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At least 379 records · Page 21

Heterogeneous fatigue damage in a nickel-based single-crystal superalloy unraveled using correlative 3D X-ray technology

Nickel-based single-crystal (Ni-SX) superalloys under cyclic stress are susceptible to cracking at stress-concentration sites, eventually leading to low-cycle fatigue (LCF) failure. LCF cracks typically originate from intrinsic defects (e.g., voids and carbides) within solidified dendrites. However, systematic quantitative experimental analyses of defect-mediated local damage remain limited. To thoroughly understand the microscopic origins and evolution of LCF damage, correlated 3D mapping of dendrites across various regions is essential. Here, in this study, macroscale micro-computed tomography (μ-CT) was initially used to capture internal interdendritic secondary cracks within bulk DD413 superalloy after LCF testing at 760 °C. Subsequently, a multimodal methodology combining synchrotron 3D microdiffraction (3D-μXRD), high-resolution μ-CT, and electron microscopy was established. This approach allowed precise localization of internal damage zones near interdendritic secondary cracks and detailed mapping of the 3D correlated distributions of dendrites, defects, and residual stress/strain fields within these zones at submicron spatial resolution. Finally, the same approach was applied to specimens subjected to interrupted loading at approximately 40 % of the fatigue life to uncover the early damage states of dendrites. The dendrite cores (DCs) and interdendritic regions (IDs) exhibit microscale heterogeneous mechanical responses: nearly defect-free DCs accumulate local irreversible slip along specific slip systems to generate slip bands, while the IDs containing various defects accommodate local microplasticity through the activation of multiple slip systems around these defects. The local tensile stress near defects in the IDs exceeds that in the DC slip band regions by more than threefold, leading to the generation of local damage zones within the IDs. Chain-like defect distributions facilitate the interconnection of these local zones into a continuous damage region, further elevating the overall tensile stress in the IDs. Additionally, geometrically necessary dislocations alone are insufficient as indicators of LCF damage; both the internal stress state and its magnitude must be considered. These experimental results provide critical data and insights for the development of multi-physics fatigue models.

Localized deformation↗

Coupled beam-target-moderator optimization for the Second Target Station

To support the design of the Second Target Station, that aims to provide the world’s highest peak brightness of cold neutrons, studies that optimize the dimensions of the target and moderators are invaluable. In this work, we investigate the influence of the target dimensions and beam profile on the performance and optimal size of the moderators. We perform optimization runs with detailed MCNP6.2 simulations using high-fidelity unstructured mesh geometries generated from parametrized CAD models. We demonstrate that small changes in target height do not influence the moderator performance if the beam dimensions are chosen adequately. We quantify the effect of beam footprint and target width on the moderator performance and show that the optimal moderator dimensions are insensitive to limited changes in target and beam profile.

Dakota↗

Determination of Site Occupancy in the M–Pd–Zn (M = Cu, Ag, and Au) γ-Brass Phase by CALculation of PHAse Diagrams Modeling and Rietveld Refinement

The Pd–Zn γ-brass phase provides exciting opportunities for synthesizing site-isolated catalysts with precisely controlled Pd active site ensembles. Introducing a third metallic element into the γ-brass lattice further perturbs the catalytic active site ensembles. Here, in this work, we introduce coinage metallic elements M (M = Cu, Ag, and Au) into the Pd–Zn γ-brass phase and investigate the site occupation factors of each element in the γ-brass lattice. The CALculation of PHAse Diagrams (CALPHAD) modeling approach supported by energetics predicted by the density functional theory and X-ray and neutron diffraction with Rietveld refinement were used to identify the SOF on each Wyckoff site for various M amounts alloyed into the Pd–Zn γ-brass phase. The present analysis unveils the strong preference for Pd occupying the outer tetrahedral (OT) site in the γ-brass lattice, while the coinage metallic elements tend to substitute for Zn on the octahedral (OH) site. The determination of site occupancy in the bulk M–Pd–Zn γ-brass phase provides opportunities to investigate and tailor potential catalytically active site ensembles in the γ-brass phase materials.

36 MATERIALS SCIENCE↗

Thermal processing to modulate surface chemistry and bulk charge distribution in nickel-rich layered lithium positive electrodes

The broader application of nickel-rich layered oxides as positive electrode materials for lithium-ion batteries has been hindered by their high manufacturing cost and inferior cycling stability. Thermal processing, which is integral to electrode materials manufacturing and fundamental in materials science, has not been fully utilized to design advanced positive electrode materials. Herein, we demonstrate the capability of using quenching heat treatment to regulate Li distribution and modulate electronic structure near particle surface. The resulting materials exhibit less parasitic reactions with the electrolyte and an improved charge distribution homogeneity in secondary particles, leading to more stable cycling performance at high voltages (4.5 V vs Li/Li + ). Our synchrotron X-ray analyses reveal the underlying interplay between surface structure and bulk charge distribution in positive electrode materials particles. While strategies used to stabilize positive electrode materials through compositional control, surface modification, and electrolyte engineering have become mature, thermal processing can be advantageous to further improve positive electrode materials manufacturing.

36 MATERIALS SCIENCE↗

Atomic faulting drives exceptional toughness in low thermal expansion chromium alloys

Endowing functional properties with mechanical responses in traditional metals has been a frontier topic, akin to transforming base metal into gold. Chromium and its alloys, with their functional deficiencies and limited ductility, serve as typical examples. Herein, we report a Cr96Fe4Ge1.3B1 alloy that unifies low thermal expansion (LTE, αl = 1.79 × 10-6 K-1, 200 − 315 K) with exceptional toughness (240.2 J·cm-3). The enhancement in mechanical responses is primarily attributed to layered Cr2B intermetallic precipitates, which ameliorate interfacial cohesion and simultaneously refine the grain structure. The weakened interlayer interactions within the Cr-B layers facilitate the nucleation and movement of numerous tiny stacking faults in precipitates, efficiently alleviating strain energy and resulting in marked work-hardening ability. Additionally, antiferromagnetic fluctuations in the BCC matrix contribute to the unique LTE behavior. This paves the way for the design of high-performance alloys featuring layered-symmetry precipitates.

Yu, Chengyi [University of Science and Technology ↗

Experimental determination of the temperature-dependent Van Hove function in a Zr 80 Pt 20 liquid

Even though the viscosity is one of the most fundamental properties of liquids, the connection with the atomic structure of the liquid has proven elusive. By combining inelastic neutron scattering with the electrostatic levitation technique, the time-dependent pair-distribution function (i.e., the Van Hove function) has been determined for liquid Zr 80 Pt 20 . We show that the decay time of the first peak of the Van Hove function is directly related to the Maxwell relaxation time of the liquid, which is proportional to the shear viscosity. This result demonstrates that the local dynamics for increasing or decreasing the coordination number of local clusters by one determines the viscosity at high temperature, supporting earlier predictions from molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fluctuation suppression in microgels by polymer electrolytes

Structural details of thermoresponsive, cationically poly(N-iso-propylacrylamide-co-methacrylamido propyl trimethyl ammonium chloride) microgels and the influence of the anionic electrolyte polystyrene sulfonate (PSS) on the internal structure and dynamics of the cationic microgels have been studied with a combination of small angle neutron scattering (SANS) and neutron spin echo (NSE) spectroscopy. While SANS can yield information on the overall size of the particles and on the typical correlation length inside the particles, studying the segmental polymer dynamics with NSE gives access to more internal details, which only appear due to their effect on the polymer motion. The segmental dynamics of the microgels studied in this paper is to a large extent suppressed by the PSS additive. Possible scenarios of the influence of the polyanions on the microgel structure and dynamics are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Pressure-induced phase transition in barium hydride studied with neutron scattering

Barium hydride can undergo a structural phase transition from an orthorhombic phase to a hexagonal phase induced by high temperature or high pressure. This transition causes an immediate increase in the hydrogen diffusion rates by over an order of magnitude, and therefore, understanding the origin and details of such transition is of great interest not only for fundamental reasons but also for improving materials for future applications. In this work, the pressure evolution of the crystal structure was characterized using neutron powder diffraction up to a maximum pressure of 11.3 GPa. The pressure dependence of the unit cell volumes, lattice parameters, atomic sites, and compressibilities were determined for both phases. A structural phase transition occurred over a wide pressure range of P = 1.3 GPa–4.9 GPa. The transition to the higher density hexagonal phase reduced the volume per formula unit of BaD 2 by 13.6%, hence increasing the volumetric storage density. In addition, we investigated the hydrogen diffusion process using high pressure quasi-elastic neutron scattering up to 7.1 GPa. Our results show that the hydrogen mobility increases with pressure in the hexagonal phase. This work sheds light on the structural and dynamical aspects of barium hydride caused by the application of high pressure. Finally, the results may aid in the development of advanced metal hydride systems with increased hydrogen dynamics.

36 MATERIALS SCIENCE↗

A high temperature cell for investigating interfacial structure on the molecular scale in molten salt/alloy systems

In this work, we describe the design and development of an in situ neutron reflectometry cell for high temperature investigations of structural changes occurring at the interface between inorganic salts, in their molten state up to 800 °C, and corrosion resistant alloys or other surfaces. In the cell, a molten salt is confined by an annular ring of single crystal sapphire constrained between the sample substrate and a sapphire plate using two gold O-rings, enclosing a liquid salt volume of 20 ml, along with a dynamic cell volume to accommodate expansion of the liquid with heating. Further, as a test case for the cell, we report on an in situ neutron reflectometry measurement of the interface between a eutectic salt mixture of MgCl 2 –KCl (32:68 molar ratio) and a single crystal sapphire substrate at 450 °C, resulting in the formation of a 60 Å layer having a scattering length density of 1.72 × 10 –6 Å –2 . While the origin of this layer is uncertain, it is likely to have resulted from the salt reacting with an existing impurity layer on the sapphire substrate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge for parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator–ferromagnetic insulator heterostructure Bi2Se3/EuS exhibiting proximity magnetism in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator–antiferromagnet heterostructure (Bi,Sb)2Te3/Cr2O3 and identify possible interfacial proximity magnetism in this material. We anticipate that the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

MENUS—Materials engineering by neutron scattering

Materials engineering by neutron scattering (MENUS) at the second target station will be a transformational high-flux, versatile, multiscale materials engineering diffraction beamline with unprecedented new capabilities for the study of complex materials and structures. It will support both fundamental and applied materials research in a broad range of fields. MENUS will combine unprecedented long-wavelength neutron flux and unique detector coverage to enable real-time studies of complex structural and functional materials under external stimuli. The incorporated small angle neutron scattering and transmission/imaging capabilities will extend its sensitivity to larger length scales and higher spatial resolution. Multimodal MENUS will provide crystallographic and microstructure data to the materials science and engineering community to understand lattice strain/phase transition/microstructure/texture evolution in three orthogonal directions in complex material systems under combined extreme applied conditions. Finally, the capabilities of MENUS will open new scientific opportunities and meet the research needs for science challenges to enable studies of a range of phenomena and answer the key questions in material design/exploration, advanced material processing, transformative manufacturing, and material operations of national impacts in our daily life.

47 OTHER INSTRUMENTATION↗

VERDI: VERsatile DIffractometer with wide-angle polarization analysis for magnetic structure studies in powders and single crystals

The VERsatile DIffractometer will set a new standard for a world-class magnetic diffractometer with versatility for both powder and single crystal samples and capability for wide-angle polarization analysis. The instrument will utilize a large single-frame bandwidth and will offer high-resolution at low momentum transfers and excellent signal-to-noise ratio. A horizontal elliptical mirror concept with interchangeable guide pieces will provide high flexibility in beam divergence to allow for a high-resolution powder mode, a high-intensity single crystal mode, and a polarized beam option. A major science focus will be quantum materials that exhibit emergent properties arising from collective effects in condensed matter. The unique use of polarized neutrons to isolate the magnetic signature will provide optimal experimental input to state-of-the-art modeling approaches to access detailed insight into local magnetic ordering.

36 MATERIALS SCIENCE↗

Alvin William Trivelpiece

An outstanding leader, physicist, and administrator, Alvin William Trivelpiece died on 7 August 2022 in Rancho Santa Margarita, California.

99 GENERAL AND MISCELLANEOUS↗

Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography *

Abstract Neutron computed tomography (NCT), a 3D non-destructive characterization technique, is carried out at nuclear reactor or spallation neutron source-based user facilities. Because neutrons are not severely attenuated by heavy elements and are sensitive to light elements like hydrogen, neutron radiography and computed tomography offer a complementary contrast to x-ray CT conducted at a synchrotron user facility. However, compared to synchrotron x-ray CT, the acquisition time for an NCT scan can be orders of magnitude higher due to lower source flux, low detector efficiency and the need to collect a large number of projection images for a high-quality reconstruction when using conventional algorithms. As a result of the long scan times for NCT, the number and type of experiments that can be conducted at a user facility is severely restricted. Recently, several deep convolutional neural network (DCNN) based algorithms have been introduced in the context of accelerating CT scans that can enable high quality reconstructions from sparse-view data. In this paper, we introduce DCNN algorithms to obtain high-quality reconstructions from sparse-view and low signal-to-noise ratio NCT data-sets thereby enabling accelerated scans. Our method is based on the supervised learning strategy of training a DCNN to map a low-quality reconstruction from sparse-view data to a higher quality reconstruction. Specifically, we evaluate the performance of two popular DCNN architectures—one based on using patches for training and the other on using the full images for training. We observe that both the DCNN architectures offer improvements in performance over classical multi-layer perceptron as well as conventional CT reconstruction algorithms. Our results illustrate that the DCNN can be a powerful tool to obtain high-quality NCT reconstructions from sparse-view data thereby enabling accelerated NCT scans for increasing user-facility throughput or enabling high-resolution time-resolved NCT scans.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Outlook towards deployable continual learning for particle accelerators

Particle accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, machine learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.

43 PARTICLE ACCELERATORS↗

Spin Correlations in the Parent Phase of Li 1–x Fe x ⁢ ODFeSe

Elucidating spin correlations in the parent compounds of high-temperature superconductors is crucial for understanding superconductivity. We used neutron scattering to study spin correlations in Li 1–x Fe x ⁢ ODFeSe, an insulating material with reduced electron carriers compared to its superconducting counterpart (T c = 41 K), serving as the undoped parent compound. Our findings show a reduced total fluctuating moment in this insulator relative to FeSe and 122 iron pnictides, likely due to increased interlayer distances from intercalation, which enhance fluctuations and reduce the intensity of spin excitations. Moreover, we observed a V-shaped spin wavelike excitation dispersion, contrasting with the twisted hourglass pattern in the superconducting counterpart. Electron doping shifts spin excitation from (π,0) point to an incommensurate position towards (π,π) direction below 65 meV. In conclusion, this transition from V-shaped to hourglasslike dispersion, akin to behaviors in hole-doped cuprates, suggests a potential shared mechanism in magnetism and superconductivity across these diverse systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

gallimgg/Topic_Modeling_Assistant

Topic Modeling Assistant for large literature databases in Scopus system with zettelkasten note exporting tool for use in Obsidian

Gallimore, GrantGregory [Oak Ridge National Labora↗