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Vacancy-Mediated Increases in Brine–Salt Surface Energies
Salt formations have been explored for the permanent isolation of spent nuclear fuel based on their high thermal conductivity, self-healing nature, and low hydraulic permeability to brine flow. Vacancy defect concentrations in salt complicate fracture mechanics not driven by dislocation dynamics and can influence the resulting surface structure. Classical molecular dynamic simulations were used to simulate tensile testing of salt crystals (halite) with vacancy defect concentrations of up to 0.5 defects/nm 3 . Increasing defect concentrations resulted in a decrease in ultimate tensile strength and fracture surface energies, driven by increased surface roughness rather than changes in the amount of surface area. Brine–salt surface energies of the fractured surfaces were 0.22 to 0.26 J/m 2 , significantly higher than values reported for atomically flat (100) surfaces at the same brine composition. This change in surface energy increased the brine–salt dihedral angle by ~27°. The dihedral angle threshold for percolation in salt is 60°, and a 27° increase due to rough fracture surfaces identifies a reduction in porosity percolation and a decrease in salt permeability. Therefore, bedded salt and salt domes may be even more stable than those previously predicted from dihedral angle calculations.
Tailoring Interfacial Interactions via Polymer-Grafted Nanoparticles Improves Performance of Parts Created by 3D Printing
Abstract not provided
A study of real-world micrograph data quality and machine learning model robustness
Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.
A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor
Abstract There has been an ongoing need for information-rich databases in the mechanical-engineering domain to aid in data-driven materials science. To address the lack of suitable property databases, this study employs the latest version of the chemistry-aware natural-language-processing (NLP) toolkit, ChemDataExtractor, to automatically curate a comprehensive materials database of key stress-strain properties. The database contains information about materials and their cognate properties: ultimate tensile strength, yield strength, fracture strength, Young’s modulus, and ductility values. 720,308 data records were extracted from the scientific literature and organized into machine-readable databases formats. The extracted data have an overall precision, recall and F-score of 82.03%, 92.13% and 86.79%, respectively. The resulting database has been made publicly available, aiming to facilitate data-driven research and accelerate advancements within the mechanical-engineering domain.
Engineering block co-polymer anion exchange membrane domains for highly efficient electrode-decoupled redox flow batteries
We report, anion exchange membranes (AEMs) with high permselectivity, chemical stability and mechanical robustness are a key enabling technology for a variety of electrochemical energy conversion and storage systems such as fuel cells, redox flow batteries and water electrolyzers. Herein we examine well understood chemically stable triblock co-polymer AEMs derived by the chloromethylation (CM-) and subsequent trimethylamine (TMA) functionalization of polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene (SEBS) block co-polymers. We show using small angle X-ray scattering (SAXS) that the introduction of a hydrophobic reinforcement matrix disrupts phase segregation and decreases ionic domain sizes in these AEM separators. The reinforcement matrix simultaneously causes the ultimate tensile strength (UTS) of the reinforced separator to increase by almost 600% compared to the unreinforced form, while maintaining comparable elasticity (ca. 500% elongation at break). Such reinforced r-SEBS30-TMA separators enable electrode-decoupled redox flow batteries (ED-RFBs) with significantly improved lifetimes by decreasing per cycle capacity fade from 0.5% to <0.05%. This 10× improvement in operando permselectivity opens the door to ED-RFBs employing inexpensive elemental actives achieving decades-long usable lifespans.
Fracture and fatigue of thin crystalline SrTiO 3 membranes
The increasing availability of a variety of two-dimensional materials has generated enormous growth in the field of nanoengineering and nanomechanics. Recent developments in thin film synthesis have enabled the fabrication of freestanding functional oxide membranes that can be readily incorporated in nanomechanical devices. While many oxides are extremely brittle in bulk, recent studies have shown that, in thin membrane form, they can be much more robust to fracture as compared to their bulk counterparts. Here, we investigate the ultimate tensile strength of SrTiO 3 membranes by probing freestanding SrTiO 3 drumheads using an atomic force microscope. We demonstrate that SrTiO 3 membranes can withstand an elastic deformation with an average strain of ~6% in the sub-20 nm thickness regime, which is more than an order of magnitude beyond the bulk limit. Further, we also show that these membranes are highly resilient upon a high cycle fatigue test, surviving up to a billion cycles of force modulation at 85% of their fracture strain, demonstrating their high potential for use in nanomechanical applications.
Machine learning guided optimal composition selection of niobium alloys for high temperature applications
Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.
Machine learned interatomic potential for dispersion strengthened plasma facing components
Tungsten (W) is a material of choice for the divertor material due to its high melting temperature, thermal conductivity, and sputtering threshold. However, W has a very high brittle-to-ductile transition temperature, and at fusion reactor temperatures (≥1000 K), it may undergo recrystallization and grain growth. Dispersion-strengthening W with zirconium carbide (ZrC) can improve ductility and limit grain growth, but much of the effects of the dispersoids on microstructural evolution and thermomechanical properties at high temperatures are still unknown. We present a machine learned Spectral Neighbor Analysis Potential for W–ZrC that can now be used to study these materials. In order to construct a potential suitable for large-scale atomistic simulations at fusion reactor temperatures, it is necessary to train on ab initio data generated for a diverse set of structures, chemical environments, and temperatures. Further accuracy and stability tests of the potential were achieved using objective functions for both material properties and high temperature stability. Additionally, validation of lattice parameters, surface energies, bulk moduli, and thermal expansion is confirmed on the optimized potential. Tensile tests of W/ZrC bicrystals show that although the W(110)–ZrC(111) C-terminated bicrystal has the highest ultimate tensile strength (UTS) at room temperature, observed strength decreases with increasing temperature. At 2500 K, the terminating C layer diffuses into the W, resulting in a weaker W–Zr interface. Meanwhile, the W(110)–ZrC(111) Zr-terminated bicrystal has the highest UTS at 2500 K.
Effect of deposition rate on microstructure and mechanical properties of 17-4 PH stainless steel fabricated by laser engineered net shaping
Laser engineered net shaping (LENS) is an additive manufacturing technique for fabricating and repairing metallic components. However, the relationships between its process parameters and the resulting mechanical properties of alloys such as 17-4 precipitation-hardening (PH) stainless steel require further investigation to enable reliable application. This study examines the specific effect of LENS deposition rate on the microstructure and mechanical properties of 17-4 PH stainless steel. Specimens were fabricated at two different deposition rates (8.47 and 9.31 mm s−1), subjected to subsequent solution and H900 aging treatment, and then evaluated via tensile testing, hardness measurements, and microscopy. A higher deposition rate results in a finer grain structure but increased porosity, leading to greater ultimate tensile strength (∼1294 MPa) yet lower ductility (strain at failure ∼5.8%) compared to the slower deposition rate (∼1266 MPa, ∼9.0% strain). Hardness follows the same trend as tensile strength. Tensile fracture surfaces for both conditions exhibited a mixed mode of ductile dimples and brittle quasi-cleavage regions, with chromium/silicon oxide particles identified within dimples. Complementary finite element modeling indicates that small void fractions primarily reduce ductility by enhancing localized plasticity, with only a marginal decrease in tensile strength. These integrated experimental and numerical results elucidate the mechanical properties linked to deposition rate, providing insight into tailoring LENS processes to achieve desired properties of 17-4 PH stainless steel.
Experimental investigation of heat treatments on the mechanical performance of grade 300 maraging steel strut-based lattices
Lightweight and strong components are essential for reducing energy consumption and enhancing efficiency. Lattice structures are one such geometry utilized to achieve weight reduction. This study investigates the mechanical properties of various lattice structures fabricated from Maraging Steel (EOS MS1) using the Direct Metal Laser Sintering (DMLS) method. The samples include three distinct cellular geometries: body-centered cubic (BCC), face-centered cubic (FCC), and octet truss configurations, which are subjected to tensile and compressive tests. The primary goal of this research is to evaluate the impact of heat treatment on the mechanical properties of cellular architecture under tensile and compressive loading conditions. Destructive, nondestructive testing, and simulation results were also obtained from different heat treatment processes. It was found that the age-hardened specimens performed the best overall in terms of ultimate tensile/compressive strength and elongation. The top-performing topologies in compression and tension were found to be the octet structure, as they were able to withstand the most loading and straining when compared to the other specimens.
Guidance on the Use of Existing ASTM Polymer Testing Standards for ABS Parts Fabricated Using FFF
When evaluating the static mechanical performance of fused filament fabrication (FFF) polymers, researchers have conducted tensile testing using American Society for Testing Materials International (ASTM) D3039 rectangular test specimens and ASTM D638 Type I and Type IV dogbone test specimens. The choice of ASTM D638 versus ASTM D3039 test specimen geometry is usually based on the perceived susceptibility of the ASTM D638 test specimens to failure within the fillet radius. ASTM polymer and plastic tensile test standards define criteria for acceptably tested specimens as requiring failure inside the narrow length (i.e., ASTM D638) or outside of the grips (i.e., ASTM D3039) for the results to be considered acceptable. There has been limited published research regarding the selection of an ASTM test specimen geometry for FFF polymer materials. This study provides evidence-based guidance through the comparison of the mechanical performance and failure acceptance rates of ASTM D3039 and ASTM D638 test specimen geometries, fabricated using FFF acrylonitrile butadiene styrene (ABS). Here, the purpose of the study is to provide guidance on the use of existing ASTM polymer testing standards for additively manufactured polymers. Results indicate there is an inherent benefit to using ASTM D3039 over ASTM D638 Type I and Type IV test specimens for tensile testing. ASTM D3039 test specimens provide the most consistent failure within the test specimen’s gage length, which is attributed to the rectangular design of the test specimen. Results also indicated that, like traditionally manufactured polymer composites, there will be differences in the tensile test results (e.g., ultimate tensile strength, elastic modulus) based on the different cross-sectional areas of the test specimen geometries.
3D printable silicone compositions exhibiting high toughness and low durometer
3D printable silicones can be designed with varying crosslink density, network structures, and types of reinforcing additives. Within this design space, one may tailor the uncured material’s rheology and mechanical response to access a wide range of potential applications. However, printable, low modulus silicones, particularly those applicable to direct ink write, are underreported in published literature. To address the need for higher performance, low modulus silicones with demonstrated printability, a new set of ca. 20–50 Shore A hardness silicone elastomers exhibiting ca. 7 MPa ultimate tensile strength and ca. 400–1200% elongation at break is presented. Mechanical properties were analyzed, providing insight to the effects of formulation constituents on mechanical properties. Cyclic mechanical testing of the silicone formulations was also performed, and the energy loss and permanent set throughout cycling were determined. In conclusion, printed structures demonstrate the feasibility of these new silicones as durable frameworks for novel soft device applications.
Signature Analysis Utilizing a Dynamic Molten Salt Reactor Model for MC&A
Moving from traditional fixed fuel nuclear reactor systems to a mobile, dynamic fuel system that has dissolved special nuclear material in a molten salt is a paradigm shift in several respects. One major consideration is how to develop effective nuclear material controls and accounting for these novel reactor systems. The Molten Salt Reactor Experiment is one example of a critical molten salt system and provided a significant reference library based on the documented effort. But it was low thermal power that was not intended to reflect a commercial scale electricity production design. Therefore, to facilitate and assist vendors with domestic licensing considerations, research is underway to identify methods that could be used for domestic safeguards approaches for these novel reactor systems. This research presents the results of a signature analysis of data generated from three simulated scenarios using the molten salt demonstration reactor model defined in the Transient Simulation Framework of Reconfigurable Modules. Each scenario provides 1 hour isotope inventories over a 180 day period. The scenarios investigated provide test cases to examine if direct gamma-ray spectroscopy of the fuel can be used to identify changes comparing a base case (no reactivity control and fixed fission contribution) to an insertion of reactivity (10 pcm no change in fission composition) and a change in fission composition. The analysis demonstrates that monitoring the total count rate in a highly collimated high-resolution photon energy spectrum is sensitive to perturbations imposed into the reactor model. The total photon count rate changes ≈2% for the fission composition change and ≈4.5% for the reactivity insertion compared to the base case. However, both scenarios show an increase in the total photon count rate. The total photon count rate can be used to identify changes due to power (number of fissions) but not due to a change in the material undergoing fission. To distinguish between the two cases of increased power, the photon spectrum would require an intensive analysis technique. A photon peak count rate ratio analysis could be used to identify changes in the fissile material fission generation in the core through identification of a static peak that shows little variation to the source of fission and a highly varying peak. The photon peak strength will ultimately be determined by the isotope’s fission yield. A preliminary analysis investigating the coupling of the isotope’s fission yield and its concentration in the fuel salt derived from the modeling has been performed. A ratio analysis of the photon peak count rates of 140 La to 99 Mo demonstrated that the reactivity insertion creates a distinct difference in the ratio compared to the fission composition change scenario.
High-Temperature Tensile and Creep Test Results on Thin Wall Tube Specimens of ODS Alloys 14YWT and OFRAC
High-temperature tensile and strain rate jump (SRJ) creep tests were conducted on axial specimens fabricated from thin wall tubes of the oxide dispersion strengthened (ODS) alloys 14YWT and OFRAC. The dimensions of the 14YWT thin wall tube was 10.7 mm OD, 0.5 mm WT and 0.92 m and of the OFRAC thin wall tube was 8.5 mm OD + 0.5 mm WT and 1.78 m in length. The axial specimens fabricated from the thin wall tubes were based on a dual-gauge design with gauge dimensions of 4 mm long and 2 mm wide. The tensile tests were conducted in air at 800ºC, 900ºC and 1,000ºC and the SRJ creep tests were conducted in air at 800ºC. Two axial specimens were used in each tensile and SRJ creep tests. The results of the tensile tests showed much lower yield stress (YS) compared to ultimate tensile strength (UTS) indicative of large work hardening properties. The results showed the UTS of OFRAC was higher than that of 14YWT by ~35 MPa at 800ºC. At 900ºC, the UTS of OFRAC and 14YWT was similar at 900ºC and at 1000ºC, the UTS of 14YWT was higher than that of OFRAC by ~10 MPa. The ductility properties of OFRAC were greater than that of 14YWT with higher values of uniform elongation (UE) and total elongation (TE) at all tensile test temperatures. The creep properties of 14YWT and OFRAC were evaluated by the strain rate jump test method at 800ºC. The stress exponent (n) for creep was obtained from the SRJ test data by plotting the values of the log strain rate against the log stress, which is measured from the stress-strain curve generated during the SRJ test. The results showed the highest stress exponents of n = 19.8 and n = 35.0 were measured from the two SRJ tests on 14YWT. The stress exponent measured from one successful SRJ test on OFRAC was n = 13.3, which was lower than that of 14YWT. Nevertheless, these high values of stress exponent are consistent with ODS ferritic alloys such as 14YWT and OFRAC since they indicate dislocation-particle interactions are the dominant creep mechanism for specific ranges of temperature and stress. The results obtained from the tensile and SRJ creep tests conducted on axial specimens in this study indicate that 14YWT and OFRAC retain high strengths at temperatures up to 1,000ºC and good creep performance at 800ºC.
Effects of Sodium Exposure on the Tensile Properties of Grade 91 steel
Two heats of G91 steel have been investigated in liquid sodium at 550- 650°C to understand their corrosion behaviour, microstructural evolution, and tensile properties. Sodium exposure experiments of subsized, sheet-type tensile specimens of G91 were performed in Argonne’s forced convection sodium materials testing loops, SMT-1 and SMT-2. Maximum exposure times of ~49,000 h at ~38,000 h, and ~20,000 h have been achieved at 550, 600, and 650°C, respectively. Thermal aging experiments of G91 steel were also conducted in parallel, and the results were compared with those of the sodium-exposed specimens to isolate the effect of thermal aging from the effect of sodium exposure. It was found that the yield stress and ultimate tensile strength of G91 were reduced by more than 50% after sodium exposures at 650°C due to decarburization. The reduction in tensile strength was accompanied by the dissolution of M 23 C 6 carbides and excessive grain growth. The effect of sodium at 550 and 600°C was insignificant for exposure times up to ~49,000 h and ~38,000 h, respectively. The effect of sodium on the microstructural stability and tensile properties of G91 is largely dependent on the carbon mass transfer in the alloy-sodium system.
An ICME Modeling Framework for Titanium/Tungsten-Carbide Metal Matrix Composites
This report describes a collaborative project to develop a validated, predictive model for the high temperature mechanical properties of a titanium-matrix, tungsten-carbide/cobalt-reinforced metal matrix composite. The modeling approach was to first develop a detailed, microstructural model linking the material structure and the interfacial debonding properties to the effective properties of the material. The project then completed a throughput simulation campaign to generate a large number of simulations for discrete microstructures and different debonding parameters. Finally, the project trained a fast, Gaussian process surrogate model against this simulation database to provide a quick model linking the material compositions, structure, and processing parameters to the resulting material properties. This model was validated against high temperature tensile test data on a few particular composite compositions. The tests validate the model predictions for ultimate tensile strength and uniform elongation/ductility, meaning the final surrogate model can now be used to tune the material composition and processing parameters to identify optimal composite compositions for particular applications.
Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel
We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.