Processing and characterization of TiB 2 -TiNiFeCrCoAl high-entropy alloy composite
Not provided.
Materials Science↗
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Practical Aspects of Stabilized FEM Discretizations of Nonlinear Conservation Law Systems with Convex Extension
This talk considers simplified finite element discretization techniques for first-order systems of conservation laws equipped with a convex (entropy) extension. Using newly developed techniques in entropy symmetrization theory, simplified forms of the Galerkin least-squares (GLS) and the discontinuous Galerkin (DG) finite element method have been developed and analyzed. The use of symmetrization variables yields numerical schemes which inherit global entropy stability properties of the POE system. Central to the development of the simplified GLS and DG methods is the Degenerative Scaling Theorem which characterizes right symmetrizes of an arbitrary first-order hyperbolic system in terms of scaled eigenvectors of the corresponding flux Jacobean matrices. A constructive proof is provided for the Eigenvalue Scaling Theorem with detailed consideration given to the Euler, Navier-Stokes, and magnetohydrodynamic (MHD) equations. Linear and nonlinear energy stability is proven for the simplified GLS and DG methods. Spatial convergence properties of the simplified GLS and DO methods are numerical evaluated via the computation of Ringleb flow on a sequence of successively refined triangulations. Finally, we consider a posteriori error estimates for the GLS and DG demoralization assuming error functionals related to the integrated lift and drag of a body. Sample calculations in 20 are shown to validate the theory and implementation.
Barth, Timothy↗
Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science
Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for MaxEnt models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical MaxEnt models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth MaxEnt models. Our proposed first-order algorithms leverage the Kullback–Leibler divergence to train large-scale and non-smooth MaxEnt models efficiently. For MaxEnt models with discrete probability distribution of n elements built from samples, each containing m features, the stepsize parameter estimation and iterations in our algorithms scale on the order of O(mn) operations and can be trivially parallelized. Moreover, the strong ℓ1 convexity of the Kullback–Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the art by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.
Physics↗
Reactive flash sintering and characterization of bulk high entropy nitrides
Over the past decade, numerous high-entropy ceramics have been synthesized, often displaying attractive properties. However, the study on facile preparation of bulk high entropy nitrides (HEN) are limited, despite its broad potential applications. This research demonstrates for the first time rapid fabrication (within ∼6 min) of bulk high-entropy nitrides, especially (Al 0.17 Nb 0.17 Ta 0.17 Ti 0.32 Zr 0.17 )N, from binary nitride powder mixtures using a highly efficient reactive flash sintering (RFS) technique. X-ray diffraction (XRD) shows the HENs from RFS are near single-phase solid solutions with a rock salt crystal structure, while in situ synchrotron study carried out during RFS captured in real time the formation of HEN, which was preserved upon cooling, suggesting thermodynamic stability of the HEN phase, even up to extreme pressure (∼35.6 GPa). Microscopic analyses using SEM, STEM, and EDS reveal decent uniformity for HEN with no obvious segregation of elements, even to submicron scale. Some properties of the obtained bulk HENs are consistent with expectations. For example, their hardness and bulk modulus are close to estimates based on rule-of-mixture (ROM) values from the constituent binary nitrides. Meanwhile, some other measured properties seem to show surprises. For example, the fracture toughness for the HENs (e.g., 7.81 ± 1.40 MPa•m 1/2 or higher) turns out to be more than double of the expected ROM estimates. The significantly improved fracture toughness is attributed to the observed nano-layered structure of the HENs, despite the HEN’s cubic crystal structure and high hardness. In addition, the oxidation resistance shows improvement up till ∼800°C, possibly due to Ta doping that suppress oxygen vacancy formation in the oxide shell, while the 5-metal HEN of (Al 0.17 Nb 0.17 Ta 0.17 Ti 0.32 Zr 0.17 )N displays superconductivity (T c of ∼5–7 K from magnetism and resistivity measurements, slightly lower than ROM estimate), despite insulating property of starting AlN. Furthermore, future study combining experimental investigation using larger samples to confirm the observed increase in fracture toughness and oxidation resistance, theoretical modeling at different length scale, and more detailed structural/chemical characterization, especially at the atomic scale, are all needed to fully understand the inter-relationships between composition, processing, structure, and novel properties for these HENs and the development of related new materials for different applications.
Flash sintering↗
A Lagrangian trajectory view on transport and mixing processes between the eye, eyewall, and environment using a high resolution simulation of Hurricane Bonnie (1998)
The transport and mixing characteristics of a large sample of air parcels within a mature and vertically sheared hurricane vortex is examined. Data from a high-resolution (2 km grid spacing) numerical simulation of "real-case" Hurricane Bonnie (1998) is used to calculate Lagrangian trajectories of air parcels in various subdomains of the hurricane (namely, the eye, eyewall, and near-environment) to study the degree of interaction (transport and mixing) between these subdomains. It is found that 1) there is transport and mixing from the low-level eye to the eyewall that carries high- Be air which can enhance the efficiency of the hurricane heat engine; 2) a portion of the low-level inflow of the hurricane bypasses the eyewall to enter the eye, that both replaces the mass of the low-level eye and lingers for a sufficient time (order 1 hour) to acquire enhanced entropy characteristics through interaction with the ocean beneath the eye; 3) air in the mid- to upper-level eye is exchanged with the eyewall such that more than half the air of the eye is exchanged in five hours in this case of a sheared hurricane; and 4) that one-fifth of the mass in the eyewall at a height of 5 km has an origin in the mid- to upper-level environment where thet(sub e) is much less than in the eyewall, which ventilates the ensemble average eyewall theta(sub e) by about 1 K. Implications of these findings to the problem of hurricane intensity forecasting are discussed.
Cram, Thomas A.↗
From Rate Measurements to Mechanistic Data for Condensed Matter Reactions: A Case Study Using the Crystallization of [Zn(OH 2 ) 6 ][ZnCl 4 ]
The kinetics of crystallization of the R = 3 hydrate of zinc chloride, [Zn(OH 2 ) 6 ][ZnCl 4 ], is measured by time-resolved synchrotron x-ray diffraction, time-resolved neutron diffraction, and by differential scanning calorimetry. It is shown that analysis of the rate data using the classic Kolmogorov, Johnson, Mehl, Avrami (KJMA) kinetic model affords radically different rate constants for equivalent reaction conditions. Reintroducing the amount of sample measured by each method into the kinetic model, using our recently developed modified-KJMA model (M-KJMA), it is shown that each of these diverse rate measurement techniques can give the intrinsic, material specific rate constant, the velocity of the phase boundary, v pb . These data are then compared to the velocity of the crystallization front directly measured optically. The time-resolved diffraction methods uniquely monitor the loss of the liquid reactant and formation of the crystalline product demonstrating that the crystallization of this hydrate phase proceeds through no intermediate phases. The temperature dependent v pb data are then well fit to transition zone theory to extract activation parameters. These demonstrate that the rate-limiting component to this crystallization reaction is the ordering of the waters (or protons) of hydration into restricted positions of the crystalline lattice resulting in large negative entropy of activation.
36 MATERIALS SCIENCE↗
Active learning of ternary alloy structures and energies
Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.
Chemistry↗
STAR FORMATION ACTIVITY IN CLASH BRIGHTEST CLUSTER GALAXIES
The CLASH X-ray selected sample of 20 galaxy clusters contains 10 brightest cluster galaxies (BCGs) that exhibit significant (>5σ) extinction-corrected star formation rates (SFRs). Star formation activity is inferred from photometric estimates of UV and Hα+[N ii] emission in knots and filaments detected in CLASH Hubble Space Telescope ACS and WFC3 observations. UV-derived SFRs in these BCGs span two orders of magnitude, including two with a SFR ≳ 100 M{sub ⊙} yr{sup −1}. These measurements are supplemented with [O ii], [O iii], and Hβ fluxes measured from spectra obtained with the SOAR telescope. We confirm that photoionization from ongoing star formation powers the line emission nebulae in these BCGs, although in many BCGs there is also evidence of a LINER-like contribution to the line emission. Coupling these data with Chandra X-ray measurements, we infer that the star formation occurs exclusively in low-entropy cluster cores and exhibits a correlation with gas properties related to cooling. We also perform an in-depth study of the starburst history of the BCG in the cluster RXJ1532.9+3021, and create 2D maps of stellar properties on scales down to ∼350 pc. These maps reveal evidence for an ongoing burst occurring in elongated filaments, generally on ∼0.5–1.0 Gyr timescales, although some filaments are consistent with much younger (≲100 Myr) burst timescales and may be correlated with recent activity from the active galactic nucleus. The relationship between BCG SFRs and the surrounding intracluster medium gas properties provide new support for the process of feedback-regulated cooling in galaxy clusters and is consistent with recent theoretical predictions.
79 ASTRONOMY AND ASTROPHYSICS↗
FCRD Advanced Reactor (Transmutation) Fuels Handbook
Transmutation of minor actinides such as Np, Am, and Cm in spent nuclear fuel is of international interest because of its potential for reducing the long-term health and safety hazards caused by the radioactivity of the spent fuel. One important approach to transmutation (currently being pursued by the DOE Fuel Cycle Research & Development Advanced Fuels Campaign) involves incorporating the minor actinides into U-Pu-Zr alloys, which can be used as fuel in fast reactors. U-Pu-Zr alloys are well suited for electrolytic refining, which leads to incorporation rare-earth fission products such as La, Ce, Pr, and Nd. It is, therefore, important to understand not only the properties of U-Pu-Zr alloys but also those of U-Pu-Zr alloys with concentrations of minor actinides (Np, Am) and rare-earth elements (La, Ce, Pr, and Nd) similar to those in reprocessed fuel. In addition to requiring extensive safety precautions, alloys containing U, Pu, and minor actinides (Np and Am) are difficult to study for numerous reasons, including their complex phase transformations, characteristically sluggish phasetransformation kinetics, tendency to produce experimental results that vary depending on the histories of individual samples, rapid oxidation, and sensitivity to contaminants such as oxygen in concentrations below a hundred parts per million. Although less toxic, rare-earth elements such as La, Ce, Pr, and Nd are also difficult to study for similar reasons. Many of the experimental measurements were made before 1980, and the level of documentation for experimental methods and results varies widely. It is, therefore, not surprising that little is known with certainty about U-Pu-Zr alloys, particularly those that also contain minor actinides and rare-earth elements. General acceptance of results commonly indicates that there is only a single measurement for a particular property. This handbook summarizes currently available information about U, Pu, Zr, Np, Am, La, Ce, Pr, and Nd and alloys of two or three of these elements. It contains information about phase diagrams and related information (including phases and phase transformations); heat capacity, entropy, and enthalpy; thermal expansion; and thermal conductivity and diffusivity. In addition to presenting information about materials properties, the handbook attempts to provide information about how well the property is known and how much variation exists between measurements. Although it includes some results from models, its primary focus is experimental data. The Handbook is organized in two sections: one with information about the U-Pu-Zr ternary and one with information about other elements and binary and vi ternary alloys in the U-Np-Pu-Am-La-Ce-Pr-Nd-Zr system. Within each section, information about elements is presented first, followed by information about binary alloys, then information about ternary alloys. The order in which the elements in each alloy are mentioned follows the order in the first sentence of this paragraph. Much of the information on the U-Pu-Zr system repeats information from the FCRD Transmutation Fuels Handbook 2015. Most of the other data has been published elsewhere (although scattered throughout numerous references, some quite obscure); however, some data from Idaho National Laboratory is presented here for the first time. As the FCRD programmatic mission evolves, future editions of this handbook will begin to include other advanced reactor fuel designs and compositions. Hence, the title of the handbook will transition to the Advanced Reactor Fuels Handbook.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Phenomenological Model for Modeling Magnetocaloric Properties in TmZn Sample
The magnetocaloric properties of the polycrystalline TmZn sample, obtained in a 2–15.4 K range temperature, under 0.1 T magnetic field and various hydrostatic pressures, have been theoretically investigated and compared with experimental results. It has been found that the behavior indicating the first-order metamagnetic transition of TmZn has been progressively eliminated with raising the hydrostatic pressure. Moreover, the Curie temperature increases gradually with the increase of the hydrostatic pressure. The agreement between simulation and experimental results confirms that the phenomenological model can investigate thermo-metamagnetic properties in TmZn system.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Magnetocaloric Effect of Micro- and Nanoparticles of Gd 5 Si 4
We report that materials exhibiting a large magnetocaloric effect (MCE) at or near room temperature are critical for solid-state refrigeration applications. The MCE is described by a change in entropy (ΔS M ) and/or temperature (ΔT ad ) of a material in response to a change in applied magnetic field. Ball milled materials generally exhibit smaller ΔS M values compared to bulk; however, milling broadens the effect, potentially increasing the relative cooling power (RCP). The as-cast Gd 5 Si 4 is an attractive option due to its magnetic transition at 340 K and associated MCE. Investigation of effect of particles size and transition temperature in the binary material, Gd 5 Si 4 , can lead to development of functionally graded bulk material with higher MCE and RCP than the traditional bulk materials. A two-step ball-milling process, in which coarse powder of Gd 5 Si 4 was first milled with poly(ethylene glycol) followed by milling in heptane was used to produce fine particles of Gd 5 Si 4 that showed a broad distribution in particle size. Magnetic measurement on the milled sample obtained after washing with water show a decrease in Curie temperature and significant broadening of the magnetic transition. Compared to bulk Gd 5 Si 4 , the maximum MCE of the milled samples is also reduced and shifted down by close to 30 K, but the MCE remains substantial over a broader temperature range. Lastly, the RCP of both milled samples increased 75% from the bulk material.
36 MATERIALS SCIENCE↗
Material coherence from trajectories via Burau eigenanalysis of braids
In this paper, we provide a numerical tool to study a material’s coherence from a set of 2D Lagrangian trajectories sampling a dynamical system, i.e., from the motion of passive tracers. We show that eigenvectors of the Burau representation of a topological braid derived from the trajectories have levelsets corresponding to components of the Nielsen–Thurston decomposition of the dynamical system. One can thus detect and identify clusters of space–time trajectories corresponding to coherent regions of the dynamical system by solving an eigenvalue problem. Unlike previous methods, the scalable computational complexity of our braid-based approach allows the analysis of large amounts of trajectories.
36 MATERIALS SCIENCE↗
Identifying the Role of Magnesium Content in Assessing the Electrochemical Performance of (CoCuMgNiZn)O
High-entropy oxides (HEOs) featuring 5 or more metals in approximately equimolar ratios, such as the prototypical rock-salt-structured (CoCuMgNiZn)O, have attracted interest for their potential to display material properties superior to oxides with combinations of 4 or fewer of the component metals. In particular, (CoCuMgNiZn)O has shown promise as an anode for lithium-ion batteries with a high specific capacity retention over extended cycling. Previous studies have suggested that magnesium, despite being electrochemically inert, provides a crucial contribution to the favorable performance of this HEO by stabilizing the crystal structure through repeated charge–discharge cycles. This paper probes the extent and mechanism of the magnesium effect by using a facile microwave-assisted hydrothermal synthesis method to vary the level of Mg content. Moreover, we extensively characterized the product with techniques such as 4D-STEM and ICP-OES, which have not previously been applied in combination with this material, in order to elucidate the relationships among chemical composition, nanostructure, and performance. Here, we show that the level of Mg incorporation is positively correlated with long-term stability and negatively correlated with rate capacity, and that the latter effect yields a stronger influence upon the overall performance, with the best-performing sample possessing a Mg quantity equivalent to ∼1/5 that of an equimolar concentration. This finding demonstrates not only that the variation of individual elemental levels offers a promising and relatively unexplored avenue to optimize the electrochemical performance of HEO materials but also that it should not be assumed that equimolar compositions of constituent elements are necessarily the best.
36 MATERIALS SCIENCE↗
Magnetocaloric properties of rapidly solidified Dy{sub 3}Co alloy ribbons
The magnetic and magnetocaloric (MC) properties of melt-spun ribbons of the Dy{sub 3}Co intermetallic compound were investigated. Samples were fabricated in an Ar environment using a homemade melt spinner system at a linear speed of the rotating copper wheel of 40 ms{sup −1}. X-ray diffraction analysis shows that ribbons crystallize into a single-phase with the Fe{sub 3}C-type orthorhombic crystal structure. The M(T) curve measured at 5 mT reveals the occurrence of a transition at 32 K from a first to a second antiferromagnetic (AFM) state and an AFM-to-paramagnetic transition at T{sub N} = 43 K. Furthermore, a metamagnetic transition is observed below T{sub N}, but the magnetization change ΔM is well below the one reported for bulk alloys. Below 12 K, large inverse MC effect and hysteresis losses are observed. This behavior is related to the metamagnetic transition. For a magnetic field change of 5 T (2 T) applied along the ribbon length, the produced ribbons show a peak value of the magnetic entropy change ΔS{sub M}{sup peak} of −6.5 (− 2.1) Jkg{sup −1}K{sup −1} occurring close to T{sub N} with a full-width at half-maximum δT{sub FWHM} of 53 (37) K, and refrigerant capacity RC = 364 (83) Jkg{sup −1} (estimated from the product |ΔS{sub M}{sup peak}| × δT{sub FWHM})
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment
The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.
US Army collaboration↗
Rapid Bayesian High Entropy Alloy Designs Fabricated via Wire Arc Additive Manufacturing
Purpose: This project seeks to demonstrate a new high-throughput (rapid) alloy design technique applied to creating new high entropy alloys (HEAs) for extreme environments. High entropy alloys shift the design paradigm from being focused on a single principal element (e.g. nickel-based alloys) to target alloys that include high atomic fractions (X >10%) of multiple elements. These HEA materials can exhibit sluggish diffusion and enhanced corrosion resistance, ideal for potential applications in advanced ultra supercritical (A-USC) steam cycles for power generation. Scope: The addition of multiple elements in high atomic fractions creates an enormous design space that cannot easily be investigated by traditional material design strategies such as designed of experiments (DOE). This project utilizes a Bayesian machine learning algorithm that has been modified to work with calculation of phase diagrams (CALPHAD) software. This Bayesian algorithm reduces manual inputs and increase the likelihood of achieving an optimal solution. Compositional inputs to this algorithm will be assessed using existing material property models for high temperature strength and corrosion resistance. The target for alloy performance will be a 15% (~100 ⁰C) increase in allowable service temperature beyond heat-resistant stainless steels while maintaining or improving alloy cost and corrosion resistance. Haynes 230 was selected as a baseline, which is 57 wt% Ni with 22 wt% Cr 14 wt% W, and 2 wt% Mo as solid solution strengtheners. In addition to rapid design via Bayesian machine learning, the alloys were rapidly fabricated using a multi-wire arc additive manufacturing (mWAAM) technique which allows for precise control of alloy composition and assessing of alloy design “windows” to study composition effects. Build speeds for wire-arc additive processes are among the highest for additive technologies enabling rapid and reliable sample fabrication when compared to conventional methods such as arc button melting. The mWAAM samples will be rapidly characterized via instrumented indentation for room temperature modulus and strength and for elevated temperature strength via hot hardness tests. After being screened with hardness testing, potential alloys will be further evaluated with conventional microscopy techniques including scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to assess agreement with modeling results. The most promising compositions will also be evaluated by printing full sized tensile specimens for mechanical behavior tests at elevated temperatures. Results: Bayesian machine learning of a single performance function was initially used to optimize five performance metrics: 1) single phase stability, 2) yield strength, 3) creep resistance (low diffusion coefficient), 4) freezing range (weldability), and 5) material cost. The single performance function was suboptimal as assumptions had to be made about the results while formulating the optimization. A goal-oriented Bayesian optimization strategy (Hanaoka, 2021) was implemented with CALPHAD for use with the five metrics above. This multi-objective Bayesian optimization (MOBO) enabled the design of NiCrCoFe alloys with V and W additions. A base composition of NiCoCr was selected as Ni provides a stable FCC matrix, Cr aids corrosion/oxidation resistance, and Co is a solid-solutions strengthener that also improves creep by increasing the activation energy. Fe helps reduce diffusion coefficients and cost. Finally, V and W were selected for their reasonable solubility and high atomic misfit to aid in solid solution strengthening. Cracking of the mWAAM specimens was an early issue, and the Easton solidification cracking model (Easton et al., 2014a) was selected for addition to the MOBO function. High performing alloys fabricated by mWAAM included Ni 28 Cr 25 Co 26 Fe 15 V 8 and Ni 62 Cr 18 Co 1 Fe 3 W 15 . It was observed that even after adapting the mWAAM process for W, the W did not fully dissolve. To fully evaluate the Ni 62 Cr 18 Co 1 Fe 3 W 15 composition, a cored wire (80-20 NiCr sheath/powder core) was manufactured and printed via WAAM, and HIP’ing was utilized to homogenize and densify the printed alloy. The V and W alloys produced met metrics 1 (solid solution), 4 (solidification cracking), and 5 (cost). However, an unmodeled mechanism of thermal stress cracking was identified in the WAAM produced materials, perhaps exacerbated by the lack of grain boundary strengthening elements (B, C). Conclusions & Recommendations: A high-throughput (rapid) alloy design technique was applied to designing and manufacturing new high entropy alloys (HEAs) for extreme environments utilizing MOBO and mWAAM. The developed process was rapid and effective in addressing the mechanisms included in the model. The lack of grain boundary strengthening element additions (e.g., B, C) was a simplification that likely produced thermal stress cracking that turned into a large part of the investigation. Additions on the order of 0.005 wt% B and 0.05 wt% C likely would have minimized thermal stress grain boundary cracking. Overall, the high throughput design strategy is promising for rapid design of metrics-driven alloys for advanced ultra supercritical (A-USC) steam cycles for power generation. The MOBO and mWAAM process could be commercialized to accelerate metrics-driven alloy design. In addition, the cored-wire process utilized for scale-up is a promising high-volume process for WAAM alloy development and scale-up.
36 MATERIALS SCIENCE↗
Mysterious SiB 3 : Identifying the Relation between α- and β-SiB 3
Binary silicon boride SiB 3 has been reported to occur in two forms, as disordered and nonstoichiometric α-SiB 3–x , which relates to the α-rhombohedral phase of boron, and as strictly ordered and stoichiometric β-SiB 3 . Similar to other boron-rich icosahedral solids, these SiB 3 phases represent potentially interesting refractory materials. However, their thermal stability, formation conditions, and thermodynamic relation are poorly understood. Here, we map the formation conditions of α-SiB 3–x and β-SiB 3 and analyze their relative thermodynamic stabilities. α-SiB 3–x is metastable (with respect to β-SiB 3 and Si), and its formation is kinetically driven. Pure polycrystalline bulk samples may be obtained within hours when heating stoichiometric mixtures of elemental silicon and boron at temperatures 1200–1300 °C. At the same time, α-SiB 3–x decomposes into SiB6 and Si, and optimum time-temperature synthesis conditions represent a trade-off between rates of formation and decomposition. The formation of stable β-SiB 3 was observed after prolonged treatment (days to weeks) of elemental mixtures with ratios Si/B = 1:1–1:4 at temperatures 1175–1200 °C. The application of high pressures greatly improves the kinetics of SiB 3 formation and allows decoupling of SiB 3 formation from decomposition. Quantitative formation of β-SiB 3 was seen at 1100 °C for samples pressurized to 5.5–8 GPa. β-SiB 3 decomposes peritectoidally at temperatures between 1250 and 1300 °C. The highly ordered nature of β-SiB 3 is reflected in its Raman spectrum, which features narrow and distinct lines. In contrast, the Raman spectrum of α-SiB 3–x is characterized by broad bands, which show a clear relation to the vibrational modes of isostructural, ordered B 6 P. The detailed composition and structural properties of disordered α-SiB 3–x were ascertained by a combination of single-crystal X-ray diffraction and 29 Si magic angle spinning NMR experiments. Notably, the compositions of polycrystalline bulk samples (obtained at T ≤ 1200 °C) and single crystal samples (obtained from Si-rich molten Si–B mixtures at T > 1400 °C) are different, SiB 2.93(7) and SiB 2.64(2) , respectively. The incorporation of Si in the polar position of B 12 icosahedra results in highly strained cluster units. This disorder feature was accounted for in the refined crystal structure model by splitting the polar position into three sites. The electron-precise composition of α-SiB 3–x is SiB 2.5 and corresponds to the incorporation of, on average, two Si atoms in each B 12 icosahedron. Accordingly, α-SiB 3–x constitutes a mixture of B 10 Si 2 and B 11 Si clusters. The structural and phase stability of α-SiB 3–x were explored using a first-principles cluster expansion. The most stable composition at 0 K is SiB 2.5 , which however is unstable with respect to the decomposition β-SiB 3 + Si. Modeling of the configurational and vibrational entropies suggests that α-SiB 3–x only becomes more stable than β-SiB 3 at temperatures above its decomposition into SiB 6 and Si. Hence, we conclude that α-SiB 3–x is metastable at all temperatures. Density functional theory electronic structure calculations yield band gaps of similar size for electron-precise α-SiB 2.5 and β-SiB 3 , whereas α-SiB 3 represents a p-type conductor.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Impact of reduced dimensionality on the correlation length and magnetization dynamics of the spin chain cobaltite Ca 3 Co 2 O 6
We have recently applied the magnetic field and temperature dependence of magnetic entropy change as a probe of the magnetic states of a single crystal of Ca 3 Co 2 O 6 to establish a more comprehensive magnetic phase diagram for this exotic system. The results are consistent with the spin-density wave description of the ground state and indicate the suppression of the modulated state in favor of a ferrimagnetic up-up-down arrangement of the spin chains with the application of a moderate field. We demonstrate, in this study, that the features of the static magnetic phase diagram are preserved when the grain size of Ca 3 Co 2 O 6 is reduced to 440 nm (below the maximum c-axis correlation length of ~550 nm in Ca 3 Co 2 O 6 ), although the polycrystalline materials show a non-saturating behavior, rounded steps, and reduced magnetization when compared to the single crystal. Our new finding of the multiple low-temperature magnetization steps in the 440 nm sample, together with conflicting reports concerning such steps in nanostructured samples, suggests that correlations in the ab plane are more important than correlations along the c-axis in the appearance of the plateaus. The relaxation processes in the system are studied through the decay of remanent magnetization, ac susceptibility, and Argand diagrams. The introduction of grain boundaries in Ca 3 Co 2 O 6 weakens the slow-dynamic state, widens the low-temperature distribution of relaxation times, and lowers activation energies within the Arrhenius-like relaxation regime.
36 MATERIALS SCIENCE↗