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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Meshfree simulation and prediction of recrystallized grain size in friction stir processed 316L stainless steel

Friction stir processing (FSP) is a promising solid-phase microstructural modification technique that can repair and enhance damaged stainless steel surfaces exposed to harsh environments. The quality of the repaired material is closely correlated to the recrystallized grain size in the stir zone (SZ), which is influenced by the thermomechanical conditions dictated by FSP process parameters. Thus, establishing a reliable relationship between these parameters and recrystallized grain size in the SZ is crucial for optimizing repair quality. However, existing experimental approaches often rely on indirect temperatures measured far from the SZ, along with rough strain rate estimations, which are imprecise and time-consuming. Meanwhile, existing mesh-based modeling methods usually face numerical challenges when dealing with the large material deformations inherent in FSP. Here, to address these issues, this study introduces a meshfree process model for FSP based on the smoothed particle hydrodynamics (SPH) method, aimed at predicting process conditions under different parameters. The model is validated using experimental data from 11 combinations of tool traverse and rotation speeds on 316 L stainless steel. Correlations between process parameters, material flow, temperature, strain, strain rate, and recrystallized grain size are revealed through SPH simulations and electron backscatter diffraction (EBSD) imaging. The results show that in situ SZ temperatures range from 1071 to 1322°C, which exceed the tool temperature by over 300°C. Furthermore, SZ temperature, strain rate, and grain size increase monotonically with higher tool temperature and faster traverse speed. A relationship is then established between the model-predicted Zener-Hollomon parameter and the recrystallized grain size based on EBSD data, expressed as ln(d) = -0.364 ln(Z) + 14.673. Finally, this relationship exhibits satisfactory accuracy with errors of less than 26.9% in predicting grain sizes at various SZ locations, which offers valuable insights for optimizing FSP repair processes for 316 L stainless steel.

316L stainless steel↗
DOE OSTI2026

Effect of fiber sizing and glass fiber laminate hybridization on vibration damping and mechanical properties of banana fiber reinforced polypropylene composites

Modern automotive applications demand lightweight, multifunctional materials to reach mileage goals and natural fiber reinforced composites (NFRCs) are one of the classes of materials proposed as a solution. NFRCs exhibit good vibration damping properties and have low density, but are often limited by processing challenges, poor-fiber matrix compatibility and variable performance. Herein, we investigate non-woven wet-lay of comingled banana fiber (BF), recycled glass fiber (rGF), and polypropylene (PP) fibers to in situ sizing and preparation of composite feedstocks for compression molding. BF and rGF hybrids were prepared by stacking rGF layers during compression molding to produce composites with various fiber ratios. The effect of fiber content, in-situ sizing and ratio of BF to rGF on tensile, flexural and vibration damping performance are investigated. Key results are the significant increase in tensile strength by in situ sizing (40 % sized at 60 wt% BF) and in flexural modulus (+58 % sized at 60 wt% BF) and flexural strength (+41 % sized 60 wt% BF) compared to the unsized equivalent. For BF-rGF hybrid composites with40 wt% total fiber content, flexural strength and modulus were improved by 51 % and 231 % respectively for a 1:1 ratio BF:rGF compared to BF reinforced system. Lastly, identifying the cross-over point where damping and stiffness are optimized for a hybrid composite. These findings demonstrate that these composites can be used as alternative to synthetic fiber or mineral filled composites in automotive applications, particularly where weight reduction, vibration damping and stiffness are desired.

Banana fiber↗
DOE OSTI2025

Influence of particle size on NIR spectroscopic characterization of sorghum biomass for the biofuel industry

NIR spectroscopy is a rapid and accurate green technology for high-throughput biomass characterization, including sorghum (Sorghum bicolor), a promising energy crop for the biofuel industry. This study assessed the influence of particle size on NIR spectroscopic analysis (wavelength range: 867–2535 nm) of sorghum biomass composition. Grown under field conditions, a total of 113 types of genetically diverse sorghum accessions were dried, ground, and sieved (<250, 250–600, 600–850, and > 850 µm particle size) for developing partial least square regression (PLSR) prediction models for moisture, ash, extractive, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin (ASL + AIL). Overall, smaller particle sizes provided better model performance, while no single particle size provided the best performance for all the selected components. With only 9 selected bands and 4 latent variables (LVs), the best PLSR model was obtained for moisture with particle size of 600–850 µm with the square root of the coefficient of determination (R) of 0.85, the ratio of prediction to deviation (RPD) of 2.2, and the root mean square error (RMSE) of 0.46 % in external validation. Similar model performances were also obtained for ash, extractive, glucan, and xylan. This study showed that size reduction could effectively improve NIR spectroscopic analysis for lipid-producing sorghum biomass for the biofuel industry.

09 BIOMASS FUELS↗
DOE OSTI2021

Size-Dependent Chemomechanical Failure of Sulfide Solid Electrolyte Particles during Electrochemical Reaction with Lithium

The very high ionic conductivity of Li 10 GeP 2 S 12 (LGPS) solid electrolyte (SE) makes it a promising candidate SE for solid-state batteries in electrical vehicles. However, chemomechanical failure, whose mechanism remains unclear, has plagued its widespread applications. Here, we report in situ imaging lithiation-induced failure of LGPS SE. In this work, we revealed a strong size effect in the chemomechanical failure of LGPS particles: namely, when the particle size is greater than 3 μm, fracture/pulverization occurred; when the particle size is between 1 and 3 μm, microcracks emerged; when the particle size is less than 1 μm, no chemomechanical failure was observed. This strong size effect is interpreted by the interplay between elastic energy storage and dissipation. Our finding has important implications for the design of high-performance LGPS SE, for example, by reducing the particle size to less than 1 μm the chemomechanical failure of LGPS SE can be mitigated.

25 ENERGY STORAGE↗
DOE OSTI2024

Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure–property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co 3 O 4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the sizedependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an “onset radius,” which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Size-Dependent Optical Band Gaps in Metal–Organic Framework Nanoparticles

Decades of research into size-dependent semiconductor optical gaps have focused on quantum confinement as the dominant mechanism. Emerging reports indicate that lattice strain─intentional or incidental─can impart optical shifts similar or greater in magnitude. Here, we report evidence of optical absorption and photoluminescence spectra of M(1,2,3-triazolate)2 (M = Mg, Cr, Mn, Fe, Co, Cu, Zn, or Cd) nanoparticles that blueshift from bulk values with decreasing particle sizes in a manner that defies explanation by conventional quantum confinement. Here, the phenomenon persists for particle sizes as large as 200 nm, whereas quantum confinement generally ceases beyond 20–30 nm diameters and follows a weaker dependence on the particle radius. Computational simulations and crystallographic analysis suggest that this behavior arises from size-dependent changes to metal–linker bonding that manifest in strain values comparable to literature reports of strain-induced optical shifts in other classes of materials. This behavior appears beyond this family of materials in other notable examples of metal–organic frameworks (MOFs), including the well-studied Cu3(trimesate)2 (CuBTC), where smaller sizes correlate with blueshifted optical gaps. Taken together, these results represent one of the few examples of size-dependent strain in crystalline materials and reinforce the emerging view that MOFs become softer materials when isolated as nanoparticles.

Electrical conductivity↗
DOE OSTI2023

Unified analysis of finite-size error for periodic Hartree-Fock and second order Møller-Plesset perturbation theory

Despite decades of practice, finite-size errors in many widely used electronic structure theories for periodic systems remain poorly understood. For periodic systems using a general Monkhorst-Pack grid, there has been no comprehensive and rigorous analysis of the finite-size error in the Hartree-Fock theory (HF) and the second order Møller-Plesset perturbation theory (MP2), which are the simplest wavefunction based method, and the simplest post-Hartree-Fock method, respectively. Such calculations can be viewed as a multi-dimensional integral discretized with certain trapezoidal rules. Due to the Coulomb singularity, the integrand has many points of discontinuity in general, and standard error analysis based on the Euler-Maclaurin formula gives overly pessimistic results. The lack of analytic understanding of finite-size errors also impedes the development of effective finite-size correction schemes. We propose a unified analysis to obtain sharp convergence rates of finite-size errors for the periodic HF and MP2 theories. Our main technical advancement is a generalization of the result of Lyness [Math. Comp. 30 (1976), pp. 1–23] for obtaining sharp convergence rates of the trapezoidal rule for a class of non-smooth integrands. Our result is applicable to three-dimensional bulk systems as well as low dimensional systems (such as nanowires and 2D materials). Our unified analysis also allows us to prove the effectiveness of the Madelung-constant correction to the Fock exchange energy, and the effectiveness of a recently proposed staggered mesh method for periodic MP2 calculations (see X. Xing, X. Li, and L. Lin [J. Chem. Theory Comput. 17 (2021), pp. 4733–4745]). In conclusion, our analysis connects the effectiveness of the staggered mesh method with integrands with removable singularities, and suggests a new staggered mesh method for reducing finite-size errors of periodic HF calculations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quantifying dispersity in size and shape of nanoparticles from small-angle scattering data using machine learning based CREASE

Here, we use machine learning (ML) enhanced computational reverse engineering analysis of scattering experiments (CREASE) to interpret small-angle X-ray scattering (SAXS) data obtained from a system of nanoparticles without a priori knowledge of their exact shapes (e.g. spheres or ellipsoids), sizes (0.5–50 nm) and distributions. The SAXS measurements yielded three categories of scattering profiles exhibiting 'strong', 'weak' and 'no' features. Diminishing features (e.g. broadening or disappearing peaks) in scattering profiles have always been attributed to the presence of significant dispersity in the system. Such featureless SAXS data are not suitable for traditional analysis using analytical models. If one were to fit a relevant analytical model (e.g. the lmfit analytical model for polydisperse spheres) to these 'weak' and 'no' SAXS profiles from our nanoparticle systems, one would obtain non-unique interpretations of the data. Relying on electron microscopy to identify the distributions of nanoparticle shapes and sizes is also unfeasible, especially in high-throughput synthesis and characterization loops. In such situations, to identify the distributions of particle sizes and shapes that could be present in the sample, one must rely on methods like ML-CREASE to interpret the data quickly and output all relevant interpretations about the structure present in the system. The ML-CREASE optimization loop takes the experimental scattering profile as input and outputs multiple candidate solutions whose computed scattering profiles match the SAXS profile input. The ML-CREASE method outputs distributions of relevant structural features, such as the volume fraction of the nanoparticles in the system and the mean and standard deviation of the particle size and aspect ratio, assuming a type of distribution (e.g. normal, log-normal) for size and aspect ratio. We find that, for the SAXS profiles analyzed here, accounting for the shape dispersity along with size dispersity of the nanoparticles using ML-CREASE improved the match between the computed scattering profiles and input experimental profiles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Effect of gap size on composition and structure of regeneration 19 years after harvest in a southeastern bottomland forest, USA

Following timber harvests in bottomland mixed-oak (Quercus L.) stands, desirable oak advance regeneration can be overgrown by shade-intolerant pioneer species. We investigated the effects of group selection opening size on composition of tree regeneration 19 years postharvest in an oak-dominated bottomland forest and compared results with earlier findings to evaluate development trends. In response to six opening sizes (7–40 m radii), we evaluated regeneration density and dominance of four tree species groups: conifers, hard mast, shade-intolerant hardwoods, and shade-tolerant hardwoods. Our objectives were to determine the optimum gap size for regenerating oaks and test the delayed oak dominance hypothesis, in which oaks slowly gain dominance as pioneer species undergo self-thinning. Opening size influenced conifer regeneration but minimally affected hardwoods. Hard mast species density was less than that of either intolerant or tolerant species regardless of opening size. Future stem density trends suggest increasing intolerant species and constant mast and tolerant species. Modeled future height trends suggest increasing mast species dominance over intolerant pioneers after 30 years. Furthermore, our results suggest that gap-based silviculture can be used to regenerate bottomland hardwood stands with desirable species including oaks; larger gaps favor conifers but there was no optimum size to enhance oak regeneration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Revisiting the Hail Radar Reflectivity–Kinetic Energy Flux Relation by Combining T-Matrix and Discrete Dipole Approximation Calculations to Size Distribution Observations

The retrieval of hail kinetic energy with weather radars or its simulation in numerical models is challenging because of the shape complexity and variable density of hailstones. We combine 3D scans of individual hailstones with measurements of the particle size distributions (PSD) and T-matrix calculations to understand how hail reflectivity Z changes when approximating hailstones as spheroids, as compared to the realistic shapes obtained by 3D scanning technology. Additionally, recent terminal velocity relations are used to compare Z to the hail kinetic energy flux E ˙ . We parameterize the hail backscattering cross sections at L, S, C, and X bands as a function of size between 0.5 and 5.0 cm, matching the range of the observed PSDs. The scattering calculations use the T-matrix method for size parameters below 1.0 and the discrete dipole approximation (DDA) method otherwise. The DDA calculations are done for 48 digital models of realistic hailstones of sizes between 1 and 5 cm. The DDA cross sections are calculated for multiple orientations and averaged assuming a fully random orientation distribution to provide a single value per hailstone. The T-matrix reflectivity assuming solid ice spheres presents negligible differences to DDA results for size parameters below 1.0. Therefore, T matrix was used to fill in the gaps left by the DDA calculations. The results are mapped to the same size bins of the observed PSDs, allowing the calculation of the radar reflectivity. This is then correlated to E ˙ , allowing a potential improvement of past retrieval methods of E ˙ from Z in multiple wavelengths.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Artificial intelligence models, photos, and data associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” (v2)

This data package is associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” published in Water Resources Research (Chen et al., 2024). This data package includes the training, validation, testing, and prediction data used by the artificial intelligence (AI) model for automated grain size and hydro-biogeochemistry quantification using streambed photos. The grain size data are extracted for each photo using You Look Only Once (YOLO), a pre-trained object detection model. This data package was originally published in October 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. Please see flmd.csv for a list of all files contained in this data package and descriptions for each. Please see dd.csv for a data dictionary that defines the column headers of .csv files in the data package. This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; and (4) six subfolders. Subfolders 1 to 4 include the training, validation, testing, and prediction data. Subfolder 5_Summary includes the summary results of different combinations of training, validation, testing, and prediction data. Subfolder 6_SupplementalData includes additional data downloaded from public sources (Kaufman et al., 2023a; Kaufman et al., 2023b; Garefalakis et al., 2023; Mair et al., 2024; https://github.com/river-corridors-sfa/Geospatial_variables). In total, the data package includes 110 folders and 44,283 files. These files include 9,047 .jpg photos, 1 .png photo, 3 .tif photos; 26,639 photo labels and individual grain sizes and probability from AI (.txt); 8,447 grain size distribution data (.dat); and 126 CSV files for results summary, and 14 required metadata files (.xlsx). The summary CSV files contain 68 columns and approximately 2,200 rows that represent photo names, site locations, recording time, GPS coordinates, grains sizes (D10, D50, D60, and D84), number of grains, and additional hydro-biogeochemical data such as water depth, flow velocity, Manning’s coefficient, friction factor, hydraulic conductivity, permeability, streambed interstitial velocity magnitude, mass transfer rate, and nitrate uptake velocity. The photos were obtained from 75 sites in the Yakima River Basin and the Columbia River shorelines, and other associated data from samples and sensors obtained when the photos were taken are publicly available (Fulton et al. 2022; Grieger et al. 2023). All files are .csv, .txt, .dat, .jpg, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multiple experimental studies of pore structure and mineral grain sizes of the Woodford shale in southern Oklahoma, USA

Pore structure study is an important part of unconventional shale reservoir characterization, since the pore system provides the primary petroleum storage space and fluid flow pathways. Previous studies have suggested that the pore structure is related to the total organic carbon (TOC) content, mineral compositions, and the maturity of the organic matter (OM). However, few studies have focused on the mineral grains, the primary grains being deposited but before cementation, which are the building blocks of shale. Eight Woodford Shale outcrop samples from southern Oklahoma were chosen to study the effects of mineral grain size on the pore structure characterization, using multiple and complementary experimental approaches, including laser diffraction, mineralogy, TOC, pyrolysis, liquid immersion porosimetry, mercury intrusion porosimetry, gas physisorption, (ultra) small angle X-ray scattering, scanning electron microscopy, and spontaneous imbibition. The results from different experiments of eight samples show that the Woodford Shale has the mean mineral grain diameters at 3–6 μm, a wide range of porosity at 3–40% and pore diameters at 50–1,000 nm, and various pore connectivity. Grain size variation was probably caused by the sea-level fluctuation during its deposition, which affect the porosity, pore size distribution, and pore connectivity. With decreasing mineral grain sizes, the porosity tends to increase while the pore connectivity worsens. The results also indicate that OM and carbonates in this low-maturity Woodford Shale could block the pores and decrease the porosity. Coupling with the grain size analyses, the control of depositional environment on grain sizes and subsequent effects on pore structure is identified. The pore structure characteristics over a wide pore-diameter range provided by multiple experiments could improve the understanding of storage space and fluid flow in the Woodford Shale to further increase its petroleum production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Component-Sizing Methodology for a Hybrid Electric Vehicle Using an Optimization Algorithm

Many leading companies in the automotive industry have been putting tremendous effort into developing new powertrains and technologies to make their products more energy efficient. Evaluating the fuel economy benefit of a new technology in specific powertrain systems is straightforward; and, in an early concept phase, obtaining a projection of energy efficiency benefits from new technologies is extremely useful. However, when carmakers consider new technology or powertrain configurations, they must deal with a trade-off problem involving factors such as energy efficiency and performance, because of the complexities of sizing a vehicle’s powertrain components, which directly affect its energy efficiency and dynamic performance. As powertrains of modern vehicles become more complicated, even more effort is required to design the size of each component. This study presents a component-sizing process based on the forward-looking vehicle simulator “Autonomie” and the optimization algorithm “POUNDERS”; the supervisory control strategy based on Pontryagin’s Minimum Principle (PMP) assures sufficient computational system efficiency. We tested the process by applying it to a single power-split hybrid electric vehicle to determine optimal values of gear ratios and each component size, where we defined the optimization problem as minimizing energy consumption when the vehicle’s dynamic performance is given as a performance constraint. The suggested sizing process will be helpful in determining optimal component sizes for vehicle powertrain to maximize fuel efficiency while dynamic performance is satisfied. Indeed, this process does not require the engineer’s intuition or rules based on heuristics required in the rule-based process.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Statistical Tool Size Study for Computer-Controlled Optical Surfacing

Over the past few decades, computer-controlled optical surfacing (CCOS) systems have become more deterministic. A target surface profile can be predictably achieved with a combination of tools of different sizes. However, deciding the optimal set of tool sizes that will achieve the target residual error in the shortest run time is difficult, and no general guidance has been proposed in the literature. In this paper, we present a computer-assisted study on choosing the proper tool size for a given surface error map. First, we propose that the characteristic frequency ratio (CFR) can be used as a general measure of the correction capability of a tool over a surface map. Second, the performance of different CFRs is quantitatively studied with a computer simulation by applying them to guide the tool size selection for polishing a large number of randomly generated surface maps with similar initial spatial frequencies and root mean square errors. Finally, we find that CFR = 0.75 achieves the most stable trade-off between the total run time and the number of iterations and thus can be used as a general criterion in tool size selection for CCOS processes. To the best of our knowledge, the CFR is the first criterion that ties tool size selection to overall efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ability of x‐ray computed tomography to resolve critical flaw size in laser‐based, paste stereolithography ceramic printing of alumina

Abstract Complex alumina parts were printed using vat photopolymerization (VPP), which is a stereolithography‐based additive manufacturing (AM) technique used to shape ceramic preforms, or green parts. The critical flaw size was determined using classical fracture mechanics techniques. The strength and fracture toughness were measured and compared to flaws detected in x‐ray computed tomography (XCT or CT) distributions as well as the fracture surfaces. The strength was lower compared traditionally made alumina, and that is due to layering effects, slurry defects, and printing defects. The critical flaw size from fracture mechanics was 206 µm. XCT has high enough resolution to detect the critical flaw size and much smaller features, where the average flaw size observed in CT scans was around 80–100 µm. The fracture surfaces indicate that flaws causing failure are larger than that of the critical flaw size (∼300 µm), but fracture surfaces do not show definitive features compared to traditionally made ceramics. Since XCT can observe flaws smaller than the critical flaw size, this method can be used as a screening technique.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Relationship between Grain Size and Sample Thickness on the Creep-Rupture Performance of Thin Metallic Sheets of INCONEL Alloy 740H

A study was conducted on INCONEL® alloy 740H® to examine the role of grain size and sheet thickness on the alloy’s creep-rupture behavior. Three different starting sheet thicknesses were utilized and multiple heat-treatment conditions anticipated for compact heat exchanger (CHX) manufacturing were applied to produce a range of grain sizes. Creep-rupture testing was conducted at 750 °C for times up to about 6000 h and the results were compared to wrought databases. The data show that both creep strength and ductility were important factors in the overall creep performance of the sheets. Reductions in performance were observed due either to accelerated creep when grain size was fine or loss of rupture ductility when grain sizes approached the sheet thickness. Some combinations of heat-treatment and thickness were able to produce typical expected wrought creep properties. Historically a ‘rule-of-thumb’ requirement for creep testing suggests 3-5 grains per sample minimum dimension to ensure homogeneous behavior. This research shows that to ensure representative wrought creep performance (i.e. no effect of sample size), the sample minimum cross-section should be 10 times the average ASTM grain size. Statistical analysis of the microstructures suggests the population of larger grains as a controlling feature in creep failure.

14 SOLAR ENERGY↗
DOE OSTI2022

Wet-chemical synthesis of Li7P3S11 with tailored particle size for solid state electrolytes

The small-size solid-state electrodes (SSEs) with high Li+ conductivity play an important role in reducing the interfacial resistance between electrode materials and SSEs and defects between SSEs. As one of the most conductive sulfide based ionic conductors, scalable synthesis of Li7P3S11 with controllable particle sizes is highly desired but rarely reported. Here, we report a facile wet-chemical synthesis method to prepare Li7P3S11 SSEs with variable sizes for all-solid-state lithium batteries (ASSLBs). By fully dissolving 70Li2S-30P2S5 precursors in ethyl acetate solution, the particle size of Li7P3S11 can be manipulated into as small as ~110 nm and high Li+ conductivity of 1.05 mS cm-1 can be achieved. A systematical mechanistic study of solvent effects suggests that the controlled particle sizes are attributed to the excellent solvability and low binding energy of ethyl acetate solvent toward solutes. Moreover, this generic method can be extended to the facilely synthesis of Li3PS4 nanoparticles with an average size of ~200 nm and high Li+ conductivity of 0.1 mS cm-1.

Zhou, Jianbin↗
DOE OSTI2021

Unraveling the implications of finite specimen size on the interpretation of dynamic experiments for polycrystalline aluminum through direct numerical simulations

Normal and Pressure-shear plate impact (NPI and PSPI) experiments are popular experimental techniques for studying the mean-field macroscopic behavior of polycrystalline metals under high-rate dynamic loading. However, since both configurations rely upon geometry for subjecting the specimen to high strain rates, these experiments often involve a limited specimen size. Moreover, because of the inherent heterogeneities present within polycrystalline metals, it is difficult to ascertain if the size of the specimen and/or regions where measurements are made are sufficiently large for making representative inferences about the mean-field macroscopic properties from single-point velocity measurements. In the present study, we quantify the expected measurement variability on observable point measurements in NPI and PSPI experiments by carrying out direct numerical simulations (DNS) of statistically representative polycrystalline microstructures subjected to dynamic compression and compression-shear loading. In particular, we consider the role of specific material heterogeneities (e.g. the grain-to-grain difference in size, crystallographic orientation) on dispersion in the normal and transverse particle velocity records and on local fluctuations in key state variables (e.g. velocity, accumulated plastic strain) by incorporating these effects directly into a representative synthetic microstructure geometry and crystalline description of pure polycrystalline aluminum. The form of the present study is a large parametric investigation, consisting of ten ensembles of one hundred simulations. Each of the thousand simulations reflects a randomly realized synthetic microstructure in one of five cases of decreasing average grain size for the two loading configurations. Our analysis of the DNS results demonstrates that for both of these experimental configurations, the grain size directly correlates with the coefficient of variation (CV) in simulated point measurements, showing a convergent decrease in CV to zero (i.e. particle velocity record approaches the mean-field value) with decreasing grain size. Remarkably, the magnitude of variations in the particle velocity record is shown to be largest where the deviatoric stresses are most significant. In the case of NPI, this occurs at the elastic and plastic wavefront, whereas, in the case of PSPI, the magnitude of fluctuations are approximately constant throughout the experimental window time. The reasoning for the scatter in particle velocity due to the heterogeneous microstructure is demonstrated to be dependent on the mechanisms for accommodating deformation and on the interaction of reflection waves generated at sites of heterogeneities occurring at the scale of grains. Lastly, we develop a power-law description for the magnitude of scattering versus characteristic length, which provides a statistical framework for assessing the required number of grains per characteristic specimen dimension for minimizing scatter within these two experimental configurations (NPI, PSPI).

36 MATERIALS SCIENCE↗