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At least 523 records · Page 29

Effects of elevated temperature exposure on the residual stress state and microstructure of PVD Cr coatings on SiC investigated via in situ X-ray diffraction and transmission electron microscopy

Pure Cr coatings are being considered as corrosion-resistant layers for SiC-SiC f composite fuel cladding in light water reactors. The residual stress state and interdiffusion behavior of Cr coatings deposited on SiC substrates using bipolar high-power impulse magnetron sputtering (B-HiPIMS) and direct current impulse magnetron sputtering (DCMS) were studied using in situ X-ray diffraction experiments at 300 °C, 700 °C, and 1200 °C. After elevated temperature exposure, evolution of the coating microstructure and coating/substrate interdiffusion was characterized by transmission electron microscopy. The compressive residual stress of the dense B-HiPIMS coating at room temperature was gradually relieved at 300 °C, while the stress transitioned to a tensile state at 700 °C. Grain growth of the Cr coating and a Cr–SiC interdiffusion layer consisting of newly formed Cr–C and Cr–Si phases were observed in the B-HiPIMS coating after exposure at 700 °C. The phases in the interdiffusion layer were identified to be Cr 7 C 3 , Cr 3 Si, and Cr 5 Si 3 C x experimentally and by thermodynamic predictions. For the DCMS coating, however, changes in residual stress and microstructure at the elevated temperatures were limited by the inherent porosity and inhomogeneous structure of the as-deposited coating. At 1200 °C, both coating types interacted rapidly with the underlying SiC substrate in the early stages of the experiment. The results from this study suggest that the characteristics of the Cr coatings do not significantly change at the reactor operating temperature of 300 °C, while changes in microstructure and stress state are expected in off-normal conditions of 700 °C and 1200 °C. Finally the results from this study suggest the B-HiPIMS coating is best-suited as a protective coating for SiC fuel cladding.

36 MATERIALS SCIENCE↗

Microstructure and mechanical behavior of a TiC nanoprecipitate strengthened V Alloy

The V-4Cr-4Ti (V44) alloys have been proposed as the prime candidate structural material for self-cooled liquid Li blanket and other designs for fusion energy applications. However, the applications of the V44 alloy are limited to a narrow operation temperature window, due to reduction in creep strength at or above 700 °C and susceptibility to irradiation hardening and embrittlement when irradiated below 400 °C. Here, in this work, we explore the feasibility of designing a novel V alloy to form a high number density of TiC nanoprecipitates, in order to simultaneously improve creep strength and provide defect sinks to mitigate irradiation hardening. Computational thermodynamics was used to design a new alloy (V44C) to achieve our goal of high TiC nanoprecipitates density within the alloy V44 matrix. To ensure scalability, the new alloy was made through arc-melting and ingot-casting followed by hot forging, cold rolling and heat treatments of homogenization and precipitation aging. The microstructure was characterized by SEM, TEM, XRD and APT, confirming the existence of nanoprecipitates predicted in the thermodynamic calculations. In addition to microstructural evaluation tensile properties at room temperature and 700 °C, and Charpy impact energy at room temperature were measured. The microstructure and mechanical properties were then compared with those from a historic reference V44 alloy. The tensile strength improvement in V44C was rationalized based on particle and solid solution strengthening mechanism. The fracture behavior was discussed based on the fractography results and necking deformation behavior.

Alloys development↗

An additively manufactured AlCuMnZr alloy microstructure and tensile mechanical properties

In this study, selective laser melting (SLM) was used to fabricate an AlCuMnZr alloy. The microstructural features that resulted from additive manufacturing (AM) were significantly refined compared to the corresponding cast alloy features. A combination of fine equiaxed and columnar grains along with in-situ formation of θ' precipitates during AM leads to enhanced yield strength (up to 300°C) in the as-fabricated AM alloy. The refinement of brittle intermetallics and a bimodal grain size distribution leads to improved tensile elongation in the AM alloy. The results illustrate the microstructural advantages that can result from additive processing over conventionally processed microstructures.

36 MATERIALS SCIENCE↗

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps and constitutive models simultaneously is currently lacking. Further, this paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

36 MATERIALS SCIENCE↗

Variations in GARS powder microstructure as a function of powder chemistry and particle size

The properties of metal produced through powder metallurgy depends on the feedstock used. Powders produced via gas atomization reaction synthesis (GARS) are used to produce oxide dispersion strengthened alloys. The desired powder size range can vary for each consolidation technique. However, powder microstructure also can vary with powder particle size, which in turn can impact the microstructure and properties of the consolidated parts. In this study, GARS powders are characterized via inductively coupled plasma mass spectroscopy, inert gas fusion, and high-resolution x-ray diffraction to determine variations in elemental and phase compositions. Transmission electron microscopy was used to understand microstructure variations as a function of chemistry and size. Across the three batches tested intermetallic content was 0.73–1.35 wt% in the 0-20 μm powder batch and increased to 2.46–3.80 wt% in the coarse 45-106 μm batch. Across all batches, volume percent of surface oxidation decreased with powder diameter, with volume percents within the range of 0.75–1.2 % across 10 μm powder particles, and below 0.4 % across coarse powder particles approximately 100 μm in diameter. These observations were supported by inert gas fusion measurements. However, the oxide layer was thicker in coarse powder particles due to a slower cooling rate. Increasing oxygen content in atomization gas to 2000 ppm and adding yttrium increased both the surface oxidation content and yttrium intermetallic content. Lastly, intermetallic phases within the powder coarsened with powder size. Intermetallic morphology changed from fine spherical intermetallic and columnar dendritic growth to a cellular structure with finer spherical intermetallic, to coarse irregular intermetallic and intermetallic along grain boundaries as a result of slower cooling rate and solidification rate in coarse powder particles. Furthermore, the addition of zirconium does not appear to significantly change intermetallic morphology, but the composition changed from a Y-Fe rich intermetallic to a Y-Zr-Fe intermetallic.

42 ENGINEERING↗

Enhancing cold spray coatings: Microstructural dynamics and performance attributes of Inconel 625 with chromium carbide incorporation for hydropower applications

The incorporation of chromium carbide (CrC) particles into the cold spray (CS) process is known to mitigate nozzle clogging, although at the expense of deposition efficiency. This study explores the intricate microstructural changes induced by varying amounts of CrC powder (12.5 % and 6 %) in conjunction with Inconel-625 (Inc-625) powder. The deposition was carried out onto A27 cast steel under different CS parameters. Microstructural characterization, including detailed electron microscopy studies, reveals a complex yet structurally stable coating. Noteworthy features include grain fragmentation and a cellular structure enriched with Nb and Mo, with minimal plastic deformation of CrC in the matrix. The cold-sprayed coatings exhibit a significant (~4 times) increase in microhardness compared to the A27 substrate. Mechanical and cavitation erosion properties were systematically investigated. Coatings subjected to higher particle energy conditions with a gas pressure of 600 psi and gas temperature of 650 °C, demonstrated superior resistance to cavitation erosion. This resistance is attributed to a combination of factors, including microstructural characteristics and porosity. Altogether, the study provides valuable insights into the structural dynamics and performance of CS coatings enriched with CrC particles.

A27 cast steel↗

Utilizing a Dynamic Segmentation Convolutional Neural Network for Microstructure Analysis of Additively Manufactured Superalloy 718

Additive manufacturing (AM) is revolutionizing almost all industries through the production of intricate geometries previously prohibited by cost or machinability. Ni-based superalloys form a primary alloy class for high temperature applications in the petrochemical, aerospace, and nuclear industries because of their intrinsic resistance to creep and the ability to heat treat the superalloy for high strength. Despite these attractive properties, the extreme work hardening of Ni-based superalloys makes traditional manufacturing of complex shapes difficult and thus, these alloys are an attractive target for AM. Superalloy 718 was chosen as an example superalloy because of the wide variety of precipitates that can form within its composition space from the repetitive heating and cooling cycles of the AM process. The precipitates and other microstructure features, such as grain boundaries and dislocations, will dictate the mechanical properties and thus, there is an extensive challenge to characterize the size, number density, composition, and volume fraction of each microstructural feature from AM fabrication using analytical electron microscopy. This work focused on the application of a pixel-wise classification machine learning (ML) model called a dynamic segmentation convolutional neural network (DSCNN) to identify the microstructural features of an as-fabricated additively manufactured superalloy 718.

Taller, Stephen↗

Enhancement of Conjugated Polymer Microstructure and Mixed-Conducting Properties via Chalcogenophene Heteroatom Substitution

Heteroatom substitution is a powerful tool to tune the intra- and intermolecular structure of conjugated polymers as well as their resulting optoelectronic and electrochemical properties. Here, a series of oligoethylene glycol bithiophene chalcogenophene polymers (p(g3T2-X)) with systematically varied furan, thiophene, selenophene, and tellurophene comonomers have been synthesized for mixed ionic-electronic conducting applications. Their microstructures have been thoroughly characterized ex situ and in situ with X-ray scattering, and their mixed conducting properties have been probed in electrochemical transistor testbeds. Chalcogenophene heteroatom choice was found to clearly dictate the polymer microstructure (crystallite dimensionality and orientation) and tune mixed conducting properties. Proceeding down Group 16, from O to Se systematically directed the molecular ordering of 2D polymer crystallites from face-on (O) to mixed (S) to edge-on (Se) orientations, with Te driving the polymer to form well-oriented edge-on 3D crystallites. Heteroatom dictated crystallite quality, and orientation tuned relative ionic transport by 2 orders of magnitude. Hole mobility (μ hole ) and mixed conducting figure of merit (μC*) were each tuned over an order of magnitude depending on heteroatom choice, with the Te-containing polymer reaching μ hole = 3.60 cm 2 V –1 s –1 and μC* = 483 F cm –1 V –1 s –1 , due to improved molecular ordering. Insights from this polymer series highlight target microstructures for enhanced mixed conduction in future conjugated polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Exploring the Relationship of Microstructure and Conductivity in Metal Halide Perovskites via Active Learning-Driven Automated Scanning Probe Microscopy

Electronic transport and hysteresis in metal halide perovskites (MHPs) are key to the applications in photovoltaics, light emitting devices, and light and chemical sensors. These phenomena are strongly affected by the materials microstructure including grain boundaries, ferroic domain walls, and secondary phase inclusions. Here, we demonstrate an active machine learning framework for “driving” an automated scanning probe microscope (SPM) to discover the microstructures responsible for specific aspects of transport behavior in MHPs. In our setup, the microscope can discover the microstructural elements that maximize the onset of conduction, hysteresis, or any other characteristic that can be derived from a set of current–voltage spectra. Furthermore, this approach opens new opportunities for exploring the origins of materials functionality in complex materials by SPM and can be integrated with other characterization techniques either before (prior knowledge) or after (identification of locations of interest for detail studies) functional probing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effect of Dispersion Medium Composition and Ionomer Concentration on the Microstructure and Rheology of Fe–N–C Platinum Group Metal-free Catalyst Inks for Polymer Electrolyte Membrane Fuel Cells

In this paper, we present an investigation of the microstructure and rheological behavior of catalyst inks consisting of Fe–N–C platinum group metal-free catalysts and a perfluorosulfonic acid ionomer in a dispersion medium (DM) of water and 1-propanol ( n PA). The effects of the ionomer-to-catalyst (I/C) ratio and weight percentage of water (H 2 O %) in the DM on the ink microstructure were studied. Steady-shear and dynamic-oscillatory-shear rheology, in combination with synchrotron X-ray scattering, was utilized to understand interparticle interactions and the level of agglomeration of the inks. In the absence of the ionomer, the inks were significantly agglomerated, approaching a gel-like microstructure for catalyst concentrations as low as 2 wt %. The effect of H 2 O % in the DM on particle agglomeration was found to vary with particle concentration. In concentrated inks (≥2 wt % catalyst), increasing H 2 O % was found to increase agglomeration because of the hydrophobic nature of the catalysts. In dilute inks (<1 wt % catalyst), the trend was reversed with increasing H 2 O %, suggesting that electrostatic interactions are dominating the behavior. In inks with 5 wt % catalyst, the addition of an ionomer was found to significantly stabilize the catalyst against agglomeration. Maximum stability was observed at 0.35 I/C for all DM H 2 O % studied. At high ionomer concentrations (I/C > 0.35), interesting differences were observed between n PA-rich inks (H 2 O % ≤ 50%) and H 2 O-rich (82% H 2 O) inks. The n PA-rich inks remained predominantly stable—ink viscosity only weakly increased with I/C and the Newtonian behavior was maintained for I/C up to 0.9. In contrast, the H 2 O-rich inks exhibited a significant increase in viscoelasticity with increasing I/C, suggesting flocculation of the catalyst by the ionomer. These differences suggest that the nature of the interactions between the ionomer and catalyst is highly dependent on the H 2 O % in the DM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Microstructural Evaluation of Phase Instability in Large Bandgap Metal Halide Perovskites

The optoelectronic performance of organic–inorganic halide perovskite (OIHP)-based devices has been improved in recent years. Particularly, solar cells fabricated using mixed-cations and mixed-halides have outperformed their single-cation and single-halide counterparts. Yet, a systematic evaluation of the microstructural behavior of mixed perovskites is missing despite their known composition-dependent photoinstability. Here, we explore microstructural inhomogeneity in (FAPbI 3 ) x (MAPbBr 3 ) 1– x using advanced scanning probe microscopy techniques. In this work, contact potential difference (CPD) maps measured by Kelvin probe force microscopy show an increased fraction of grains exhibiting a low CPD with flat topography as MAPbBr 3 concentration is increased. The higher portion of low CPD contributes to asymmetric CPD distribution curves. Chemical analysis reveals these grains being rich in MA, Pb, and I. The composition-dependent phase segregation upon illumination, reflected on the emergence of a low-energy peak emission in the original photoluminescence spectra, arises from the formation of such grains with flat topology. Bias-dependent piezo-response force microscopy measurements, in these grains, further confirm vigorous ion migration and cause a hysteretic piezo-response. Our results, therefore, provide insights into the microstructural evaluation of phase segregation and ion migration in OIHPs pointing toward process optimization as a mean to further enhance their optoelectronic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Redox-Induced Microstructure and Phase Dynamics in Nickel: Insights from In Situ Synchrotron X-ray Diffraction

Using in situ synchrotron X-ray diffraction, we interrogate the microstructural and phase evolution of polycrystalline nickel (Ni) during redox cycling in O 2 , H 2 , and H 2 O environments. Oxidation in O 2 promotes strong (111) texturing in both the NiO overlayer and the underlying Ni substrate. However, this crystallographic alignment is lost following reduction in H 2 and subsequent reoxidation, demonstrating irreversible microstructural changes. H 2 exposure leads to proton dissolution into the Ni lattice, triggering a localized phase transition from face-centered cubic (FCC) to hexagonal close-packed (HCP) Ni in hydrogen-saturated regions. In H 2 O-containing atmospheres, dissociative H 2 O adsorption produces protons that permeate the NiO layer, forming γ-NiOOH within the NiO lattice and HCP Ni beneath the NiO overlayer as protons accumulate. Kinetic analysis via the Johnson-Mehl-Avrami–Kolmogorov model uncovers distinct growth mechanisms: preoxidized Ni surfaces follow one-dimensional (1D) kinetics for NiO, γ-NiOOH, and HCP growth, whereas pristine Ni exhibits three-dimensional (3D) kinetics due to island-like nucleation and growth of NiO. Furthermore, these results highlight the critical interplay between H 2 O dissociation, hydrogen permeation, and redox-driven phase transformations, with practical implications in engineering nickel-based catalysts and hydrogen storage systems through controlled microstructural and phase evolution.

36 MATERIALS SCIENCE↗

Imaging grain microstructure in a model ceramic energy material with optically generated coherent acoustic phonons

Characterization of microstructure, chemistry and function of advanced energy materials remains a challenge for instrumentation science with tremendous opportunity for new discoveries. This active area of research is starting to make considerable strides with examples ranging from methodologies that employ bright X-rays, electron microscopy and optical spectroscopy. However, further development of instruments, capable of multimodal measurements, is necessary to fully reveal complex microstructure evolution in realistic environments and dynamical systems that are far from equilibrium. In this regard, laser-based instruments have a unique advantage as multiple measurement methodologies are easily combined into a single instrument using standard optics. A new pump-probe method that uses optically generated acoustic phonons is expanding standard optical characterization by providing depth resolved information. Here we report on a noteworthy extension of this pump-probe method to image grain microstructure in ceria. Rich information regarding the orientation of individual crystallites is obtained by monitoring acoustic phonon velocities and noting how the polarization of the probe beam influences the detected signal amplitude. When paired with other optical microscopies, this methodology will provide new perspectives for optical characterization of ceramic materials.

36 MATERIALS SCIENCE↗

Mutual modulation between surface chemistry and bulk microstructure within secondary particles of nickel-rich layered oxides

Abstract Surface lattice reconstruction is commonly observed in nickel-rich layered oxide battery cathode materials, causing unsatisfactory high-voltage cycling performance. However, the interplay of the surface chemistry and the bulk microstructure remains largely unexplored due to the intrinsic structural complexity and the lack of integrated diagnostic tools for a thorough investigation at complementary length scales. Herein, by combining nano-resolution X-ray probes in both soft and hard X-ray regimes, we demonstrate correlative surface chemical mapping and bulk microstructure imaging over a single charged LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) secondary particle. We reveal that the sub-particle regions with more micro cracks are associated with more severe surface degradation. A mechanism of mutual modulation between the surface chemistry and the bulk microstructure is formulated based on our experimental observations and finite element modeling. Such a surface-to-bulk reaction coupling effect is fundamentally important for the design of the next generation battery cathode materials.

25 ENERGY STORAGE↗

Learning two-phase microstructure evolution using neural operators and autoencoder architectures

Abstract Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. After DeepONet is trained in the latent space, it can be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks.

36 MATERIALS SCIENCE↗

Microstructurally resolved electrochemical evolution of mechanical- and irradiation-induced damage in nuclear alloys

Abstract There is a need for high-throughput, scale-relevant, and direct electrochemical analysis to understand the corrosion behavior and sensitivity of nuclear materials that are exposed to extreme (high pressure, temperature, and radiation exposure) environments. We demonstrate the multi-scale, multi-modal application of scanning electrochemical cell microscopy (SECCM) to electrochemically profile corrosion alterations in nuclear alloys in a microstructurally resolved manner. Particularly, we identify that both mechanically deformed and irradiated microstructures show reduced charge-transfer resistance that leads to accelerated oxidation. We highlight that the effects of mechanical deformation and irradiation are synergistic, and may in fact, superimpose each other, with implications including general-, galvanic-, and/or irradiation-activated stress-corrosion cracking. Taken together, we highlight the ability of non-destructive, electrochemical interrogations to ascertain how microstructural alterations result in changes in the corrosion tendency of a nuclear alloy: knowledge which has implications to rank, qualify and examine alloys for use in nuclear construction applications.

Chen, Xin↗

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗

Alloying effects on deformation induced microstructure evolution in copper

In this work, we investigated the effects of alloying elements on plastic deformation and microstructure evolution in polycrystalline copper (Cu) and Cu alloyed with 1 wt. % lead (Cu-1 %Pb). These materials were selected due to the size mismatch between Cu and Pb, with the latter forming precipitates at grain boundaries. Multi-modal characterization techniques, including neutron diffraction, electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM), along with finite element simulations were employed to study the deformation behavior across multiple length scales. While both Cu and Cu-1%Pb exhibited similar macroscale response and final deformation textures, both dislocation line profile analysis and TEM revealed increased dislocation density in deformed Cu-1%Pb specimens. The presence of lead precipitates also significantly affected local plastic deformation during compression, with their influence diminishing with increasing strain. These results demonstrate the complex relationships between alloying elements, plastic deformation, microstructural evolution, and material behavior under load. The insights gained from this multi-scale and multi-technique approach contribute to the fundamental understanding of microstructural evolution in immiscible alloys and are valuable for tailoring the properties of structural materials for specific engineering applications.

36 MATERIALS SCIENCE↗