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At least 1,675 records · Page 93

Graph-based design of irregular metamaterials

In the field of metamaterial research, random structures offer a novel and less conventional approach compared to traditional periodic designs. Designing random metamaterials is challenging when it comes to ensuring intercon- nectivity, which is essential for manufacturability. This study introduces an innovative framework for generating random metamaterials using graph al- gorithms, ensuring connectivity and adaptability across various base shapes, including cylinders, triangles, pyramids, and cubes. By employing graph algorithms, our framework enhances the intuitiveness and efficiency of de- sign representation and manipulation, streamlining the design process. The framework generates families of designs that exhibit a wide range of prop- erty magnitudes that can be adjusted intuitively by modifying the input parameters. The rapid design process allows many designs to be generated, offering the user a multitude of solutions around the target property range. The designs can be effectively implemented in various fields and subjected to diverse analytical studies, including static, dynamic, and eigenfrequency assessments. We illustrate computational results for two key properties (stiff- ness and acoustic impedance), showcasing the method’s effectiveness through examples ranging from rod-based to cube-based designs. Here, the framework not only advances metamaterial research but also creates new opportunities for innovation in fields requiring customized material properties.

36 MATERIALS SCIENCE↗

Learning interpretable surface elasticity properties from bulk properties via neural network equation learners

Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness. However their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face-centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure–property relationships for materials science phenomena such as surface elasticity.

Equation learning↗

Equilibrium vapor pressure properties for absorbent and adsorbent materials

Sorption devices are important tools for the efficient utilization of fuels and waste heat. Amid a tremendous diversity of cycles and applications, all sorption systems have an equilibrium vapor pressure that depends on the sorbent temperature and composition. The vapor pressure properties of working fluids are reported in the literature in a variety of ways, which impedes wide-ranging cross comparisons or screening studies for novel applications. This work compiles equilibrium vapor pressure properties for 123 liquid absorbents with 31 absorbates and 139 solid adsorbents with 16 adsorbates. The adsorption pairs are represented with six functional forms. Additionally, most of the absorption pairs are represented with 10 functional forms, plus several that are represented with custom empirical equations. Because the functional forms used in the literature vary widely, in this work each functional form was generalized. This paper is designed to facilitate comparisons of working fluid properties for screening studies, provide a quick reference to existing research, and present a framework for standardizing the reportage of vapor pressure equilibrium data for existing and future working pairs.

36 MATERIALS SCIENCE↗

Tailoring Cu-Zr gradient nanoglass structures: Influence of nanoparticle size and cooling rates on glass-glass interfaces

The study of gradient nanoglasses (GNGs) has gained attention due to their unique mechanical properties and potential applications in advanced materials. This study employs molecular dynamics simulations to synthesize a GNG using Cu-Zr metallic glass nanoparticles (NPs) sized from 3 to 15 nm. The NPs were produced by melting and quenching metallic clusters at a relatively slow quench rate of 10 9 K/s. The synthesis of GNG is elucidated along with the characterization of its heterogeneous metallic glass nanostructure. A seamless GNG structure is formed through cold compression of Cu 64 Zr 36 amorphous NPs of varying sizes. The influence of NP size on the GNG structure is investigated, utilizing deeply relaxed NPs, which exhibit a characteristic Cu segregation pattern on their surfaces. The results highlight an increase in structural heterogeneity due to heterogeneous mass transport and the development of local composition and density variations caused by Cu segregation at glass-glass interfaces (GGIs). A reduction in NP size is correlated with decreased Cu atomic displacements and local density at GGIs, suggesting that larger NPs may produce stronger GGIs. This research presents a novel methodology for synthesizing heterogeneous metallic glasses, demonstrating the capacity to control and customize nanostructure heterogeneity through the manipulation of NP sizes and cooling rates. Furthermore, these findings enhance our understanding of structural evolution during nanoglass synthesis and lay the foundation for further exploration in nanomaterial synthesis and characterization.

36 MATERIALS SCIENCE↗

Fuel reid vapor pressure level and ethanol content on stochastic preignition, effects at steady and unsteady engine operation

The present work investigates relations between fuel Reid vapor pressure (RVP) and biofuel (ethanol) content on stochastic preignition (SPI) at both sustained steady-state engine operation and following load transients. This work stems from in-field observations that automotive original equipment manufacturers have observed consistent seasonal increases in United States customer drivability complaints and warranty claims during September and October where SPI is suspected to be responsible. The seasonal timing of these events coincides with the United States seasonal fuel property changeover initiating on September 15 each year, where fuel RVP increases. To explore potential linkage between fuel RVP and SPI the present study employs engine SPI experiments coupled with laboratory spray measurements of fuels with RVPs of 8, 12, and 16 psi in both E10 (10% ethanol) and E25 (25% ethanol) fuels. Engine results are partitioned into fuel RVP and ethanol content effects on SPI in steady-state, sustained high-load engine operation and unsteady-state low- to high-load transitions, where off-engine spray vessel patternation and tip penetration results help to elucidate the observed fuel effects on SPI. A boosted direct-injected, spark-ignition engine was fueled with three market relevant E10 and E25 fuels with RVPs of 8, 12, and 16 to characterize the interplay between winter fuels and abnormal combustion behavior. The steady-state work shows that for high-load, steady-state engine operation, SPI is directly linked to fuel retention, which was found to be dependent on fuel distillation. The unsteady-state engine operation work shows that following low-to high-load transitions, SPI can occur from a memory of fuel property effects at low-load operation. Specifically, the fuel RVP effect on fuel spray collapse at low loads was found to correlate with SPI with a more than 95% confidence interval following low- to high-engine-load transitions. Results suggest that fuel-wall impingement at low-load operation could carry over into high-load transitions and generate SPI events following low- to high-load transitions.

09 BIOMASS FUELS↗

Hygroscopic growth of UO 2 F 2 nanoparticles

Hygroscopicity is an important physicochemical property of aerosol that describes the ability of a particle to uptake water. The hygroscopic properties of uranyl fluoride (UO 2 F 2 ) aerosol generated from a UF 6 hydrolysis reactor was investigated for the first time using a custom-built Humidified Tandem Differential Mobility Analyzer (HTDMA). The HTDMA is capable of measuring UO 2 F 2 nanoparticle growth determined by mobility size over a wide range of atmospheric humidity from dry conditions at <10% relative humidity (RH) to 85% RH. The hygroscopic properties were determined for nanoparticles as small as 3.5 nm in this study. Although the largest size of UO 2 F 2 nanoparticles was 80 nm, monodisperse aerosol with a mobility diameter of up to approximately 500 nm can be investigated using the HTDMA. Anhydrous UO 2 F 2 nanoparticles with a mobility diameter of 3.5 nm were shown to be highly hygroscopic with a deliquescence relative humidity (DRH) of 10%. Hydrates with a larger mobility diameter from 10 to 80 nm were non-hygroscopic with no observable DRH and limited water uptake up to 85% RH. Here, these results demonstrate the hygroscopic properties of UO 2 F 2 nanoparticles are highly variable and based on both the mobility size and hydration state. Hygroscopicity affects the physicochemical properties of UO 2 F 2 nanoparticles, including the aerosol phase state and viscosity, with impacts on aerosol growth, coagulation, and deposition that is critical for understanding the fate and transport of UO 2 F 2 nanoparticles in the atmosphere.

Hygroscopicity↗

Cryo-quenched Fe–Ni–Cr alloy decorative steel single crystals II: Alloy phases, structure, hardness, tensile, tribological, magnetic and electronic properties

We have previously reported (Boatner et al., J. Alloys Compd. 691 (2017) 666–671) on the discovery, formation, processing, and application of a new decorative ternary steel alloy and on its use in a wide range of practical applications including: custom and commercial knives, art objects, jewelry, electronics, and furniture. This decorative property results when polished single crystals of the 70 wt%Fe-15 wt%Ni-15 wt% Cr austenitic alloy are cryo-quenched into the martensitic phase resulting in the formation of a three-dimensional raised pattern of mixed austenitic/martensitic laths. These laths propagate across large dimensions of the material due to the absence of grain boundaries in the single crystal. The macroscopic optically reflective 3-D decorative pattern reproduces the structural symmetry inherent in the single-crystal orientation. This pattern results solely from the metallurgical phase properties of the ternary alloy and is completely distinct from the properties of Damascus steels or more modern so-called pattern-welded steels. Here we use X-ray diffraction, nanoindentation, hardness measurements/scratch testing, tribological measurements, and alloy resistivity and magnetization methods to obtain a more fundamental and comprehensive study of the effects of the austenitic/martensitic phase transition on the structural and physical properties of the 70 wt%Fe-15 wt%Ni-15 wt% Cr alloy. These results reveal new insight into irreversible phenomena that are associated with the inhibited phase transition on heating of the two-phase, mixed austenitic/martensitic structure that is formed subsequent to cryo-quenching the alloy crystal to 77 K.

36 MATERIALS SCIENCE↗

Developing techniques for crystal growth synthesis and characterization of rare earth-silicon-germanium magnetoresponsive alloys

In this study, a crystal growth technique was developed for the family of intermetallic, magnetoresponsive RE 5 (Si 1-x Ge x ) 4 alloys in order to access the intrinsic and anisotropic properties. Initial bulk properties measured on polycrystalline samples alluded to the properties being obscured or averaged and high-quality, well-characterized, single crystals would clarify this. Successful crystal growth led to development of oriented sample preparation techniques specific for the crystal structures (monoclinic and orthorhombic) and mechanical properties (hard and brittle) of these alloys, and customized for the various measurement techniques undertaken. Here we describe our journey, sometimes circuitous, from crystal growth to sample preparation and cite the scientific discoveries that came about as a result.

36 MATERIALS SCIENCE↗

Dynamic calibration of differential equations using machine learning, with application to turbulence models

We present a methodology for calibration of parametric ordinary and partial differential equation models, using off-the-shelf software for back-propagation in Neural Networks (NN). As a prototypical example, we consider calibration of a Reynolds-averaged Navier-Stokes (RANS) turbulence closure model, against ground truth data from direct numerical simulations (DNS) of two different turbulent flows. Numerical time integration is represented as a custom NN, where only the RANS model parameters are trainable. A loss function is defined to quantify the mismatch between the NN prediction and the ground truth over a predefined, finite time integration window. This loss function is then minimized using a gradient descent method utilizing the back-propagation algorithm. Furthermore, this dynamic approach to training is to be contrasted with a static approach, wherein a least square regression estimate for parameters is obtained in the limit of an infinitesimal time integration window. In a first test of static and dynamic approaches against ground truth data generated by the model, the former proves to be significantly faster and more accurate than the latter at recovering the parameters. When both calibration approaches are tested against DNS data, for which it is known that the model cannot achieve a perfect fit, the static approach yields a good prediction only for short times, while the dynamic approach results in physical and stable predictions over the entire integration window. After optimization of the dynamic approach for time step, spatial resolution, stability, and physics-based constraints, we obtain a 50% improvement of outcomes over those obtained from the existing, manually calibrated set of parameters, demonstrating the merits of this systematic and automated procedure.

97 MATHEMATICS AND COMPUTING↗

Synergistic cellulase–xylanase formulations for enhanced dewatering and fiber bonding toward energy-efficient and sustainable paper and packaging production

A mechanistic understanding of the synergistic effects of enzymes on cellulosic fibers dewatering and fiber bonding is essential for advancing energy-efficiency and lightweight production of paper and packaging materials. This study investigates the impact of varying cellulase and xylanase formulations on equilibrium moisture content (EMC) after pressing and tensile strength of cellulosic fiber webs using a factorial experimental design. Nine custom enzyme formulations were evaluated at controlled dosages ranging from ∼20 to 76 FPU/mL cellulase and ∼500–1130 IU/mL xylanase. Response surface modeling revealed a significant synergy, particularly at 20–40 FPU/mL cellulase combined with ≥ 1000 IU/mL xylanase. Under these conditions, EMC decreased by up to 3.6% compared with the untreated refined control, while tensile index gains of up to 16% were statistically significant for optimized blends (p < 0.05). The interaction between cellulase and xylanase was also significant for the tensile response (p = 0.010). Protein efficiency analysis showed that optimized formulations containing 30–40% less protein outperformed the commercial benchmark. The nonlinear synergy between cellulase and xylanase is attributed to their complementary substrate specificities. Endoglucanase- and β‑glucosidase‑rich cellulases hydrolyze internal β‑1,4‑glycosidic bonds in amorphous cellulose, loosening fiber walls and increasing flexibility, while xylanases target hemicellulose, primarily xylan-rich domains, enhancing porosity and improve cellulase accessibility. Tailoring enzyme formulations at low loadings overcomes traditional trade-offs between strength and dewatering, enabling cost-effective, energy-efficient, low-carbon solutions for sustainable packaging and hygiene products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Background Concentrations of Argon-39 in Shallow Soil Gas

While radioisotopes of noble gases are known to be indicators of underground nuclear explosions (UNE), McIntyre et al. (2017) was the first to report the presence of 39Ar in shallow soil gas in association with a decades old UNE. While this finding hinted at the potential application of 39Ar to be used as an indicator of an UNE, doing so would also require an understanding of the natural concentrations of 39Ar present in soil gas. Without knowing the expected range and variability of naturally occurring concentrations of 39Ar, it is difficult to determine what measured concentrations would be indicative of an elevated concentration. This paper presents results from 16 soil gas samples and three atmospheric air samples collected from various locations across the western United States. Shallow soil gas samples were collected into self-contained underwater breathing apparatus (SCUBA) tanks using a custom-built soil gas sampling system and then processed and analyzed for 39Ar. The measured concentrations of 39Ar varied from atmospheric air concentrations to about 3.5 times atmospheric air concentrations. The variation in concentration was primarily attributed to the latitude the sample was collected at, which was consistent with previous work (Johnson et al. 2015). The results presented here represent the first measurements of natural background 39Ar concentrations in shallow soil gas. This data will be necessary if 39Ar is to be used as an indicator of UNE.

Fritz, Brad G.↗

Development and testing of a continuous maritime monitor for radionuclide aerosols

Monitoring airborne concentrations of radionuclide activity may provide a timely warning to sea-based assets to avoid contamination from a radioactive plume. The development and testing of an automated aerosol monitoring system that can capture and detect radioactive particulate from marine air is presented. A custom electrostatic precipitator (ESP) was designed to capture particulate onto a reusable collection media. The collection efficiency of the ESP system for radon progeny was determined to be ~23%. A conservative calculation of the minimum detectable concentration of 214 Bi was estimated as 0.3-8 Bq/m 3 . The system was demonstrated in continuous operation, without consumables and limited maintenance, in a marine environment at the PNNL campus in Sequim, Washington. In conclusion, a successful 2-month deployment indicates the feasibility of the approach for continuous maritime monitoring for radionuclide aerosols.

Moore, Michael E. [Pacific Northwest National Labo↗

Behavioral simulations and experimental evaluations of stress induced spatial nonuniformity of dynamic bulk modulus in additive friction stir deposited AA 6061

Additive friction stir deposition (AFSD) is a rapidly developing solid-state additive manufacturing (AM) technology that enables the fabrication of customized products on a relatively large scale. The unique forge-liked process offers high quality products with refined equiaxed microstructure and exceptional mechanical performance. However, in AFSD, the spatially distributed mechanical properties within a deposited layer have not been established so far, due to the lack of a suitable large-scale mapping method. In the present work, an ultrasonic dynamic-elasticity mapping method was employed to map the distribution of physical properties in a AFSD 6061 Al. Strong spatial asymmetries in elastographies were mainly due to the residual stresses. The origin of the asymmetries was investigated through thermo-mechanical simulations of the AFSD process. It was observed that majority of the asymmetries were associated with the mechanical residual stresses instead of the symmetric thermal residual stresses during linear deposition of the AFSD process. However, the thermal residual stresses can be manipulated to become asymmetric with a 90° turn in the linear deposition path, due to the difference between the levels of cooling along the edges of the inner and outer corners. Hence, the mechanical and thermo-mechanical residual stresses can be additive or subtractive depending on the direction of the turns. Here, such numerically predicted behaviors were also experimentally observed on the AFSD fabricated samples by ultrasonic elastography. Furthermore, additional preliminary studies on multi-layer AFSD fabrications show the asymmetric mechanical stresses generated during the deposition process on different layers generate constructive or destructive interference which results to spatially variable dynamic bulk modulus depending on the deposition orientations of layers.

36 MATERIALS SCIENCE↗

An innovative radial gradient material design using hot isostatic pressing for applications in extreme environments

Functionally graded materials (FGMs) are highly advanced continuous or discontinuous structures whose structural and material properties vary along a singular geometric dimension either in the axial or radial direction. Here, the radial gradient FGM design makes for an optimal structural design to incorporate a bi-metallic structure with a copper-based high entropy alloy (Cu-HEA) with good mechanical properties and high irradiation resistance, and Chromium (Cr) with great corrosion resistance. This study focuses on the experimental design of a metal powder loading mechanism to fabricate a bi-metallic radial gradient structure using Cu-HEA and 99.9 % pure Cr metal powders. The powder loading strategy uses custom-designed concentric cylindrical dividers to separate the individual compositions. Two benchtop trial runs were performed for design optimization. The optimized design was then implemented to eventually load the HEA and Cr powders for consolidation via powder metallurgy hot isostatic pressing (PM-HIP). The electron microscopy analysis reveals the successful fabrication of the radial gradient structure with the chemical mapping analysis, demonstrating the gradual composition shift from the HEA at the center to the pure-Cr at the periphery via a three-step gradient.

High Entropy Alloys (HEAs)↗

Optimized manufacturing process for multilayer two-dimensional focusing mirrors in laboratory X-ray applications

Recent advances in laboratory X-ray applications require high-performance optical components that achieve exceptional imaging resolution and beam uniformity within compact experimental setups. Montel mirrors have become a preferred solution due to their unique dual-reflection focusing mechanism and a space-efficient design. Here, in this study, we present an effective manufacturing process for producing Montel mirrors tailored to focus laboratory X-ray beams. The mirrors were fabricated from single-crystal silicon substrates, chosen for their high mechanical stability and compatibility with precision polishing techniques. Our approach begins with the integration of a deterministic chemo-mechanical polishing (CMP)-based pre-shaping step followed by ion beam figuring (IBF), significantly improving manufacturing efficiency. Subsequently, our custom-developed advanced metrology and IBF techniques were employed for fabricating an off-axis, elliptical cylinder Montel mirror system with a 6-mrad total slope, with stringent optical specifications. While post-IBF processes, including multilayer coating, dicing, and gluing, introduced minor surface errors, yet their impact on performance remained negligible. The Montel mirrors manufactured with the optimized process exhibited significantly improved beam uniformity and a reduced focal spot size. These findings validate our approach as a viable solution for high-precision Montel mirror fabrication and facilitate further advancements in laboratory X-ray applications.

36 MATERIALS SCIENCE↗

X-ray observation of individual Ti-6Al-4V spherical powder particle impact in an in-situ operando laser directed energy deposition system

Understanding the particle capture mechanism in the laser directed energy deposition additive manufacturing process provides a foundation for improving productivity and reducing material loss. Capturing this phenomenon is challenging due to its highly transient time interval. To analyze the impact behavior of spherical Ti-6Al-4V powder, a custom laser directed energy deposition system was designed to control the deposition of individual particles. Synchrotron X-ray imaging at 24 kHz monitored the impact dynamics of individual powder particles. A multi-physics model based on the Smoothed-Particle Hydrodynamics scheme supported the analysis by providing the temperature and velocity fields of the molten pool. Results revealed that in laser directed energy deposition, surface tension forces dominated the powder–molten pool interaction. Hydrophobic Ti-6Al-4V powder particles had an equilibrium contact angle of 121 degrees with their molten state. Furthermore, Ti-6Al-4V powder particles required a high impact velocity (6 to 12 m/s) to transition from oscillation to submergence behavior. Lastly, oscillating powder particles that impacted near the location of the laser beam exhibited faster melting, highlighting the contribution of the laser beam to the melting mechanism. This work lays the foundation for investigations of powder particle impact in laser directed deposition and supports the validation of numerical models of powder–molten pool interactions.

36 MATERIALS SCIENCE↗

Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression

Here, an interpretable machine learning method, physics-informed genetic programming-based symbolic regression (P-GPSR), is integrated into a continuum thermodynamic approach to developing constitutive models. The proposed strategy for combining a thermodynamic analysis with P-GPSR is demonstrated by generating a yield function for an idealized material with voids, i.e., the Gurson yield function. First, a thermodynamic-based analysis is used to derive model requirements that are exploited in a custom P-GPSR implementation as fitness criteria or are strongly enforced in the solution. The P-GPSR implementation improved accuracy, generalizability, and training time compared to the same GPSR code without physics-informed fitness criteria. The yield function generated through the P-GPSR framework is in the form of a composite function that describes a class of materials and is characteristically more interpretable than GPSR-derived equations. The physical significance of the input functions learned by P-GPSR within the composite function is acquired from the thermodynamic analysis. Fundamental explanations of why the implemented P-GPSR capabilities improve results over a conventional GPSR algorithm are provided.

42 ENGINEERING↗