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At least 361 records · Page 20

Bayesian inference of fiber orientation and polymer properties in short fiber-reinforced polymer composites

Herein we present a Bayesian methodology to infer the elastic modulus of the constituent polymer and the fiber orientation state in a short-fiber reinforced polymer composite (SFRP). The properties are inversely determined using only a few experimental tests. Developing composite manufacturing digital twins for SFRP composite processes, including injection molding and extrusion deposition additive manufacturing (EDAM) requires extensive experimental material characterization. In particular, characterizing the composite mechanical properties is time consuming and therefore, micromechanics models are used to fully identify the elasticity tensor. Hence, the objective of this paper is to infer the fiber orientation and the effective polymer modulus and therefore, identify the elasticity tensor of the composite with minimal experimental tests. To that end, we develop a hierarchical Bayesian model coupled with a micromechanics model to infer the fiber orientation and the polymer elastic modulus simultaneously which we then use to estimate the composite elasticity tensor. We motivate and demonstrate the methodology for the EDAM process but the development is such that it is applicable to other SFRP composites processed via other methods. Our results demonstrate that the approach provides a reliable framework for the inference, with as few as three tensile tests, while accounting for epistemic and aleatory uncertainty. Posterior predictive checks show that the model is able to recreate the experimental data well. The ability of the Bayesian approach to calibrate the material properties and its associated uncertainties, make it a promising tool for enabling a probabilistic predictive framework for composites manufacturing digital twins.

36 MATERIALS SCIENCE↗

Focused Ion Beam Tomography of Alloy 617 Corroded in Molten Chloride Salt

Materials qualification of reactor structural materials is a critical step in rapid implementation of advanced nuclear reactor technologies, particularly to assess the corrosion performance in these designs. Accelerated qualification of reactor structural materials requires incorporating powerful computational toolsets, such as phase field modelling in the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, to predict the evolution of structural materials due to corrosion. Accordingly, computational toolsets will require experimental data generated at appropriate length scales to validate accuracy. Focused ion beam (FIB) provides a high degree of control over manipulation of materials for analytical purposes, including capturing data on the evolution in the microstructure and elemental composition of materials at the mesoscale, an appropriate length scale for phase field modelling of intergranular diffusion phenomena using the MOOSE framework. For instance, the FEI Helios G4 UX dual beam plasma FIB microscope at the Irradiated Materials Characterization Laboratory (IMCL) is capable of backscatter diffraction (EBSD) and energy-dispersive x-ray spectroscopy (EDS) documenting the evolution in the microstructure and elemental composition, respectively. The Helios can perform EDS and EBSD three-dimensionally (3D) using tomography, which is then combined using different software packages to visualize 3D volumes correlating elemental composition to microstructural data. The purpose of this investigation was to develop a streamlined characterization and data processing workflow for 3D tomography studies on the FEI Helios G4 plasma FIB. The investigation is segmented into three parts: 1) Optimizing the data collection workflow, 2) identifying appropriate data processing and visualization software (i.e. DREAM.3D, MIPAR, and VGStudioMax), and 3) establishing an infrastructure for public release. The optimization of the data collection workflow is in collaboration with members of the U220 department to setup formal training on the tomography operation of the G4, through ThermoFisher Scientific, and exploring DREAM.3D, MIPAR, and VGStudioMax data processing/visualization software packages. VGStudioMax currently demonstrates the most promise for future use. Optimization of the data collection and processing workflow is still ongoing. A collaboration with INL High Performance Computing (HPC) established an open-source license for expediting the public release of FIB tomography datasets through HPC. FIB tomography data generated by the G4 will provide comprehensive data for validating 3D phase field mesoscale modelling tools within the MOOSE framework for accelerated qualification of reactor structural materials.

Copeland-Johnson, Trishelle↗

Nitride or Oxynitride? Elucidating the Composition–Activity Relationships in Molybdenum Nitride Electrocatalysts for the Oxygen Reduction Reaction

Molybdenum nitride (Mo–N) catalysts have shown promising activity and stability for the oxygen reduction reaction (ORR) in acid. However, the effect of oxygen (O) incorporation (from synthesis, catalysis, or exposure to air) on their activity remains elusive. Here, we use reactive sputtering to synthesize three compositions of thin-film catalysts and use extensive materials characterization to investigate the depth-dependent structure and incorporated O. We show that the as-deposited Mo–N films are highly oxidized both at the surface (>30% O) and in the bulk (3–21% O) and that the ORR performance is strongly correlated with the bulk structure and composition. Activity for 4e– ORR is highest for compositions with the highest N/O and N/Mo ratio. Furthermore, H 2 O 2 production for the films with moderate O content is comparable to or higher than the most H 2 O 2 -selective nonprecious metal catalysts in acidic electrolyte, on a moles per mass or surface area of catalyst basis. Density functional theory provides insight into the energetics of O incorporation and vacancy formation, and we hypothesize that activity trends with O/N ratios can be traced to the varying crystallite phases and their interactions with ORR adsorbates. This work demonstrates the prevalence and significance of O in metal nitride electrocatalysts and motivates further investigation into the role of O in other nonprecious metal materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stochastic microstructural analysis of ceramic matrix composites using a high-fidelity multiscale framework

This paper presents a high-fidelity multiscale framework to simulate the mechanical behavior of ceramic matrix composites (CMCs), accounting for the complex micromorphology captured using detailed material characterization. First, high-resolution micrographs are obtained for the specific carbon fiber silicon-carbide-nitride matrix (C/SiNC) CMC to characterize the variability of the architectural features and manufacturing-induced defects at the microscale. An image processing algorithm is then used to precisely estimate the size and distribution of all subscale features and defects from the micrographs. The information is then used to generate a three-dimensional stochastic representative volume element (SRVE) to reconstruct microscale constituents accounting for the variability. As a result, the generated SRVEs are simulated using the high-fidelity generalized method of cells (HFGMC) micromechanics theory to investigate the effects of defects on the elastic properties of C/SiNC CMCs.

Ceramic Matrix Composites↗

K–Co–Mo–S x chalcogel: high-capacity removal of Pb 2+ and Ag + and the underlying mechanisms

Chalcogenide-based aerogels, known as chalcogels, represent a novel class of nanoparticle-based porous amorphous materials characterized by high surface polarizability and Lewis base properties, exhibiting promising applications in clean energy and separation science. This work presents a K–Co–Mo–S x (KCMS) chalcogel as a highly efficient sorbent for heavy metal ions and details its sorption mechanisms. Its incoherent structure comprises Mo 2 V (S 2 ) 6 and Mo 3 IV S(S 6 ) 2 anion-like clusters with four- and six-coordinated Co–S polyhedra, forming a Co–Mo–S covalent network that hosts K + ions through electrostatic attraction. The interactions of KCMS with heavy metal ions, particularly Pb 2+ and Ag + , reveal that KCMS is exceptionally effective in removing these ions from ppm concentrations down to trace levels (≤5 ppb). KCMS rapidly removes Ag + (≈81.7%) and Pb 2+ (≈99.5%) within five minutes, achieving >99.9% removal within an hour, with a distribution constant K d ≥10 8 mL g -1 . KCMS exhibits an impressive removal capacity of 1378 mg g -1 for Ag + and 1146 mg g -1 for Pb 2+ , establishing it as one of the most effective materials known to date for heavy metal removal. This material is also effective for the removal of Ag + and Pb 2+ along with Hg 2+ , Ni 2+ , Cu 2+ , and Cd 2+ from various water sources even in the presence of highly concentrated and chemically diverse cations, anions, and organic species. Analysis of the post-interacted KCMS by synchrotron X-ray pair distribution function (PDF), X-ray photoelectron spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDS) revealed that the sorption of Pb 2+ , Ag + , and Hg 2+ mainly occurs by the exchange of K + and Co 2+ . Despite being amorphous, this material exhibits unprecedented ion-exchange mechanisms both for the ionically and covalently bound K + and Co 2+ , respectively. In conclusion, this discovery advances our knowledge of amorphous gels and guides material synthesis principles for the highly selective and efficient removal of heavy metal ions from water.

54 ENVIRONMENTAL SCIENCES↗

The Inner Shell Spectroscopy beamline at NSLS-II: a facility for in situ and operando X-ray absorption spectroscopy for materials research

The Inner Shell Spectroscopy (ISS) beamline on the 8-ID station at the National Synchrotron Light Source II (NSLS-II), Upton, NY, USA, is a high-throughput X-ray absorption spectroscopy beamline designed for in situ, operando, and time-resolved material characterization using high monochromatic flux and scanning speed. This contribution discusses the technical specifications of the beamline in terms of optics, heat load management, monochromator motion control, and data acquisition and processing. Results of the beamline tests demonstrating the quality of the data obtainable on the instrument, possible energy scanning speeds, as well as long-term beamline stability are shown. The ability to directly control the monochromator trajectory to define the acquisition time for each spectral region is highlighted. Examples of studies performed on the beamline are presented. The paper is concluded with a brief outlook for future developments.

36 MATERIALS SCIENCE↗

Use of Graph Theory and Neural Networks for Microstructural Classification

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Modifications in segmentation also show potential in improving the accuracy of our current pyCHIP classifier. Replacing previous encoders trained on ImageNet with those trained on microscopy images like MicroNet has initially shown better performance at classifying images of irradiated samples. Additionally, different normalization approaches were tested to show no discernable effect on classification. The Louvain method for community detection is analyzed on a set of irradiated samples with different parameters to determine which proved beneficial under what circumstances. We suggest that microscopy experiments be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗

Initial Temperature Testing of Advanced Manufactured Melt Wire Package

As a part of the In-Pile Instrumentation Program, The Idaho National Laboratory (INL) has recently established in-house capabilities to fabricate and test new advanced manufactured sensors for measuring the peak irradiation temperature inside a nuclear test reactor. Although nuclear test reactors can use standard methods of real-time temperature monitoring, such as thermocouples, the complexity of feedthroughs and attachment complications to collect real-time measurements greatly increases the cost of the experiment. Passive monitoring techniques may be utilized that exploit the melting point of well characterized material (standard melt wires), however, the limited space available for instrumentation during experiments is also a challenge. To accommodate the challenge, INL has expanded its temperature detection instrumentation capabilities paired with the advancement of in-house advanced manufacturing options to include printed melt wires for peak irradiation temperature measurement. The advanced manufactured melt wire package can determine peak temperatures in experiments that can fit the challenging space available during irradiation. Recent results and knowledge from this ongoing work have made it possible to develop unique temperature sensors that can accomplish user specific temperature ranges inside the harsh environments experienced during radiation testing. This report summarizes the initial temperature testing using fabricated melt wires with two different melting temperatures, silver (melting temperature of ~960 °C) and solder (melting temperature of ~160 °C), that were initially tested for their relative melting points using non-standard method of furnace and hot plate testing under normal air conditions. Future work will include more controlled way of testing printed melt wires under inert gas with multiple temperature measurements followed by comparison of standard melt wire package performance for baseline characterization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Development of Multimodal Few-Shot Analytics for Electron Micrographs

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Attempts at creating a multimodal approach can provide further improvements to current models and help extract more salient features from data. In this vein, raw spectrum data was taken to provide an additional modality to our current pyCHIP classifier. Modifications in segmentation also show potential in improving the accuracy of the pyCHIP classifier. Classifier output was analyzed using network graphs and unsupervised clustering algorithms such as spectral clustering to detect better segmentation methods than the current “chipping” approach. We suggest that the chip selection process can be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗

Atomically Dispersed Ni‐N‐C Catalysts for Electrochemical CO 2 Reduction

Abstract The atomic dispersion of nickel in Ni‐N‐C catalysts is key for the selective generation of carbon monoxide through the electrochemical carbon dioxide reduction reaction (CO 2 RR). Herein, the study reports a highly selective, atomically dispersed Ni 1.0% ‐N‐C catalyst with reduced Ni loading compared to previous reports. Extensive materials characterization fails to detect Ni crystalline phases, reveals the highest concentration of atomically dispersed Ni metal, and confirms the presence of the proposed Ni‐N x active site at this reduced loading. The catalyst shows excellent activity and selectivity toward CO generation, with a faradaic efficiency for CO generation (FE CO ) of 97% and partial current density for CO (j co ) of ‐9.0 mA cm −2 at ‐0.9 V in an electrochemical H‐type cell. CO 2 RR activity and selectivity are also studied by rotating disk electrode (RDE) measurements where transport limitations can be suppressed. It is expected that the utility of these Ni‐N‐C catalysts will lie with tandem CO 2 RR reaction schemes to multi‐carbon (C 2+ ) products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mechanical and chemical properties of PVD and cold spray Cr-coatings on Zircaloy-4

In this paper, accident-tolerant fuel (ATF) cladding materials aim to improve fuel reliability and safety during accident scenarios in water-cooled reactors. These ATF cladding should also perform comparable or better than the current zirconium alloy cladding under reactor normal operating conditions. Surface-modified Zircaloy-4 was produced by depositing a protective coating of chromium by two different coating techniques, Physical Vapor Deposition (PVD) and Cold Spray (CS). The coating thickness, structure, composition, and mechanical properties of the coated Zircaloy were assessed with advanced materials characterization techniques. Results showed a strengthening effect of the Cr-coating, along with ~83% higher hardness compared to that of the Zircaloy-4 substrate. Surface topography and surface chemistry of the Cr-coated Zircaloy-4 were assessed with contact profilometry, atomic force microscopy, contact angle goniometry, and X-ray photoelectron spectroscopy. Deposited Cr-coating significantly improved surface wettability compared to the substrate Zircaloy-4. The formation of surface oxides on the coatings by prolonged exposure to ambient air showed a further increase in the hydrophobic behavior. Surface parameters and surface chemistry after flow boiling critical heat flux tests, conducted at atmospheric pressure, evidenced a slight increase in surface roughness with notable changes in wettability associated with the formation of surface oxides.

36 MATERIALS SCIENCE↗

Ultra-clear Aerogels for Insulated Glass in Refrigeration Doors (CRADA Final Report)

Display refrigerators commonly employ glass doors to provide product visibility; however, these doors exhibit significantly poorer thermal insulation than solid doors, resulting in increased heat transfer and higher energy consumption. To address this limitation, this project investigated the use of silica aerogel as a transparent insulating medium for refrigerator glass doors, with the objective of enhancing thermal performance without compromising visual clarity. In Phase I of the project, we assessed the feasibility of silica aerogel insulation through laboratory and full-scale studies, including material characterization, durability testing, and heat transfer measurements, along with the development and performance evaluation of prototype glass doors under realistic operating conditions. In Phase II, the project focused on scaling the technology toward practical deployment. This included scaling up the manufacturing process to achieve a surface area roughly 20 times larger for commercial applications, developing full-scale prototypes that integrated silica aerogel into standard display refrigerator doors, and conducting field evaluations to assess thermal performance, energy savings, and operational durability under real-world conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Deep learning at the edge enables real-time streaming ptychographic imaging

Abstract Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods.

36 MATERIALS SCIENCE↗

Robust Heat-Flux Sensors for Coal-Fired Boiler Extreme Environments

In this project, robust heat-flux measurement systems were developed. The heat-flux sensors utilize thermoelectric effects to directly transduce the heat-flux inputs to analog electrical voltage signals. They were constructed from dedicated materials that can withstand temperatures of at least 1000°C and maintain adequate performance at these conditions for prolonged periods of time. The proposed approaches took into account numerous considerations, including system cost, sensor head resilience, sensor footprint, data accuracy, response time, and maintenance requirements. Through modern thermoelectric materials design, methodical materials selection and rigorous testing in materials characterization labs and medium-scale fire research facilities, we have demonstrated functioning laboratory prototypes, upon which one could base industrial heat-flux sensing platforms capable of operating in the challenging high-temperature, corrosive environments of the boilers of coal-fired power plants. A distributed sensor array for heat-flux measurements throughout the furnace water-wall, the superheater area and the economizer coils can provide critical data for the power plant control systems to increase efficiency, improve safety and reduce down times. For example, the combined heat-flux sensor/control systems can contribute to the optimization of burner and boiler operations under flexible loads, the optimization of heat-exchange conditions and overall reduction of heat rate and emissions, the prediction of imminent overheating conditions, and the optimization of the soot-blowing protocols.

20 FOSSIL-FUELED POWER PLANTS↗

Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy

Atom probe tomography, and related methods, probe the three-dimensional architecture of a material. The software tools that microscopists use, and how these tools are connected into workflows, makes a substantial contribution to the accuracy and precision of such a material characterization experiment. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced that collaborating is useful. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results.

36 MATERIALS SCIENCE↗

Fe-carbide/Fe-oxide-based nanocomposites synthesized as magnetic nanomaterials via laser ablation synthesis in solution (LASiS)

Magnetic nanostructured materials (MNMs) have gained prominence in materials technology developments owing to their potential biomedical applications for hyperthermia cancer treatment, and transplant organ cryopreservation. Herein, we report the facile and cost-effective synthesis of Fe-based composite MNMs, comprising both Fe@Fe 2 O 3 and Fe 3 C@C core-shell nanoparticles (NPs), via Laser Ablation Synthesis in Solution (LASiS) using Fe targets ablated under acetone and toluene. Detailed materials characterizations using electron microscopy-based imaging, diffraction studies, and spectroscopic analyses - including Raman and Mössbauer spectroscopy - relate the structure-composition properties for different Fe-oxide/carbide phases in the aforesaid MNMs to their respective magnetic responses. Specifically, we confirm the presence of ultra-small (2–10 nm) amorphous Fe-oxide NPs, as well as Fe@Fe 2 O 3 core-shell NPs (20–40 nm) in the samples synthesized by ablating Fe under acetone. In contrast, samples synthesized under toluene indicate a higher concentration of Fe 3 C@C core-shell NPs (20–40 nm) with a relatively low concentration of Fe 2 O 3 NPs (2–10 nm). Furthermore, the crystallinity of the metallic phases and carbonaceous shell coatings are systematically increased by carrying out LASiS under heated toluene (up to ~95 °C). Importantly, mössbauer spectroscopy results indicate that the elevation in toluene temperature leads to an increase in the concentrations of Fe 3 C@C NPs from ~40 % to ~53 % (at.).

36 MATERIALS SCIENCE↗

Pulsed-Beam Transmission Electron Microscopy and Radiation Damage

Here, we review the use of pulsed electron-beams in transmission electron microscopes (TEMs) for the purpose of mitigating specimen damage. We begin by placing the importance of TEMs with respect to materials characterization into proper context, and we provide a brief overview of established methods for reducing or eliminating the deleterious effects of beam-induced damage. We then introduce the concept of pulsed-beam TEM, and we briefly describe the basic methods and instrument configurations used to create so-called temporally structured electron beams. Following a brief overview of the use of high-dose-rate pulsed-electron beams in cancer radiation therapy, we review historical speculations and more recent compelling but mostly anecdotal findings of a pulsed-beam TEM damage effect. This is followed by an in-depth technical review of recent works seeking to establish cause-and-effect relationships, to conclusively uncover the presence of an effect, and to explore the practicality of the approach. These studies, in particular, provide the most compelling evidence to date that using a pulsed electron beam in the TEM is indeed a viable way to mitigate damage. Throughout, we point out current gaps in understanding, and we conclude with a brief perspective of current needs and future directions.

36 MATERIALS SCIENCE↗

CRADA Final Report: CRADA Number NFE-24-10036 with ThermaMatrix, Inc.

ThermaMatrix, Inc provides novel vision inspection solutions for a wide range of manufacturers and industries, providing and implementing the leading technologies for nondestructive inspection (NDI) and material characterization. Many other inspection solutions are either not adequate or are not approachable due to implementation barriers needing expert level operators, excessive inspection time, and high cost. ThermaMatrix’s advanced vision inspection technology addresses all of these limitations. The Lab Embedded Entrepreneurial Program (LEEP) opportunity by the Department of Energy (DOE) allows small-business start-ups to leverage national laboratory capabilities and skilled scientists to rapidly develop their technology that aligns with DOE goals. ThermaMatrix, Inc. was positioned in the Innovation Crossroads program at Oak Ridge National Laboratory to further develop the novel Watson Vision Inspection System to support manufacturing quality control efforts. The research goals were (1) explore fundamental parameters that would improve preexisting capabilities, (2) full-scale industrial setup for demonstration, and (3) capability testing and verification. Manufacturing is demanding more NDI implementation to support their quality control needs, which this technology development would support.

36 MATERIALS SCIENCE↗