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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

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↗
DOE OSTI2019

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↗
DOE OSTI2020

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↗
DOE OSTI2025

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↗
DOE OSTI2015

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↗
NASA NTRS2019

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↗
DOE OSTI2025

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↗
DOE OSTI2019

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↗
DOE OSTI2019

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↗
DOE OSTI2023

Plastic homogeneity in nanoscale heterostructured binary and multicomponent metallic eutectics: An overview

Heterostructured materials comprised of relatively soft/hard disparate phases typically exhibit composite strengthening but lack plastic deformability at ambient temperatures. However, heterostructured systems comprised of nanoscale phases can simultaneously enhance yield strength and strain hardening, thereby promoting uniform distribution of plastic flow. In this review, the atomic-scale deformation mechanisms in model systems of eutectic alloys, Al-Al 2 Cu and Al-Si, refined to nanoscales via laser rapid solidification are discussed, and compared with literature on multi-component (high entropy) eutectics such as Ni-Al-Fe-based with Cr and/or Co additions. The nano-lamellar Al-Al 2 Cu structures exhibit unit defect mechanisms not reported in monolithic Al 2 Cu intermetallic: localized shear on {0 1 1} and shear-induced faults on {1 2 1} planes, constrained by closely-spaced dislocation arrays in Al confined by Al/Al 2 Cu interfaces. The unexpected plasticity mechanisms are enabled by slip continuity in nanoscale Al-Al 2 Cu eutectics associated with the orientation relationship and interface habit planes. In nano-fibrous Al-Si eutectic, tensile ductility at strength approaching 600 MPa is observed resulting from dislocation plasticity in the nano-Al channels and cracking in Si nanofibers. Molecular dynamics simulations show that Al dislocations easily cross-slip (screw) or climb (edge) along Al-Si interfaces, making slip transmission difficult. The propagation of nano-cracks is suppressed by surrounding strain hardening Al, retaining good ductility of the sample, in spite of lack of direct slip transmission. Finally, the critical unit mechanisms of slip transmission and interface-enabled plasticity observed in nanoscale eutectic binary systems can also explain the strength-ductility relationship in multi-component eutectics and homogeneously distributed plastic flow with increasing microstructural heterogeneity.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Effect of inter-species selective interactions on the thermodynamics and nucleation free-energy barriers of a tessellating polyhedral compound

The phase behavior and the homogeneous nucleation of an equimolar mixture of octahedra and cuboctahedra are studied using thermodynamic integration, Gibbs-Duhem integration, and umbrella sampling simulations. The components of this mixture are modeled as polybead objects of equal edge lengths so that they can assemble into a space-filling compound with the CsCl crystal structure. Taking as reference the hard-core system where the compound crystal does not spontaneously nucleate, we quantified the effect of inter-species selective interactions on facilitating the disorder-to-order transition. Facet selective and facet non-selective inter-species attractions were considered, and while the former was expectedly more favorable toward the target tessellating structure, the latter was found to be similarly effective in nucleating the crystal compound. Ranges for the strength of attractions and degree of supersaturation were identified where the nucleation free-energy barrier was small enough to foretell a fast process but large enough to prevent spinodal fluctuations that can trap the system in dense metastable states lacking long-range order. At those favorable conditions, the tendency toward the local orientational order favored by packing entropy is amplified and found to play a key role seeding nuclei with the CsCl structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

An Analysis Framework for Understanding the Origin of Nuclear Activity in Low-power Radio Galaxies

Using large samples containing nearly 2300 active galaxies of low radio luminosity (1.4 GHz luminosity between 2 × 10{sup 23} and 3 × 10{sup 25} W Hz{sup −1}, essentially low-excitation radio galaxies) at z ≲ 0.3, we present a self-contained analysis of the dependence of the nuclear radio activity on both intrinsic and extrinsic properties of galaxies, with the goal of identifying the best predictors of the nuclear radio activity. While confirming the established result that stellar mass must play a key role on the triggering of radio activities, we point out that for the central, most massive galaxies, the radio activity also shows a strong dependence on halo mass, which is not likely due to enhanced interaction rates in denser regions in massive, cluster-scale halos. We thus further investigate the effects of various properties of the intracluster medium (ICM) in massive clusters on the radio activities, employing two standard statistical tools, principle component analysis and logistic regression. It is found that ICM entropy, local cooling time, and pressure are the most effective in predicting the radio activity, pointing to the accretion of gas cooling out of a hot atmosphere to be the likely origin in triggering such activities in galaxies residing in massive dark matter halos. Our analysis framework enables us to logically discern the mechanisms responsible for the radio activity separately for central and satellite galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗
DOE OSTI2021

Competing magnetic interactions and emergent phase diagrams in double perovskite Y 2 Ni x Co 1–x MnO 6

We present a comparative study of double perovskites Y 2 Ni x Co 1–x MnO 6 for x = 1, 0.5, and 0. The polycrystalline samples of Y 2 Ni x Co 1–x MnO 6 with space group P2 1 /n were synthesized via sol-gel technique. X-ray photoelectron spectroscopy (XPS) confirms the presence of majority Ni 2+ /Mn 4+ in Y 2 NiMnO 6 (YNMO) and a mixed valence state Ni 2+/3+ /Co 2+/3+ /Mn 4+/3+ in co-doped Y 2 Ni 0.5 Co 0.5 MnO 6 (YNCMO). The temperature dependent magnetization results suggest homogeneous substitution of Ni ions with Co ions with minimal contribution of 3+ cationic magnetic interactions in YNCMO. The superexchange ferromagnetic (FM) interaction has a major contribution to the magnetism of YNMO. The strength of antiferromagnetic (AFM) coupling resulting from the antiphase boundaries in Y 2 Ni x Co 1–x MnO 6 increases with the increase in the Co concentration. The AC susceptibility (AC-χ) study further validates the presence of higher density of antiphase boundaries, which is reflected by the dynamics of domain walls in YNCMO. The isothermal magnetic entropy change (ΔS M ) as functions of temperature and magnetic field is exploited to assess the stabilization of different magnetic phases. Finally, the findings of ΔS M (T,µ 0 H) lead to the proposed new magnetic phase diagrams for Y 2 Ni x Co 1–x MnO 6 for x = 1 and 0.5, in comparison with the previously established phase diagram of Y 2 CoMnO 6 .

36 MATERIALS SCIENCE↗
DOE OSTI2022

Antimicrobial properties of a multi-component alloy

High traffic touch surfaces such as doorknobs, countertops, and handrails can be transmission points for the spread of pathogens, emphasizing the need to develop materials that actively self-sanitize. Metals are frequently used for these surfaces due to their durability, but many metals also possess antimicrobial properties which function through a variety of mechanisms. This work investigates metallic alloys comprised of several metals which individually possess antimicrobial properties, with the target of achieving broad-spectrum, rapid sanitation through synergistic activity. An entropy-motivated stabilization paradigm is proposed to prepare scalable alloys of copper, silver, nickel and cobalt. Using combinatorial sputtering, thin-film alloys were prepared on 100 mm wafers with ≈50% compositional grading of each element across the wafer. The films were then annealed and investigated for alloy stability. Antimicrobial activity testing was performed on both the as-grown alloys and the annealed films using four microorganisms—Phi6, MS2, Bacillus subtilis and Escherichia coli —as surrogates for human viral and bacterial pathogens. Testing showed that after 30 s of contact with some of the test alloys, Phi6, an enveloped, single-stranded RNA bacteriophage that serves as a SARS-CoV-2 surrogate, was reduced up to 6.9 orders of magnitude (> 99.9999%). Additionally, the non-enveloped, double-stranded DNA bacteriophage MS2, and the Gram-negative E. coli and Gram-positive B. subtilis bacterial strains showed a 5.0, 6.4, and 5.7 log reduction in activity after 30, 20 and 10 min, respectively. Antimicrobial activity in the alloy samples showed a strong dependence on the composition, with the log reduction scaling directly with the Cu content. Concentration of Cu by phase separation after annealing improved activity in some of the samples. The results motivate a variety of themes which can be leveraged to design ideal antimicrobial surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Formation of multicomponent alloy particles in doped ceria under I 2+ ion irradiation and thermal annealing

Multicomponent alloys resembling the e-phase particles in nuclear fuel have the potential to immobilize 99 Tc in a stable waste form. Formation of such multicomponent alloys under extreme conditions is studied in a non-activated model system: CeO 2 doped with 2 wt% Mo, 1.5 wt% Ru, 0.75 wt% Pd, 0.5 wt% Re and 0.25 wt% Rh. The doped CeO 2 films were irradiated with I 2+ ions (610°C, 10 16 and 5×10 16 I 2+ /cm 2 ). For selected samples post-irradiation heat treatment was conducted (900°C, 1100°C). Analytical TEM revealed the formation of 5-20 nm precipitates containing Pd, Mo, and Re sometimes associated with cavities. Iodine was found to segregate to grain boundaries and cracks. After heat treatment at 1100°C, the CeO 2 matrix had recrystallized. Precipitates containing Pd, Mo, Re, and Ru were observed near the interface with the polycrystalline YSZ substrate. It follows that Pd is the most mobile element, followed by Mo and Re. Finally, while irradiation promotes precipitation, high-temperature effects are more significant.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Wavelet compression techniques for hyperspectral data

Hyperspectral sensors are electro-optic sensors which typically operate in visible and near infrared bands. Their characteristic property is the ability to resolve a relatively large number (i.e., tens to hundreds) of contiguous spectral bands to produce a detailed profile of the electromagnetic spectrum. In contrast, multispectral sensors measure relatively few non-contiguous spectral bands. Like multispectral sensors, hyperspectral sensors are often also imaging sensors, measuring spectra over an array of spatial resolution cells. The data produced may thus be viewed as a three dimensional array of samples in which two dimensions correspond to spatial position and the third to wavelength. Because they multiply the already large storage/transmission bandwidth requirements of conventional digital images, hyperspectral sensors generate formidable torrents of data. Their fine spectral resolution typically results in high redundancy in the spectral dimension, so that hyperspectral data sets are excellent candidates for compression. Although there have been a number of studies of compression algorithms for multispectral data, we are not aware of any published results for hyperspectral data. Three algorithms for hyperspectral data compression are compared. They were selected as representatives of three major approaches for extending conventional lossy image compression techniques to hyperspectral data. The simplest approach treats the data as an ensemble of images and compresses each image independently, ignoring the correlation between spectral bands. The second approach transforms the data to decorrelate the spectral bands, and then compresses the transformed data as a set of independent images. The third approach directly generalizes two-dimensional transform coding by applying a three-dimensional transform as part of the usual transform-quantize-entropy code procedure. The algorithms studied all use the discrete wavelet transform. In the first two cases, a wavelet transform coder was used for the two-dimensional compression. The third case used a three dimensional extension of this same algorithm.

Evans, Bruce↗
DOE OSTI2021

Ab initio prediction of an order-disorder transition in Mg 2 Ge O 4 : Implication for the nature of super-Earth's mantles

Here we present an ab initio prediction of an order-disorder transition (ODT) from a I¯42d-type to Th 3 P 4 -type phase in the cation sublattices of Mg 2 GeO 4 , a post-post-perovskite phase. This uncommon type of prediction is achieved by carrying out a high-throughput sampling of atomic configurations in a 56-atom supercell followed by a Boltzmann ensemble statistics calculation. Mg 2 GeO 4 is a low-pressure analog of I¯42d-type Mg 2 SiO 4 , a predicted major planet-forming phase of super-Earths' mantles. Therefore, a similar ODT is anticipated in I¯42d-type Mg 2 SiO 4 as well, which should impact the internal structure and dynamics of these planets. Furthermore, the prediction of this Th 3 P 4 -type phase in Mg 2 GeO 4 further enhances the relationship between the crystal structures of Earth/planet-forming silicates and oxides at extreme pressures and those of rare-earth sesquisulfides at low pressures.

36 MATERIALS SCIENCE↗