Publications

Controlling microstructural evolution in metallic additive manufacturing (AM) is difficult, especially in producing refined as-built grains instead of coarse, directional grains. Traditional solutions involve adding inoculants to AM feedstocks, but titanium (Ti) alloys cannot employ this approach without producing detrimental secondary phases. Ti-Cu (Ti-copper) alloys offer a solution through constitutional supercooling and/or solid state thermal cycling under AM conditions. This work analyzes a compositionally graded directed energy deposition (DED) Ti-Cu build, single-melt laser tracks, and dilatometric heat treatments to evaluate if, when, and by what mechanism(s) microstructural refinement occurs. Refinement by inoculation of unmelted powder particles was also considered. Constitutional supercooling produced no net microstructural refinement as any equiaxed dendrites which form are remelted with new deposition. This finding agreed with solidification modeling of powder bed fusion-laser beam (PBF-LB) and DED builds. Solid state thermal cycling refined microstructures only during ex-situ dilatometric heat treatments, suggesting build parameter optimization is needed to achieve refinement in-situ. Accidental heterogeneous nucleation on unmelted Ti powder, originating from the different thermophysical properties of Ti and Cu, provided the most significant microstructural refinement. This work systematically assesses the microstructural refinement mechanisms of Ti-Cu in AM builds and offers insights into microstructural control in eutectoid alloys.

Scalable quantum processors require high-fidelity universal quantum logic operations in a manufacturable physical platform. Donors in silicon provide atomic size, excellent quantum coherence and compatibility with standard semiconductor processing, but no entanglement between donor-bound electron spins has been demonstrated to date. Here we present the experimental demonstration and tomography of universal one- and two-qubit gates in a system of two weakly exchange-coupled electrons, bound to single phosphorus donors introduced in silicon by ion implantation. We observe that the exchange interaction has no effect on the qubit coherence. We quantify the fidelity of the quantum operations using gate set tomography (GST), and we use the universal gate set to create entangled Bell states of the electrons spins, with fidelity 91.3 ± 3.0%, and concurrence 0.87 ± 0.05. These results form the necessary basis for scaling up donor-based quantum computers.

The effect of proton implantation as isolation implant and subsequent annealing on the optical absorption and electrical resistivity of low-bandgap p-GaSb is reported. The measured transmittance spectra indicates that implantation creates a distribution of energy levels extending into the bandgap. Electrical measurements show that the average sheet resistance of the implanted layer increases only by an order of magnitude from its pre-implantation value at a proton dose of ∼1013 cm−2 followed by 200 °C annealing. It is also shown that annealing reduces the implantation-induced optical absorption while still retaining a high electrical resistivity.

Cyber-physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber-physical systems requires improved understanding of the combined cyber-physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber-physical data from smart grid systems to analyse differences and similarities in behaviour during cyber-, physical-, and cyber-physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber-physical systems. Through this analysis, deeper insights can be shared with decision-makers on what cyber and physical components are strongly or weakly linked, what cyber-physical pathways are most traversed, and the criticality of certain cyber-physical nodes or edges. This paper presents several types of clustering methods for cyber-physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber-physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber-physical graph interdependency analysis.

A deep convolutional neural network (DCNN) was used for microstructure reconstruction using artificial intelligence (MR-AI) by predicting local average fiber orientation distributions (FOD) in a 3D prepreg platelet molded composite (PPMC) pin bracket. To train the MR-AI model, surface strain fields from residual stresses simulated in PPMC plates were used as the input to the DCNN. A training dataset included PPMC plates with various degrees of global fiber alignment, based on the information obtained from high-fidelity flow simulation of a pin bracket. The MR-AI model was then deployed to analyze FOD in the 3D pin bracket by conducting thermo-elastic residual stress analysis. Initially, the MR-AI model was established entirely on the synthetic simulation data. Then, a μCT scan of a physically molded pin bracket was used to create a finite element model that provided data for additional validation of the DCNN model. For the μCT scan finite element pin bracket the MR-AI model predicted the distribution of fiber orientation tensor components with MAE of 0.10 indicating a global prediction error of 10 %. For the flow simulated pin bracket, the MR-AI model predicted the distribution of fiber orientation tensor components with a global prediction error of 11 %. The MR-AI model showed the ability to predict regions of varying alignment in the base and flange of the pin bracket. The proposed MR-AI methodology allows for rapid prediction of FOD in geometrically complex parts and offers a promising path to detecting unique fiber orientation states in molded components.

In response to gradual nanoindentation, the surface of muscovite mica deforms by sudden stochastic nanometer-scale displacement bursts. Here, the statistics of these displacement events are interpreted using a statistical model previously used to model earthquakes to understand how chemically reactive environments alter the surface properties of this material. We show that the statistics of nanoindentation displacement bursts in muscovite mica are tuned by chemomechanical weakening in a manner similar to how the statistics of model events are tuned by a mechanical weakening parameter that describes how easily system-spanning cracks can be nucleated. Because the predictions of this model are independent of any surface defects or structural details, these results suggest this simple model can be universally used to describe chemomechanical weakening in many systems prone to slip avalanches on a wide range of spatio-temporal scales.

Barium titanate (BTO) is a ferroelectric perovskite used in electronics and energy storage systems because of its high dielectric constant. Decreasing the BTO particle size was shown to increase the dielectric constant of the perovskite, which is an intriguing but contested result. We investigated this result by fabricating silicone-matrix nanocomposite specimens containing BTO particles of decreasing diameter. Furthermore, density functional theory modeling was used to understand the interactions at the BTO particle surface. Combining results from experiments and modeling indicated that polymer type, particle surface interactions, and particle surface structure can influence the dielectric properties of polymer-matrix nanocomposites containing BTO.

Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings can be done throughout a part to achieve functional gradation and optimization at a structural level. To predict the performance of particular microstructural configuration and thereby overall performance, constitutive models of materials with microstructure are needed. In this work we provide neural network architectures that provide effective homogenization models of materials with anisotropic components. These models satisfy equivariance and material symmetry principles inherently through a combination of equivariant and tensor basis operations. We demonstrate them on datasets of stochastic volume elements with different textures and phases where the material undergoes elastic and plastic deformation, and show that the these network architectures provide significant performance improvements.

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.

Material Testing 2.0 (MT2.0) is a paradigm that advocates for the use of rich, full-field data, such as from digital image correlation and infrared thermography, for material identification. By employing heterogeneous, multi-axial data in conjunction with sophisticated inverse calibration techniques such as finite element model updating and the virtual fields method, MT2.0 aims to reduce the number of specimens needed for material identification and to increase confidence in the calibration results. To support continued development, improvement, and validation of such inverse methods—specifically for rate-dependent, temperature-dependent, and anisotropic metal plasticity models—we provide here a thorough experimental data set for 304L stainless steel sheet metal. The data set includes full-field displacement, strain, and temperature data for seven unique specimen geometries tested at different strain rates and in different material orientations. Commensurate extensometer strain data from tensile dog bones is provided as well for comparison. We believe this complete data set will be a valuable contribution to the experimental and computational mechanics communities, supporting continued advances in material identification methods.

Metal chalcogenides are of interest as electrocatalysts, battery materials, and more. XPS is a valuable tool for studying changes to these materials before and after catalysis, making reference spectra for the pristine materials valuable. Here, we present XPS spectra for a family of nickel sulfoselenide (NiSSe) materials based on the Ni3X2 crystal structure, Ni3S2−xSex. XPS surveys and high-resolution spectra of Ni 2p, S 2p, S 2s, Se 3d, Se 3p, and Se 3s were recorded using Al Kα radiation.

It has become increasingly important for microbiology educators to help students learn critical concepts of the discipline. This is particularly true in virology, where current challenges include increasing rates of vaccine hesitancy, misinformation about the COVID-19 pandemic, and controversy surrounding research on pathogens with pandemic potential. Having students learn virology can attract more people to the fieldand increase the number of people who can engage in meaningful discourse about issues relating to the discipline. However, the limited number of virologists who teach undergraduates, combined with the fact that many institutions lack stand-alone virology courses, results in virology often being taught as a limited number of lectures within an undergraduate microbiology course (if it is covered at all), which may or may not be taught by an individual trained as a virologist. To provide a framework to teach virology to undergraduate students, a team of virology educators, with support from the American Society for Virology (ASV), developed curriculum guidelines for use in a stand-alone undergraduate virology course or a virology section within another course (D. B. Kushner et al., J Virol 96:e01305-22, 2022, https://doi.org/10.1128/jvi.01305-22). These guidelines are available at the ASV website (https://asv.org/curriculum-guidelines/). To assist educators in implementing these guidelines, we created examples of measurable learning objectives. This perspective provides details about the virology curriculum guidelines and learning objectives and accompanies the perspective by Boury et al. in this issue of the Journal of Microbiology & Biology Education (25:e00126-24, 2024, https://doi.org/10.1128/jmbe.00126-24) about the recent revision of the microbiology curriculum guidelines overseen by the American Society for Microbiology.

Decarbonizing the glass industry requires alternative melting technology, as current industrial melting practices rely heavily on fossil fuels. Hydrogen has been proposed as an alternative to carbon-based fuels, but the ensuing consequences on the mechanical behavior of the glass remain to be clarified. A critical distinction between hydrogen and carbon-based fuels is the increased generation of water during combustion, which raises the equilibrium solubility of water in the melt and alters the behavior of the resulting glass. A series of five silicate glasses with 80% silica and variable [Na2O]/([H2O] + [Na2O]) ratios were simulated using molecular dynamics to elucidate the effects of water on fracture. Several fracture toughness calculation methods were used in combination with atomistic fracture simulations to examine the effects of hydroxyl content on fracture behavior. This study reveals that the crack propagation pathway is a key metric to understanding fracture toughness. Notably, the fracture propagation path favors hydrogen sites over sodium sites, offering a possible explanation of the experimentally observed effects of water on fracture properties.

Solid–water interfaces are crucial for clean water, conventional and renewable energy, and effective nuclear waste management. However, reflecting the complexity of reactive interfaces in continuum-scale models is a challenge, leading to oversimplified representations that often fail to predict real-world behavior. This is because these models use fixed parameters derived by averaging across a wide physicochemical range observed at the molecular scale. Recent studies have revealed the stochastic nature of molecular-level surface sites that define a variety of reaction mechanisms, rates, and products even across a single surface. To bridge the molecular knowledge and predictive continuum-scale models, we propose to represent surface properties with probability distributions rather than with discrete constant values derived by averaging across a heterogeneous surface. This conceptual shift in continuum-scale modeling requires exponentially rising computational power. By incorporating our molecular-scale understanding of solid–water interfaces into continuum-scale models we can pave the way for next generation critical technologies and novel environmental solutions.

Dielectric barrier discharges, particularly micro cavity plasma arrays, offer significant potential for plasma-catalytic research due to their ability to ignite plasma in direct contact with a catalytic surface, enabling the observation of plasma-surface interactions. A key factor in their application is the generation of reactive species, such as atomic oxygen, within the cavities. These species can interact with both the surface (e.g. for activation or cleaning) and the gas being treated (e.g. for oxidation). Given the central role of oxygen atoms in plasma catalysis and their use as a model for more complex species, this work investigates the transport of these atoms out of the cavities. Two-photon absorption laser-induced fluorescence spectroscopy with picosecond laser excitation is performed in the volume above the cavities. The results are compared with a basic diffusion model. The reactor operates with a He/O2 mixture at a flow rate of 1 slm and atmospheric pressure. Densities of up to 1016 cm−3 are measured near the surface. Time-dependent measurements show that, at a distance of 350 µm from the surface, a density equilibrium is reached within less than 3 ms of reactor operation. Decay times due to ozone formation after the reactor is turned off are on a similar scale. Spatially resolved measurements show that the oxygen density decreases exponentially from the surface but remains detectable up to approximately 1 mm above the surface, indicating significant application potential. Variations in the O2 admixture show a density maximum at 0.4%, confirming previous helium state enhanced actinometry measurements within the cavities.

Technical key performance indicators (KPIs) are important metrics used to assess and quantitatively summarize various aspects of photovoltaic (PV) systems, including long-term performance, economic viability, and carbon footprint. Herein, a group of experts of the International Energy Agency's Photovoltaic Power Systems Programme Task 13 collect and describ the most important technical KPIs used in the industry. Thereby, a set of best practices for reliably handling PV system data is presented and the impact of data quality and climatic variability on KPI calculation is investigated. The effective use of technical KPIs allows triggering data-driven and informed decisions to optimize PV systems and providing a comprehensive overview of how PV systems operate across different conditions and climates. With the worldwide growth of the PV industry, more companies operate/own PV systems in different regions, where the climatic and seasonal profiles differ. This requires context-aware evaluation of KPIs, or the judicious application of multiple KPIs, to ensure that each asset is evaluated correctly. Beyond that, there is untapped potential in the utilization of KPIs through geospatial mapping and extrapolation of fleet KPIs. This study demonstrates that the uncertainty in KPI estimation is not well understood and depends on data quality, climatic variability, and system configuration.

Additive manufacturing (AM) technology, specifically 3D printing, holds great promise for in-orbit manufacturing. In-space printing can significantly reduce the mass, cost, and risk of long-term space exploration by enabling replacement parts to be made as needed and reducing dependence on Earth. However, printing in a zero-gravity environment poses challenges due to the absence of a rigid ground for the print platform, which can result in vibrational and rotational forces that may impact printing integrity. To address this issue, this paper proposes a novel linear magnetic position tracking algorithm, named Navigation Integrating Magnets By Linear Estimation (NIMBLE), for dynamic vibration compensation during 3D printing of truss structures in space. Compared to the most commonly used nonlinear optimization method, the NIMBLE algorithm is more than two orders of magnitude faster. With only a single 3-axis magnet sensor and a small NdFeB magnet, the NIMBLE algorithm provides a simple and easily implemented tracking solution for in-orbit 3D printing.

The Used Fuel Disposition Campaign (UFDC) was established within the United States (U.S.) Department of Energy (DOE) Office of Nuclear Energy (NE) to conduct research and development (R&D) activities associated with storage, transportation, and disposal of used or spent nuclear fuel (UNF or SNF) and high-level radioactive waste.

The deployment of heavy-duty (HD) hydrogen fuel cell vehicles that are entering the market now is driving the need for expanded HD hydrogen refueling station infrastructure to meet demand. This expansion must prioritize safety and reliability, necessitating careful consideration of the associated risks. In this study, we use a light-duty (LD) hydrogen refueling station as a comparative tool to quantify the risks for a HD station, which is essentially a scaled-up version of a LD station.

Abstract not provided.

Abstract not provided.

The Strategic Petroleum Reserve (SPR) is the world’s largest supply of emergency crude oil. The reserve consists of four sites in Louisiana and Texas. Each site stores crude in deep, underground salt caverns. It is the mission of the SPR’s Enhanced Monitoring Program to examine available sensing data to inform our understanding of each site. This report discusses the monitoring data, processes, and results for each of the four sites for fiscal year 2024.

This report describes a two-dimensional model of Saturn based on the CASTLE transmission line code. Building on previous modeling efforts, 2D circuit models based on the “chain-link fence” geometry are constructed for pre-ReCap Saturn and post-ReCap Saturn. The 2D model results are in better agreement with data from Shot 4550 measurements of load currents and doses then the previous 1D model. Lower doses (9%) predicted by the new model can be compensated by increasing the load A-K gap.

This report describes the proposed efforts for a three-year (CY23-25) program to develop refractory metal boride/carbide precursors for metal-organic chemical vapor deposition (MOCVD) applications. Reported are the CY24 results on the thermal processing of bis-cyclopentadienyl dialkyl and tetra-alkyl precursors to obtain metal carbide products. Precursors evaluated are commercially available. Materials were processed within in a custom-built MOCVD system at 1000 ⁰C, as well as in a hot isostatic press (HIP) at temperatures of 1000 ⁰C or 1650 ⁰C at pressures of 5000 psi. The products were identified as metal carbide, metal oxide, or a mixture of carbide and oxide phases depending on the starting material and process used. Density functional theory calculations were performed to determine the decomposition mechanism and to inform how ligand choice led to the products.

Magnetic reconnection is a fundamental plasma physics process ubiquitous in astrophysics, and important in both magnetic confinement fusion and space weather. The MARZ fundamental science program was recently established on Z to enable the first laboratory astrophysics platform able to access and study the strongly radiatively cooled magnetic reconnection regime. Simulations of this system have successfully used a resistive-MHD approach, but in some regions of parameter space Hall physics has the potential to be important. We describe implementation of a Hall method on a staggered grid resistive-MHD method (compatible with the approach used to model MARZ experiments. We then present a different Hall method based on cell-centered field quantities. Both approaches have been implemented in the Sandia KRAKEN code, to enable us to contrast different numerical Hall-MHD methods within the same HED code.