. This new SM will induce an endothermic reaction to quickly freeze the corium in place, with minimal hydrogen explosion and maximum radionuclide retention. Because corium spreading is a complex process strongly influenced by coupled chemical reactions (with underlying containment material and especially with the proposed SM), decay heat and phase change. No existing tool is available for modeling such a complex process. This LDRD project focused on two research areas: experiments to demonstrate the feasibility of the novel SM concept, and modeling activities to determine the potential applications of the concept to actual nuclear plants. We have demonstrated small-scale to large-scaled experiments using lead oxide (Pb0) as surrogate for molten corium, which showed that the reaction of the SM with molten Pb0 results in a fast solidification of the melt and the formation of open pore structures in the solidified Pb0 because of CO₂ released from the carbonate decomposition.

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