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We have investigated four models for calculating the contribution of an applied magnetic field to the free energy of Fe and Fe alloys—Weiss Molecular Field Theory (WMFT), Kuz’min, Arrott, and Curie-Weiss. On the basis of these models, the shifts in phase transformation including both ferromagnetic and paramagnetic phases as a function of magnetic field and alloy content can be predicted. The Kuz’min model is easiest to solve and is also best able to predict the trends in experimentally observed shifts in ferrite/austenite phase transformations for Fe-based alloys under an applied magnetic field both below and near the Curie temperature. For phase transformations above the Curie temperature, the predictions using the Curie-Weiss form with WMFT parameters, here extended to alloy systems, are in good agreement with experimental results. Different aspects of the four models have been discussed in detail with a view to developing a reliable methodology to predict shifts in phase diagrams as a function of alloy content.

Magnetic fields applied during electrodeposition are known to increase incorporation of inert second phase particles through field-induced convective forces. However, this incorporation was demonstrated in the bore of electromagnets, limiting application scope. Here we use low-cost permanent magnets to incorporate doped yttria particles that phosphoresce under ultraviolet radiation into a nickel coating. Negligible yttria amounts are observed in conventionally deposited coatings while a magnetic field perpendicular to the applied current successfully integrated enough yttria for a luminescent coating. These are useful for coating health monitoring applications; however, this magnetically assisted processing method is useful for other particle systems across multiple applications.

Nanocrystalline materials are known to possess excellent radiation resistance due to high fraction of grain boundaries that act as defect sinks, provided they are microstructurally stable at such extreme conditions. In this work, radiation response of a stable nanocrystalline Cu-Ta alloy is studied by irradiating with 4 MeV copper ions to doses (close to the surface) of 1 displacements per atom (dpa) at room temperature (RT); 10 dpa at RT, 573 and 723 K; 100 and 200 dpa at RT and 573 K. Nanoindentation results carried out for samples irradiated till 100 dpa at RT and 573 K show exceptionally low radiation hardening behavior compared to various candidate materials from literature. Results from microstructural characterization, using atom probe analysis and transmission electron microscopy, show a stable nanocrystalline microstructure with minimal grain growth and a meagre swelling in samples irradiated to 100 dpa (~0.2%) and 200 dpa at RT, while no voids in those at 573 K. This radiation tolerance is partly attributed to the stability of Ta nanoclusters due to phase separating nature of the alloy. Additionally, the larger tantalum particles are observed to undergo ballistic dissolution at doses greater than 100 dpa and are eventually precipitated as nanoclusters, replenishing the sink strength, which enhanced material's radiation tolerance when exposed to high irradiation doses and elevated temperatures.

The electrodeposition of copper in the presence of a magnetic field has previously been shown to affect both electrochemical processes and the microstructure. In this work, we report a magnetically-induced anomalous second phase in thick films deposited with a current density of 500 mA/cm2 and 0.5 T magnetic field strength. Atom probe tomography suggests the phase is a combination of CuH and CuH2. Formation of the latter phases is likely induced by the applied magnetic field and indicates its role in promoting the hydrogen evolution reaction during deposition. Keywords: Electrodeposition, Magnetic field, Hydride, Hydrogen evolution reaction, Atom probe tomography

Understanding the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is critical to inform sound policy decisions. We demonstrate how the transmission of undetected cases with pre-symptomatic, asymptomatic and mild symptoms, which are typically underreported due to lower testing capacity, explained the “bomb-like” behavior of exponential growth in the coronavirus disease 2019 (COVID-19) cases during early stages before the effects of large-scale, non-pharmaceutical interventions such as social distancing, school closures, or lockdowns. Using a Bayesian approach to epidemiological compartmental modeling, we captured the initial stages of the pandemic resulting in the explosion of cases and compared the parameter estimation with empirically measured values from the current knowledgebase. Parameter estimation was conducted using Markov chain Monte Carlo (MCMC) sampling methods with a Bayesian inference framework to estimate the proportion of undetected cases. Using data from the exponential phase of the pandemic prior to the implementation of interventions we estimated the basic reproductive number (R0) and symptomatic rates in Italy, Spain, South Korea, New York City, and Chicago. From this modeling study, R0 was estimated to be 3·25 (95% CrI, 1·09-29·77), 3·62 (95% CrI, 1·13-34·89), 2·75 (95% CrI, 1·04-22·44), 3·31 (95% CrI, 1·69-20·55), and 3·46 (95% CrI, 1·01-34·41), respectively. For all locations, 3-25% of infected patients were identified with moderate and severe symptoms in the early stage of the COVID-19 pandemic. Our modeling results support the mounting evidence that potentially large fractions of the infected population were undetected with asymptomatic and mild symptoms. Furthermore, a significant number of models of transmission that do not account for these asymptomatic cases may lead to an underestimation of R0 and, subsequently, policies that do not sufficiently reduce transmission to contain the spread of the virus. Detecting asymptomatic transmission can help slow down the spread of SARS-CoV-2. Author Summary The spread of SARS-CoV-2 has led to a global pandemic that is still spreading across countries. We fitted a mathematical model to reported infections in Spain, Italy, South Korea, New York, and Chicago before any large-scale interventions, such as lockdowns and school closures, and found that undetected infected individuals drove the accelerated pace of transmissions. Due to the limited capacity in testing in many of the five locations, undetected cases were most likely asymptomatic and mild to moderate symptomatic infections. Given the explosive nature in the number of cases during the early phase of the pandemic and the latest serological surveys, our study suggested that most active cases were undetected. Other cohort studies have shown that a significant proportion of cases reported little or no symptoms. We also showed that early detection of asymptomatic and mild symptomatic cases can lead to a slower spread of SARS-CoV-2 as evident in South Korea. Policies targeting symptomatic individuals, such as travel restrictions on affected areas or quarantines of sick individuals, are not as effective because they neglect asymptomatic transmission events.

The role of kinetically and thermodynamically driven microstructural evolution on FeNiZr was explored through in situ XRD analysis. A statistical approach based on log-likelihoods and composite link model was used to fit and extract important data from the XRD patterns. Best practices on using the statistical approach to obtained quantitative information from the XRD patterns was presented. It was shown that the alloyed powder used in the current study presents more thermodynamic stability than previously reported ball-milled powders. Based on hardness values, it was shown that mechanical strength is expected to be retained at higher processing temperatures. Lastly, a 2-dimensional heat transfer model was used to understand heat flow through the powder compacts.

We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron carbide (B–C) system. Through statistical data analytics, ReaxFF’s parametric complexity was overcome via dimensional reduction (55 parameters) while retaining enough degrees of freedom to capture most of the variability in responses. Two potentials were identified which improved on an existing parameterization for the objective set if interest, showcasing the framework’s capabilities.

Tailoring microstructure evolution with the use of external forces (magnetic, electric, mechanical, etc.) can expand the ability to control material properties and performance through microstructure engineering. In particular, crystalline materials are comprised of a distribution of grains of differing sizes, crystallographic orientations (texture), and topologies, which may evolve differently under the influence of various fields and lead to changes in material properties, bounded by movable grain boundaries. In this work, simulations are used to demonstrate the effect of an applied field, in this case a magnetic field, on the evolution of microstructure at the grain scale. The simulation results show that fields can be used to control microstructure geometry and texture. They also show that a dynamic field application of the field, through a changing field direction over time, can create new microstructure features even in a system that does not seem as sensitive to as static field, and that temperature can play a key role in this evolution as well. This work overall demonstrates how unconventional processing techniques on polycrystalline microstructures can be impacted by applying fields, and ultimately be used to improve properties and performance through engineered microstructures.

Processing of materials under an externally applied magnetic field could enable exploitation of broader processing spaces, affecting the relative stability of phases. To properly predict how a material will react to an applied field, the magnetic properties must be incorporated into the free energy calculations. Previous works assumed alloy composition played a negligible effect on the magnetization term; however, this assumption is not valid for all systems. Here, we assess twelve binary iron systems to quantify the shifts of magnetic moment and Curie temperature with respect to alloy content. Descriptive magnetic property equations for these binary systems were assessed in conjunction with experimental data and prior descriptions obtained from literature. To showcase the impact of using the compositionally dependent magnetic property predictions, the austenite loops for Fe–Si and Fe–Mo were re-calculated under an applied magnetic field. Further, the magnetic property data summarized herein can also be used for future assessments or re-assessments of the iron alloy systems reported, in addition to the current goal of processing under magnetic fields.

Predicting grain growth in nanocrystalline materials requires modeling approaches that incorporate grain boundary thermodynamics and kinetics. In this work, the thermokinetic model of Chen et al. (2012) for grain growth was applied to experimental X-ray diffraction measurements from a binary nanocrystalline alloy in an effort (1) to understand the influence of thermodynamic, kinetic, and material parameters in the model; and (2) to extend the thermokinetic model by incorporating temperature dependence. The model performs well for the grain boundary saturated case in the binary nanocrystalline alloy, where it is assumed that solute segregates to the grain boundaries and thermodynamically/kinetically reduces the driving force for grain growth. In this work, a sensitivity analysis of parameters (Monte Carlo global sensitivity analysis) identifies the important thermodynamic/kinetic parameters and their correlation with one another for the present model. This model was then extended to include the change in these independent thermodynamic/kinetic parameters as a function of temperature and to model the effect of initial grain size distribution. This research shows that the thermodynamic and kinetic contributions can describe grain growth in nanocrystalline materials and this extended model can be parameterized for grain size evolution and stabilization with temperature for nanocrystalline systems.

Classical molecular dynamics (MD), along with a bond-order potential for GaAs, has been used to study threshold displacement energies (Ed) of Ga and As recoils. Considering the crystallographic symmetry of GaAs, recoil events are confined in four unit stereographic triangles. To investigate the displacement energy’s dependence on crystallographic orientation, more than 3600 recoil events were simulated to uniformly sample values of Ed. Various defect configurations produced at these low energy recoils and the separation distances of Frenkel pairs were quantified and outlined. For both Ga and As, the minimum, $E_{\rm{d}}^{{\rm{min}}}$ , is found to be 8 eV, but the maxima, $E_{\rm{d}}^{{\rm{max}}}$ , are 22 and 28 eV for Ga and As, respectively. The distribution of Ed within unit stereographic triangles indicates that Ed shows a weak dependence on the recoil directions, in contrast to other semiconductors. The average threshold displacement energy is 13 ± 1 eV, which is in excellent agreement with available experiments.

The objective of this research is to demonstrate how similarity metrics can be used to quantify differences between sets of diffraction patterns. A set of 49 similarity metrics is implemented to analyze and quantify similarities between different Gaussian-based peak responses, as a surrogate for different characteristics in X-ray diffraction (XRD) patterns. A methodological approach was used to identify and demonstrate how sensitive these metrics are to expected peak features. By performing hierarchical clustering analysis, it is shown that most behaviors lead to unrelated metric responses. For instance, the results show that the Clark metric is consistently one of the most sensitive metrics to synthetic single peak changes. Furthermore, as an example of its utility, a framework is outlined for analyzing structural changes because of size convergence and isotropic straining, as calculated through the virtual XRD patterns.

Boron-based ceramics are appealing for lightweight applications in both vehicle and personnel protection, stemming from their combination of high hardness, high elastic modulus, and low density as compared to other ceramics and metal alloys. However, the performance of these ceramics and ceramic composites is lacking because of their inherent low fracture toughness and reduced strength under high-velocity threats. The objective of the present article is to briefly discuss both the challenges and the state of the art in experimental and computational approaches for engineering grain boundaries in boron-based armor ceramics, focusing mainly on boron carbide (B4C) and boron suboxide (B6O). The experimental challenges involve processing these ceramics at full density while trying to promote microstructure features such as intergranular films to improve toughness during shock. Many of the computational challenges for boron-based ceramics stem from their complex crystal structure which has hitherto complicated the exploration of grain boundaries and interfaces. However, bridging the gaps between experimental and computational studies at multiple scales to engineer grain boundaries in these boron-based ceramics may hold the key to maturing these material systems for lightweight defense applications.

Fused Filament Fabrication (FFF) additive manufacturing (AM) is a popular and widespread polymer AM method that provides rapid part production with relatively low cost prototyping. Polymeric structures made through FFF AM typically have internal pore structures which result from the manufacturing process. A Monte Carlo algorithm is implemented to simulate pore morphology evolution from build-path networks to spheroidal pores in AMed post-annealed structures. The model demonstrated that the evolution of these pore networks phenomenologically follows stages associated with the Plateau-Rayleigh instability and that thermal gradients result in migration of the spherical pores, as was observed experimentally.