BYU Represents at DPS

Many material properties can be traced back to properties of their grain boundaries. Grain boundary energy (GBE), as a result, is a key quantity of interest in the analysis and modeling of microstructure. A standard method for calculating grain boundary energy is molecular dynamics (MD); however, on-the-fly MD calculations are not tenable due to the extensive computational time required. Lattice matching (LM) is a reduced-order method for estimating GBE quickly; however, it has only been tested against a relatively limited set of data, and does not have a suitable means for assessing error. In this work, we use the recently published dataset of Homer et al. (2022) to assess the performance of LM over the full range of GB space, and to equip LM with a metric for error estimation. LM is used to generate energy estimates, along with predictions of facet morphology, for each of the 7,304 boundaries in the Homer dataset. The relative and absolute error of LM, based on the reported MD data, is found to be 5%-8%. An essential part of the LM method is the faceting relaxation, which corrects the expected energy by convexification across the compact space (S2) S 2) of boundary plane orientations. The original Homer dataset did not promote faceting, but upon extended annealing, it is shown that facet patterns similar to those predicted by LM emerge.

Due to the difficulty and expense of underwater field trials, a high-fidelity underwater simulator is a necessity for testing and developing algorithms. To fill this need, we present HoloOcean, an open-source underwater simulator, built upon Unreal Engine 4 (UE4). HoloOcean comes equipped with multiagent support, various sensor implementations of common underwater sensors, and simulated communications support. Due to being built upon UE4, new environments are straightforward to add, enabling easy extensions to be built. HoloOcean is controlled via a simple Python interface, allowing simple installation via pip, and requiring few lines of code to execute simulations. Each agent is equipped with various control schemes and dynamics that can be customized via the Python interface. Also included is a novel sonar sensor framework that leverages an octree representation of the environment for efficient and realistic sonar imagery generation. In addition, to improve the authenticity of the imaging sonar simulation, we use a novel cluster-based multipath ray-tracing algorithm, various probabilistic noise models, and properties of reflecting surfaces. We also leverage the sonar simulation framework to simulate sidescan, single-beam, and multibeam profiling sonars.

Bronin et al. [Phys. Rev. E 108, 045209 (2023)] recently reported molecular-dynamics simulations of ultracold neutral plasmas expanding in a quadrupole magnetic field. While the main results are in agreement with prior experimental measurements, we present data showing oscillations not captured in the simulations of Bronin et al. Plasmas formed using pulsed or continuous-wave ionization processes have similar confinement times.

The past few decades have made clear that the properties and performances of emerging functional and quantum materials can depend strongly on their local atomic and/or magnetic structure, particularly when details of the local structure deviate from the long-range structure averaged over space and time. Traditional methods of structural refinement (e.g., Rietveld) are typically sensitive only to the average structure, creating a need for more advanced structural probes suitable for extracting information about structural correlations on short length- and time-scales. In this Perspective, we describe the importance of local magnetic structure in several classes of emerging materials and present the magnetic pair distribution function (mPDF) technique as a powerful tool for studying short-range magnetism from neutron total-scattering data. We then provide a selection of examples of mPDF analysis applied to magnetic materials of recent technological and fundamental interest, including the antiferromagnetic semiconductor MnTe, geometrically frustrated magnets, and iron-oxide magnetic nanoparticles. The rapid development of mPDF analysis since its formalization a decade ago puts this technique in a strong position for making continued impact in the study of local magnetism in emerging materials.

A new deposition method developed by Goddard Space Flight Center fluorinates Al mirrors with XeF2 followed by a LiF coating to create what they term Al+XeLiF. This in-situ, room temperature process produces mirrors with high reflectivity in a broad spectral range, from the FUV to the IR, and is reported to be stable in relative humidities of 30% and lower. These mirrors are envisioned for missions requiring sensitivity down to 100 nm wavelength such as the habitable worlds observatory. Because most mission integration and testing campaigns require prolonged exposure to lab environments, and launch sites experience high relative humidities (RH) on average, some at 80% RH. We investigate Al+XeLiF stability in a wider range of temperatures and humidities along with employing additional characterization techniques including atomic force microscopy and x-ray photoelectron spectroscopy. We found that Al+XeLiF is stable in environments up to 82%RH when kept at cooler temperatures (3°C and 21°C). However, this material is unstable when stored at 60°C, experiencing roughening and loss in reflection from resulting Al surface plasmon excitation.

Although teachers have opportunities to learn about many things through teaching experience, we know little about how they develop science subject matter knowledge in this setting. With both limited opportunities to learn science subject matter knowledge before becoming teachers and minimal science professional development available while working as a teacher, it is important to understand the extent to which elementary teachers develop science subject matter knowledge in their regular classroom practice and the factors that influence that development. In this longitudinal, mixed methods study we collected both quantitative and qualitative data before and following their final field experience, which was their first opportunity to have significant teaching experience. Findings suggest two important factors for subject matter knowledge development: time for considering science subject matter and a learning setting that values science. In contrast, indicators of learner capacity (i.e., prior knowledge) and time teaching the topics were not associated with teacher subject matter knowledge development.