Publications

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Recently proposed collision-specific parameters for direct simulation Monte Carlo simulations are tested for binary mixtures of nitrogen, oxygen, and argon. Near ambient conditions, the traditional collision-averaged parameters are highly accurate, whereas the collision-specific parameters are not. The simulated transport using the collision-averaged parameters for mixtures with helium, however, is found to be inaccurate. Hence, we propose a novel method to determine molecular parameters by combining the Chapman-Enskog theory with empirical mixing rules and experimental data. The optimized parameters are highly accurate for the binary mixtures of nitrogen, oxygen, and argon and greatly improve the simulated transport for the helium mixtures.

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Hybrid bonded silicon nitride thin-film lithium niobate (TFLN) Mach-Zehnder modulators (MZMs) at 1310 nm were designed with metal coplanar waveguide electrodes buried in the silicon-on-insulator (SOI) chip. The MZM devices showed greatly improved performance compared to earlier devices of a similar design, and similar performance to comparable MZM devices with gold electrodes made on top of the TFLN layer. Both devices achieve a 3-dB electro-optic bandwidth greater than 110 GHz and voltage-driven optical extinction ratios greater than 28 dB. Half-wave voltage-length products ( Vπ L) of 2.8 and 2.5 Vċ cm were measured for the 0.5 and 0.4 cm long buried metal and top gold electrode MZMs, respectively.

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Yttrium iron garnet (Y₃Fe₅O₁₂; YIG) has a unique combination of low magnetic damping, high spin-wave conductivity, and insulating properties that make it a highly attractive material for a variety of applications in the fields of magnetics and spintronics. While the room-temperature magnetization dynamics of YIG have been extensively studied, there are limited reports correlating the low-temperature magnetization dynamics to the material structure or growth method. Here, in this study, we investigate liquid phase epitaxy grown YIG films and their magnetization dynamics at temperatures down to 10 K. We show there is a negligible increase in the ferromagnetic resonance linewidth down to 10 K, which is unique when compared with YIG films grown by other deposition methods. From the broadband ferromagnetic resonance measurements, polarized neutron reflectivity, and scanning transmission electron microscopy, we conclude that these liquid phase epitaxy grown films have negligible rare-earth impurities present, specifically the suppression of Gd diffusion from the Gd₃Ga₅O₁₂ (GGG) substrate into the Y₃Fe₅O₁₂ film, and therefore negligible magnetic losses attributed to the slow-relaxation mechanism. Overall, liquid phase epitaxy YIG films have a YIG/GGG interface that is five times sharper and have ten times lower ferromagnetic resonance linewidths below 50 K than comparable YIG films by other deposition methods. Thus, liquid phase epitaxy grown YIG films are ideal for low-temperature experiments/applications that require low magnetic losses, such as quantum transduction and manipulation via magnon coupling.

Ferroelectric Al1−xScxN has raised much interest in recent years due to its unique ferroelectric properties and complementary metal oxide semiconductor back-end-of-line compatible processing temperatures. Potential applications in embedded nonvolatile memory, however, require ferroelectric materials to switch at relatively low voltages. One approach to achieving a lower switching voltage is to significantly reduce the Al1−xScxN thickness. In this work, ferroelectric behavior in 5-27 nm films of sputter deposited Al0.72Sc0.28N has been studied. We find that the 10 kHz normalized coercive field increases from 4.4 to 7.3 MV/cm when reducing the film thickness from 27.1 to 5.4 nm, while over the same thickness range, the characteristic breakdown field of a 12.5 μm radius capacitor increases from 8.3 to 12.1 MV/cm. The 5.4 nm film demonstrates ferroelectric switching at 5.5 V when excited with a 500 ns pulse and a switching speed of 60 ns.

We report high-resolution frequency study and phase locking have been performed on a terahertz (THz) quantum-cascade vertical-external-cavity surface-emitting laser (QC-VECSEL) operating around 2.5 THz. A subharmonic diode mixer is used to down convert the THz signal to a 100 MHz intermediate frequency that is phase locked to a stable 100 MHz microwave reference. Between 90% and 95% of the QC-VECSEL signal is locked within 2 Hz of the multiplied RF reference, and amplitude fluctuations on the order of 1%–10% are observed, depending on the bias point of the QC-VECSEL. The bandwidth of the locking loop is ~1 MHz. Many noise peaks in the IF signal are observed, likely corresponding to mechanical resonances in the 10 Hz–10 kHz. These peaks are generally -30 to -60 dB below the main tone and are below the phase noise level of the multiplied RF reference that ultimately limits the phase noise of the locked QC-VECSEL.

Sulfur-deficient polycrystalline two-dimensional (2D) molybdenum disulfide (MoS2) memtransistors exhibit gate-tunable memristive switching to implement emerging memory operations and neuromorphic computing paradigms. Grain boundaries and sulfur vacancies are critical for memristive switching; however, the underlying physical mechanisms are not fully understood. Furthermore, the adsorption of water and gaseous species strongly perturbs electronic transport in monolayer MoS2, and little work has been done to explore the influence of surface interactions on defect-related kinetics that produces memristive switching. Here, we study the switching kinetics of back-gated MoS2 memtransistors using current transient measurements in a controlled atmosphere chamber. We observe that adsorbed water molecules lead to suppression of the electronic trap-filling processes concomitant with the resistive switching process, resulting in altered kinetics of the resistive switching. Additionally, using the transient response from “bunched” drain voltage pulse trains performed as a function of temperature, we extract the energy of the affected trap state and find that it places the trap roughly midgap [ E T = E C - 0.7 ( ± 0.4 ) eV]. Our results highlight the importance of controlling for surface interactions that may affect switching kinetics in 2D memtransistors, synaptic transistors, and related memory devices.

Topological metals are conducting materials with gapless band structures and nontrivial edge-localized resonances. Their discovery has proven elusive because traditional topological classification methods require band gaps to define topological robustness. Inspired by recent theoretical developments that leverage techniques from the field of C*-algebras to identify topological metals, here, we directly observe topological phenomena in gapless acoustic crystals and realize a general experimental technique to demonstrate their topology. Specifically, we not only observe robust boundary-localized states in a topological acoustic metal, but also re-interpret a composite operator—mathematically derived from the K-theory of the problem—as a new Hamiltonian whose physical implementation allows us to directly observe a topological spectral flow and measure the topological invariants. Our observations and experimental protocols may offer insights for discovering topological behaviour across a wide array of artificial and natural materials that lack bulk band gaps.

This report updates the high-level test plan for evaluating surface deposition on three commercial 32PTH2 spent nuclear fuel (SNF) canisters inside NUTECH Horizontal Modular Storage (NUHOMS) Advanced Horizontal Storage Modules (AHSMs) from Orano (formerly Transnuclear Inc.) and provides a summary of the surface sampling activities that have been conducted to date. The details contained in this report represent the best designs and approaches explored for testing as of this publication. Given the rapidly developing nature of this test program, some of these plans may change to accommodate new objectives or requirements. One goal of this testing is to collect defensible and detailed dust deposition measurements from the surface of dry storage canisters in a marine coastal environment to guide chloride-induced stress corrosion cracking (CISCC) research. Another goal is to provide data for the validation of computational fluid dynamics (CFD) based deposition modeling. To facilitate surface sampling, the otherwise highly prototypic dry storage systems will not contain SNF but rather will be electrically heated to mimic the decay heat and thermal hydraulic environment. Test and heater design is supported by detailed CFD modeling. Instrumentation throughout the canister, storage module, and environment will provide extensive information about the thermal-hydraulic behavior of horizontal dry cask storage systems. Manual sampling over a comprehensive portion of the canister surface at regular time intervals will offer detailed quantification and composition of the deposited particulates from a realistic storage environment. Discussions of a potential host site for the Canister Deposition Field Demonstration (CDFD) are ongoing. Until a host site is chosen, testing of key CDFD hardware components including the heater assemblies, power skid, and remote data acquisition system will continue. Functional testing of the finalized heater assemblies and test apparatus started this fiscal year. These initial heater tests have shown the assemblies are performing within design specifications. Staged surface sampling of a mockup of a canister outside the AHSM on a transfer skid was also performed. Refinements to the sampling procedures and techniques were captured from observation of these activities and lessons-learned debriefs. These updated sampling procedures and techniques are planned to be tested again in the field using the mockup in order to assure personnel are using the most accurate and repeatable methods possible prior to deployment for actual CDFD testing.

This report describes the results of preliminary testing of aerosol monitoring equipment that will be used to continuously monitor the aerosol source term for the multi-year Canister Deposition Field Demonstration (CDFD). These data are required inputs for the development and validation of models for the deposition of dust and potentially corrosive salts on the surface of spent nuclear fuel (SNF) dry storage canisters. Surface salt loads correlate with the extent of corrosion damage on a metal surface, and potentially to the likelihood and timing of initiation of stress corrosion cracks. Aerosols will be monitored at the CDFD site using three instruments. A Dekati^® ELPI+ cascade impactor will be used for real-time monitoring of aerosol particle sizes. It will also collect dust in 14 size bins on impactor targets that can be chemically analyzed to determine the soluble salts present as a function of particle size. However, this instrument can only measure dried aerosols, with a diameter of <10 µm. The second instrument is a Topas laser particle size spectrometer, which provides real-time monitoring of aerosol particle sizes up to ~40 µm in size. It monitors both the ambient (potentially deliquesced) aerosol particle size distributions required for the dust deposition models and the distributions of the equivalent dried particles, allowing correlation with the Dekati^® data. However, it does not discriminate between inert dust particles and salt aerosols, and it does not retain samples of the different particle sizes for later analysis. The third instrument that will monitor aerosols at the CDFD site is a Clean Air Status and Trends Network (CASTNET) tower, which uses a multiple canister system to collect weekly samples for analysis to total suspended aerosol particle compositions and atmospheric gas concentrations. This status report describes work in FY23 to develop the capabilities for using these tools. In two training exercises, the cascade impactor and laser particle sizer were deployed in two different testing environments, one indoor and one outdoor. For the cascade impactor, the tests provided opportunities for the operators to familiarize themselves with impactor substrate preparation, and post-test sample removal and analysis. For the laser particle sizer, the tests were used to evaluate different instrument parameters, to determine the most appropriate settings for capturing transient events. Data and samples were collected for weeks to months for each test, and the results are presented here. In addition to the preliminary testing, contracts were developed with WSP Analytical Labs for sample preparation and analysis of the cascade impactor samples. The impactor tower from outdoor test was delivered to WSP and used to train the staff there in disassembly, sample extraction, sample analysis, and tower reassembly with new target substrates. These are tasks that WSP will be performing routinely for the CDFD project. The CASTNET system cannot be purchased or tested until an actual site has been selected for the CDFD test. Work for this FY has been restricted to preparation of contracts for purchasing the CASTNET tower, and for sample analysis, once the tower is in operation.

Here this paper introduces a publicly available PyTorch-ABAQUS deep-learning framework of a family of plasticity models where the yield surface is implicitly represented by a scalar-valued function. In particular, our focus is to introduce a practical framework that can be deployed for engineering analysis that employs a user-defined material subroutine (UMAT/VUMAT) for ABAQUS, which is written in FORTRAN. To accomplish this task while leveraging the back-propagation learning algorithm to speed up the neural-network training, we introduce an interface code where the weights and biases of the trained neural networks obtained via the PyTorch library can be automatically converted into a generic FORTRAN code that can be a part of the UMAT/VUMAT algorithm. To enable third-party validation, we purposely make all the data sets, source code used to train the neural-network-based constitutive models, and the trained models available in a public repository. Furthermore, the practicality of the workflow is then further tested on a dataset for anisotropic yield function to showcase the extensibility of the proposed framework. A number of representative numerical experiments are used to examine the accuracy, robustness and reproducibility of the results generated by the neural network models.

Solar-powered photochemical water splitting using suspensions of photocatalyst nanoparticles is an attractive route for economical production of green hydrogen. SrTiO3-based photocatalysts have been intensely investigated due to their stability and recently demonstrated near-100% external quantum yield (EQY) for water splitting using wavelengths below 360 nm. To extend the optical absorption into the visible, SrTiO3nanoparticles have been doped with various transition metals. Here we demonstrate that doping SrTiO3nanoparticles with 1% Rh introduces midgap acceptor states which reduce the free electron concentration by 5 orders of magnitude, dramatically reducing built-in potentials which could otherwise separate electron-hole (e-h) pairs. Rhodium states also function as recombination centers, reducing the photocarrier lifetime by nearly 2 orders of magnitude and the maximum achievable EQY to 10%. Furthermore, the absence of built-in electric fields within Rh-doped SrTiO3nanoparticles suggests that modest e-h separation can be achieved by exploiting a difference in mobility between electrons and holes.

A significant amount of uncertainty exists regarding potential human exposure to laboratory biomaterials and organisms in Biosafety Level 2 (BSL-2) research laboratories. Computational fluid dynamics (CFD) modeling is proposed as a way to better understand potential impacts of different combinations of biomaterials, laboratory manipulations, and exposure routes on risks to laboratory workers. Here, in this study, we use CFD models to simulate airborne concentrations of contaminants in an actual BSL-2 laboratory under different configurations. Results show that ventilation configuration, sampling location, and contaminant source location can significantly impact airborne concentrations and exposures. Depending on the source location and airflow patterns, the transient and time-integrated concentrations varied by several orders of magnitude. Contaminant plumes from sources located near a return vent (or exhaust like a fume hood or ventilated biosafety cabinet) are likely to be more contained than sources that are further from the exhaust. Having a direct flow between the source and the exhaust (through-flow condition) may reduce potential exposures to individuals outside the air flow path. Designing a BSL-2 room with ventilation and airflow patterns that maximize through-flow conditions to the return/exhaust vents and minimize dispersion and mixing throughout the room is, therefore, recommended. CFD simulations can also be used to assist in characterizing the impacts of supply and return vent locations, room layout, and source locations on spatial and temporal contaminant concentrations. In addition, proper placement of particle sensors can also be informed by CFD simulations to provide additional characterization and monitoring of potential exposures in BSL-2 facilities.

This report is the revised (Revision 10) Task F specification for DECOVALEX-2023. Task F is a comparison of the models and methods used in deep geologic repository performance assessment. The task proposes to develop a reference case for a mined repository in a fractured crystalline host rock (Task F1) and a reference case for a mined repository in a salt formation (Task F2). Teams may choose to participate in the comparison for either or both reference cases. For each reference case, a common set of conceptual models and parameters describing features, events, and processes that impact performance will be given, and teams will be responsible for determining how best to implement and couple the models. The comparison will be conducted in stages, beginning with a comparison of key outputs of individual process models, followed by a comparison of a single deterministic simulation of the full reference case, and moving on to uncertainty propagation and uncertainty and sensitivity analysis. This report provides background information, a summary of the proposed reference cases, and a staged plan for the analysis.

Pb-Zr-Ti-O (PZT) perovskites span a large solid-solution range and have found widespread use due to their piezoelectric and ferroelectric properties that also span a large range. Crystal structure analysis via Rietveld refinement facilitates materials analysis via the extraction of the structural parameters. These parameters, often obtained as a function of an additional dimension (e.g., pressure), can help to diagnose materials response within a use environment. Often referred to as in-situ studies, these experiments provide an abundance of data. Viewing structural changes due to applied pressure conditions can give much-needed insight into materials performance. However, challenges exist for viewing/presenting results when the details are inherently three-dimensional (3D) in nature. For PZT perovskites, the use of polyhedra (e.g., Zr/Ti-O6 octahedra) to view bonding/connectivity is beneficial; however, the visualization of the octahedra behavior with pressure dependence is less easily demonstrated due to the complexity of the added pressure dimension. We present a more intuitive visualization by projecting structural data into virtual reality (VR). We employ previously published structural data for Pb0.99(Zr0.95Ti0.05)0.98Nb0.02O3 as an exemplar for VR visualization of the PZT R3c crystal structure between ambient and 0.62 GPa pressure. This is accomplished via our in-house CAD2VR™ software platform and the new CrystalVR plugin. The use of the VR environment enables a more intuitive viewing experience, while enabling on-the-fly evaluation of crystal data, to form a detailed and comprehensive understanding of in-situ datasets. Discussion of methodology and tools for viewing are given, along with how recording results in video form can enable the viewing experience.

Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. Here in this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.