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At least 91 records · Page 5

Insight into multi-step geological evolution of C-type asteroids from Ryugu particles

C-type asteroids are the source of the carbonaceous chondrite meteorites and represent remnants of primitive planetesimals that formed at the outer margins of the early Solar System and may have delivered volatiles to the inner Solar System, in particular the early Earth. However, the nature of carbonaceous chondrites is not well understood owing to terrestrial alteration. Here, in this work, we present the petrology and mineral chemistry of surface materials collected by the Japan Aerospace Exploration Agency (JAXA) Hayabusa2 spacecraft from the C-type asteroid Ryugu. The Ryugu particles we studied are similar to CI (Ivuna-type) chondrites but with some important differences, such as the presence of Na–Mg phosphates and Na-rich phases and the lack of ferrihydrite and gypsum. Ryugu particles experienced several steps of aqueous alteration, metasomatism and brecciation under variable conditions. These materials represent mixed lithologies and formed at different locations within their parent asteroid. The evidence presented here demonstrates that the C-type asteroid Ryugu experienced a complex geologic evolution shortly after its formation.

58 GEOSCIENCES↗

Virtual node graph neural network for full phonon prediction

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Additionally, our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility. In this study, the authors present a virtual node graph neural network to enable the prediction of material properties with variable output dimensions. This method offers fast and accurate predictions of phonon band structures in complex solids.

36 MATERIALS SCIENCE↗

Lightweight Metal Stamping Optimization Enabled by Artificial Intelligence

Successfully manufacturing an automotive body structure made via the sheet metal stamping process depends upon simultaneous consideration of component design, tooling design, stamping process control, and material properties. In many cases, introducing lightweight sheet materials (e.g., aluminum alloys, magnesium alloys, advanced high strength steels) holds the potential to significantly reduce vehicle weight, but challenges the stamping process by introducing materials with inherently less ductility. Successful and repeatable applications require co-developing the stamping process controls with the varying material properties, including formability. During the stamping process, as soon as the forming limit of the sheet is exceeded, the material shows localized necking which quickly leads to splits. Controlling process variability to avoid these material splits will enable deployment of less formable, lighter, and stronger materials for stamped automotive components. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as inverse optimization (or backward prediction). This research has been performed in collaborations with USCAR (US Council for Automotive Research) and AutoForm. The members of USCAR are Ford, GM, and Stellantis.

36 MATERIALS SCIENCE↗

Eulerian finite element implementations of a dislocation density-based continuum model

In Eulerian finite element simulations, the mesh moves relative to the material. After every change of position between the mesh and the material, the state variables are interpolated to the new mesh position, which is referred to as advection. Large strain crystal plasticity models are based on the multiplicative decomposition of the total deformation gradient. Further, the stress is evaluated as a function of the thermoelastic strain, temperature, and other state variables. Advection of tensor quantities, such as the strain, is coupled with possibly significant advection errors. In an effort to reduce the advection errors, we develop two rate forms of an established dislocation density-based continuum model. To that end, we replace the multiplicative decomposition of the deformation gradient with the additive decomposition of the velocity gradient, and define the stress rate instead of the total stress. The Eulerian implementation is compared with Lagrangian calculations, and two numerical examples with severe deformation levels are presented.

36 MATERIALS SCIENCE↗

Life cycle cost, energy, and carbon emissions of molds for precast concrete: Exploring the impacts of material choices and additive manufacturing

Molds for precast concrete are commonly used to create simple- to complex-shaped concrete products away from construction sites. These molds are often handmade from wood; however, additively manufacturing (AM, or 3D printing) fiber-reinforced polymer composites is an advantageous alternative, producing significantly more durable, highly complex molds faster, but likely at a higher cost. Here, this study explores the impact of material and production variables on the cost, energy, and carbon emissions of employing composite AM molds over the full lifecycle. The case study employed techno-economic and life cycle assessments to show that using wood flour–poly(lactic acid) or recycled carbon fiber–acrylonitrile butadiene styrene for AM molds can be less expensive than conventional wood molds, especially when considering use phase costs. While wood molds have the least environmental impacts due to wood's higher biogenic carbon sequestration and minimal processing, optimizing AM designs could reduce energy demand, carbon emissions, and cost.

36 MATERIALS SCIENCE↗

Elasto-viscoplastic fast Fourier transform modeling framework for assessing microstructural effects on stress intensity factors characterizing fracture toughness

A large-strain elasto-viscoplastic fast Fourier transform (LS-EVPFFT) model with non-periodic (NP) velocity-based boundary conditions is adapted to simulate the sensitivity of stress intensity factors on microstructure for 304L stainless steel. The material was characterized via electron backscattered diffraction (EBSD) serial-sectioning to obtain a measured 3-D microstructural cell to perform simulations. The NP-LS-EVPFFT model, including the simulation setup and boundary conditions, was verified using a crystal plasticity finite element (CPFE) model. To this end, the generation of meshes of notched specimens was developed, which involved creating Python scripts for mesh “cutting” in Abaqus, and Sculpt scripts in Cubit for meshing of the measured microstructural cell processed with DREAM.3D. The complexity of the mesh preparation highlighted the advantages of the FFT-based model, which circumvents the mesh generation process. Given the efficiency of the FFT-based model, statistical distribution of stress intensity factors in function of crystal orientation at the crack tip, grain structure, and crystallographic texture surrounding the crack tip were predicted. Further, the distributions reveal about 10% variation of stress intensity factors with microstructure with the most significant sensitivity found to be the crystal orientation at the crack tip. The methodology developed in this work is discussed as a practical simulation tool for predicting the sensitivity of stress intensity factors on microstructural variability in metallic materials.

36 MATERIALS SCIENCE↗

Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The basic features and potential applications of each model are explained. Lastly, the key factors for constitutive training and limitations of the current work are also discussed in detail.

36 MATERIALS SCIENCE↗

A complete grain-level assessment of the stress-strain evolution and associated deformation response in polycrystalline alloys

Polycrystalline alloys are used pervasively across structural applications contingent upon extensive experimental testing. A statistically representative number of tests are required to expose the variability in the material's performance, as a result of non-uniform microstructures and associated micromechanical fields. In a more direct means of capturing this pertinent information, multi-modal experimental techniques are presented to measure and track the complete micromechanical state, evolving during loading, of each and every grain within the regions of interest. Specifically, a combination of high-energy X-ray diffraction microscopy and digital image correlation coupled with electron backscatter diffraction are conducted on a specimen for each of the alloys, Haynes 282 and Ti7Al. The results of the multi-modal analysis definitively demonstrate that the degree of heterogeneity increases with deformation level and is used to assess the number of grains necessary for a representative volume element description of the stress state for each of these materials. Moreover, higher resolution imaging is used for identification of the slip system activity and subsequently used to study slip transmission events. An accurate knowledge of the resolved shear stress in adjacent grains (grain interactions) is demonstrated to be a key descriptor of the slip transmission events.

36 MATERIALS SCIENCE↗

Investigating relationship between surface topography and emissivity of metallic additively manufactured parts

Due to the direct relationship between thermal history and mechanical behavior, in situ thermal monitoring is key in gauging quality of parts produced with additive manufacturing (AM). Accurate monitoring of temperatures in an AM process requires knowledge of environment and object parameters including object emissivity. The emissivity is dependent on several variables, including: wavelength, material composition, temperature, and surface topography. Researchers have been concerned with the thermal emissivity dependence on temperature since large ranges are seen in metal powder bed processes, but there is also an extensive range of surfaces produced by AM. This work focused on discovering what roughness characteristics control thermal emissivity through investigation of prototypic 316 stainless steel AM samples produced with a range of build conditions on a laser powder bed fusion machine. Through experimental measurements of emissivity using hemispherical directional reflectance (HDR), guided by simulations using a finite-difference time-domain (FDTD) Maxwell solver, it was found that combinations of existing roughness parameters describing both height and slope of the surface correlate well with emissivity changes. Furthermore, these parameters work well due to their apt description of surface features encouraging internal reflection, which is the phenomenon that increases emissivity when a surface falls under the geometric optical region conditions.

36 MATERIALS SCIENCE↗

Faraday detector uranium isotope ratio measurement: Insights from solution- and laser ablation-based sampling methodologies on the Neoma MC-ICP-MS

In this study we utilize an all-faraday cup detector configuration on the Neoma MC-ICP-MS to perform uranium isotope ratio determinations from certified reference materials and a nuclear fuel precursor material via solution and laser ablation based sampling methodologies. The goal in performing these measurements is primarily to demonstrate that the all-faraday cup method can produce highly precise and accurate isotope ratios even at relatively low signal intensity levels while also developing a preliminary understanding of how the complex interplay between different amplifier resistor levels (e.g. 10 11 vs 10 13 Ω) and integration times impacts the precision and accuracy of different types of measurements. Our results show that the faraday cups with 1013 Ω resistors in their amplifier feedback loops can be used to produce accurate and relatively precise isotope ratios for ion beams down to ~5 K cps. However, at the lower end of the signal intensities observed in this study, longer integration times are necessary to mitigate the impact of ion beam instability that can occur during laser ablation based sampling. Despite this potential limitation, the all-faraday cup method is highly versatile as exemplified by the analysis of a collection of uranium reference materials with highly variable uranium isotopic compositions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Extended continuous cooling transformation diagram for 410 stainless steel encompassing δ → γ and γ → αʹ transformations determined by dilatometry

In this study, we use dilatometry to construct an extended Continuous Cooling Transformation (CCT) diagram for 410 stainless steel, capturing both δ → γ and γ → αʹ transformations specifically for directed energy deposition (DED) additive manufacturing (AM) applications. To ensure that the findings are representative of AM specific microstructures and compositions, dilatometry coupons were extracted from a wire arc DED wall, thereby preserving the inherent microstructural and compositional variability within the material. The on-heating phase transformations, including A c1 , A c3 , and γ → δ, were found to be essentially location independent, despite the typical heterogeneity associated with AM. Conversely, increasing cooling rates were observed to shift the start and finish temperatures of both δ → γ and γ → αʹ transformations to lower values. The resulting extended CCT diagram offers a more complete depiction of phase transformation behavior in the system, enabling accurate microstructure evolution modeling and residual stress predictions specifically for directed energy deposition additive manufacturing applications.

Additive manufacturing↗

The significance of biomass densification in biological-based biorefineries: A critical review

Replacing fossil fuels with renewable biofuels derived from lignocellulosic biomass is an important aspect of addressing environmental challenges and developing a sustainable industrial society. Densification overcomes the problems of low bulk density and poor flowability of biomass and has been commercialized for producing solid biofuels, but the overall impact of densification on biological-based biorefineries that primarily target liquid fuels (e.g., ethanol) is still under investigation. Herein, this review provides a thorough summary of the application of densified biomass in biological-based biorefineries. First, the effects of densification parameters as well as variables of biomass materials on the densified products’ quality are reviewed, and different commonly used densification technologies are also discussed and compared. Then, the discussion focuses on the physiochemical modifications of biomass caused by densification that may influence further pretreatment and/or enzymatic hydrolysis in biorefineries. Industrial pelleting has been generally shown to exhibit positive/neutral effects on the enzymatic hydrolysis of multiple biomass feedstocks after pretreatments, indicating the viability of using pellets as starting feedstocks in biorefineries. Densification causes structural disruption of biomass, which may facilitate further biochemical conversions. Integrating biomass densification in the feedstock supply chain is feasible for large-scale biorefineries to overcome the techno-economic barriers and become profitable. Suggestions are presented for the efficiency enhancement and cost reduction in densification-based biorefineries. To enable wider applications, it is now the time to employ more demo and full-scale activities in different regions of the world pushing the research and innovation of densification integrated within biorefining.

09 BIOMASS FUELS↗

Structure–Function Insights into Thermoresponsive Copolymers as Lanthanide Precipitants

The synthetic toolbox for stimuli-responsive polymers has broadened to include many tunable variables, making these materials applicable in diverse technologies. However, unraveling the key composition–structure–function relationships to facilitate ground-up design remains a challenge due to the inherent dispersity in sequence and conformations for synthetic polymers. We here present a systematic study of these relationships using a model system of copolymers with a thermoresponsive (N-isopropylacrylamide) backbone in addition to metal-chelating (acrylic acid) and hydrophobic structural comonomers and evaluate their efficiency at isolating technologically critical lanthanide ions. The efficiency of lanthanide ion extraction by precipitation was quantitated with a metallochromic dye to reveal trends relating copolymer hydrophobicity to improved separations. Further, we examined the role of different hydrophobic comonomers in dictating the solution-phase conformation of the polymer in the presence and absence of lanthanide ions, and we correlated key features of the hydrophobic comonomer to extraction efficiency. Lastly, we identified how the local proximity of thermoresponsive, chelating, and hydrophobic subunits facilitates metal extraction by manipulating the copolymer sequence with multiblock polymerization. Through mechanistic analysis, we propose a binding-then-assembly process through which metal ions are coprecipitated with macromolecular chelators.

Copolymers↗

Eulerian finite element simulations of the drop weight impact test with a dislocation Density-based continuum model

During a drop weight impact test, the kinetic energy of the falling weight is transferred into the sample resting on the anvil. The plastic deformation in the sample is an important mechanism for the dissipation of the input kinetic energy. We use Eulerian finite element analysis to simulate the deformation and temperature evolution in a 1,3,5-trinitro-1,3,5-triazine (RDX) sample consisting of multiple crystals. In Eulerian finite element simulations, the mesh moves relative to the material. After every change of position between the mesh and the material, the state variables are interpolated to the new mesh position, i.e., advection. In an effort to reduce the advection errors, we use a rate form of a dislocation density-based continuum model by Luscher et al. Here, the simulations predict localization of plastic deformation, and plastic dissipation as a significant source of heat generation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Ab initio generalized Langevin equation

We introduce a machine learning–based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori–Zwanzig formalism, under the constraint of the fluctuation–dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

97 MATHEMATICS AND COMPUTING↗

Defining Computational Emissivity Uncertainty Over Large Temperature Scales Due to Surface Evolution

There is a dearth in the literature on how to capture the uncertainty generated by material surface evolution in thermal modeling. This leads to inadequate or highly variable uncertainty representations for material properties, specifically emissivity when minimal information is available. Inaccurate understandings of prediction uncertainties may lead decision makers to incorrect conclusions, so best engineering practices should be developed for this domain. In order to mitigate the aforementioned issues, this study explores different strategies to better capture the thermal uncertainty response of engineered systems exposed to fire environments via defensible emissivity uncertainty characterizations that can be easily adapted to a variety of use cases. Here, two unique formulations (one physics-informed and one mathematically based) are presented. The formulations and methodologies presented herein are not exhaustive but more so are a starting point and give the reader a basis for how to customize their uncertainty definitions for differing fire scenarios and materials. Lastly, the impact of using this approach versus other commonly used strategies and the usefulness of adding rigor to material surface evolution uncertainty is demonstrated.

97 MATHEMATICS AND COMPUTING↗

Development of Stable Solid Oxide Electrolysis Cells for Low-Cost Hydrogen Production

The project objective was to demonstrate a solid oxide cell-based steam electrolysis stack that exhibits robustness, reliability, endurance, hydrogen purity, and produces hydrogen at elevated pressure of 2 to 3 bar. Innovative materials and processing methods were evaluated to improve degradation characteristics. Performance improvement focused on nearly all layers involved in the cell and stack assembly. Primary attention was paid to zirconia-ceria interface resistance control via sintering optimization and decrease in degradation from the oxygen electrode by evaluating low strontium (Sr) or Sr-free composition for both the oxygen electrode and current collection layer. Stack robustness was addressed by validating redox tolerance of fuel electrode, confirming capability of cells to survive repeated thermal cycles, studying the effect of pressure on performance and degradation, evaluating the effect of contamination on fuel and oxygen electrode performance and degradation, and identifying mitigation strategies to improve performance. The characterization included evaluation of electrochemical performance and stability followed by microstructural analysis. At the cell level, performance and stability improvements were achieved by incorporating a Sr-free oxygen electrode and a denser oxygen electrode barrier layer. At the stack level, pressurized operation reduces demand on first stage compression, the redox tolerant fuel electrode mitigates risk from service interruptions, and improvements to interconnect coating alleviate chromium (Cr) contamination effects. The denser barrier layer was achieved by adding a sintering aid to the samaria-doped ceria (SDC) composition that reduced sintering temperature by 150 °C. The resulting density was on par with the baseline SDC barrier layer density and the lower sintering temperature resulted in less resistive phase formation during sintering. Button cell tests did not demonstrate a change in performance when exposed to silicon (Si) or manganese (Mn) impurities to the fuel electrode and Cr impurity to the oxygen electrode. More detailed study however is warranted. The project addressed SOEC performance and stability at the cell and stack levels through a systematic approach to known sources of degradation that were combined and tested in three stack tests using an electrolyte supported cell design to allow for evaluation of a variety of fuel and oxygen electrode compositions. STK-82 and STK-83 had identical compositions. STK-100 incorporated the best materials and processing variables developed under this and concurrent projects, and was tested at elevated pressure in steam electrolysis. • STK-82 recovered performance after redox and thermal cycling, demonstrating the robustness of the stack and seals. It exhibited stable performance in testing for 500 hours in SOEC mode, followed by 300 hours of cycling between SOEC and SOFC tests. Degradation during SOEC operation was 1.8 %/ 1,000 hours. • STK-83 generated hydrogen at >80% steam conversion, and oxygen above 98.5 % purity during pressurized operation. Both hydrogen and oxygen were generated at 3 barg pressure without the use of a pressure vessel. In addition to balanced pressure, electrolysis operation at 1 bar differential pressure across anode and cathode was also demonstrated to substantial the robustness of the cell and seal. • STK-100 measured at initial ambient pressure conditions showed an area specific resistance of 1.1 ohm-cm 2 , and STK-83 had 1.3 ohm-cm 2 .

08 HYDROGEN↗

King City Asbestos Corporation (KCAC) Mine Carbon Mineralization Field Test (Final Combined Report)

The overall objective of the KCAC carbon mineralization project is to determine the most successful approaches to CO 2 mineralization of asbestos-bearing serpentinite rocks and mine waste at the closed KCAC asbestos mine. The objectives of Budget Period 1 (BP1) are to characterize the site including, mineralogical and geochemical variability in site materials, baseline conditions including CO 2 flux and local climate, and to establish the designs for the second, experimental phase of the project. As described in Section 3 of this report, all tasks outlined in the Statement of Project Objective (SOPO) have been completed or are on schedule to be completed by the end of BP1. As described in Section 4, all success criteria and milestones have been met or will be met by the end of BP1. We conclude that the findings of BP1 strongly support moving to the second phase of the project to investigate methods to increase the rate of CO 2 mineralization. CO 2 flux monitoring during BP1 established an estimated baseline uptake of CO 2 over the 142,193 m 2 KCAC mine site of 147 and 179 metric tons CO 2 /yr using dynamic closed chamber and eddy covariance monitoring methods, respectively. Mineralogical analysis shows that the loose surficial waste material contains pyroaurite and minor hydromagnesite, indicating past carbon mineralization. Sediment samples at depths below 60 cm contained ~3 wt% brucite and localized high brucite contents of ~24 wt% were measured at depth, indicating significant unreacted material at relatively shallow depths. Together, these results demonstrate that despite prolonged exposure to the atmosphere, reactive material is still present and accessible and is naturally taking up atmospheric CO 2 . However, these natural rates are still well below rates that can be achieved by applying practices that can accelerate the CO 2 mineralization reaction. Designs for the Field-Testing Phase (BP2) of the project have been established, incorporating the findings of BP1. Based on results to date we have selected two methods to accelerate the rate of CO 2 mineralization, which we refer to as the Tilling Method and the Greenhouse Method. We hypothesize that these methods can increase the CO 2 mineralization rate by up to 5 times greater than the natural background rate measured in BP1. An Initial Lifecyle Assessment (LCA) and techno-economic analysis (TEA) were conducted for these two methods. The LCA concluded that the baseline version of the Greenhouse and Tilling methods at commercial scale are expected to achieve a CDR efficiency of 97% and 71%, respectively (cumulative, over 30 years of operation). Incorporating the net CO 2 removal rates calculated in the LCA, the TEA found the levelized, 30-year costs of the baseline Greenhouse and Tilling methods to be $\$$95/t-CO 2 and $\$$119/t-CO 2 , respectively, and that costs could be brought to well below $\$$100/t-CO 2 for either method by scaling up the volume of rock utilized. An initial EH&S Risk Assessment, evaluating the potential EH&S risks of the proposed carbon mineralization methods at commercial scale, was completed by LLNL EH&S professionals for these two methods.

58 GEOSCIENCES↗