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Results for “Grain aggregates”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Advancing Cross-Sectional Scanning Electron Microscopy of Perovskite Solar Cells

Organic–inorganic perovskites are an emerging class of photovoltaic materials. Despite achieving power conversion efficiencies surpassing 26%, the challenge of perovskite stability including degradation during exposure to operational conditions such as light, heat, humidity, water, oxygen, and electric fields is well known. Related, perovskite instability has limited high-resolution electron imaging and characterization techniques that can be used for understanding degradation mechanisms. Furthermore, we demonstrate perovskite device cross-section preparation using mechanical polishing in a water-free environment with cryogenic Ar ion milling. Scanning electron microscopy was then used in both backscattered electron and secondary electron imaging modes to obtain information about layer structure, grain aggregate structure, and compositional heterogeneity. Monte Carlo CASINO simulations inform optimum beam conditions and image acquisition parameters and the effects of accelerating voltage, dwell times, and frame averaging for practical image acquisition are reported.

14 SOLAR ENERGY↗
DOE OSTI2024

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation

Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated virtual screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers for meaningful downstream applications. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient generation of accurate conformers. Furthermore, we evaluate the chemical and biochemical quality of our generated conformers on multiple downstream applications, including property prediction and large-scale oracle-based protein docking. Overall, CoarsenConf generates more accurate conformer ensembles compared to prior generative models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Elementary Composite Binary and Grain Alignment Locked in Dust Growth

Abstract Planets are known to grow out of a star-encircling disk of the gas and dust inherited from an interstellar cloud; their formation is thought to begin with coagulation of submicron dust grains into aggregates, the first foundational stage of planet formation. However, with nanoscale and submicron solids unobservable directly in the interstellar medium (ISM) and protoplanetary disks, how dust grains grow is unclear, as are the morphology and structure of interstellar grains and the whereabouts and form of “missing iron.” Here we show an elementary composite binary in 3D sub-10 nm detail—and the alignments of its two subunits and nanoinclusions and a population of elongated composite grains locked in a primitive cosmic dust particle—noninvasively uncovered with phase-contrast X-ray nanotomography. The binary comprises a pair of oblate, quasi-spheroidal grains whose alignment and shape meet the astrophysical constraints on polarizing interstellar grains. Each member of the pair contains a high-density core of octahedral nanocrystals whose twin relationship is consistent with the magnetite’s diagnostic property at low temperatures, with a mantle exhibiting nanoscale heterogeneities, rounded edges, and pitted surfaces. This elongated binary evidently formed from an axially aligned collision of the two similar composite grains whose core–mantle structure and density gradients are consistent with interstellar processes and astronomical evidence for differential depletion. Our findings suggest that the ISM is threaded with dust grains containing preferentially oriented iron-rich magnetic nanocrystals that hold answers to astronomical problems from dust evolution, grain alignment, and the structure of magnetic fields to planetesimal growth.

Hu, Z. W. (ORCID:0000000278648830)↗
DOE OSTI2024

A coarse-grained model of clay colloidal aggregation and consolidation with explicit representation of the electrical double layer

The aggregation of clay minerals in liquid water exemplifies colloidal self-assembly in nature. These negatively charged aluminosilicate platelets interact through multiple mechanisms with different sensitivities to particle shape, surface charge, aqueous chemistry, and interparticle distance and exhibit complex aggregation structures. Experiments have difficulty resolving the associated colloidal assemblages at the scale of individual particles. Conversely, all-atom molecular dynamics (MD) simulations provide detailed insight on clay colloidal interaction mechanisms, but they are limited to systems containing a few particles. We develop a new coarse-grained (CG) model capable of representing assemblages of hundreds of clay particles with accuracy approaching that of MD simulations, at a fraction of the computational cost. Our CG model is parameterized based on MD simulations of a pair of smectite clay particles in liquid water. A distinctive feature of our model is that it explicitly represents the electrical double layer (EDL), i.e., the cloud of charge-compensating cations that surrounds the clay particles. Our model captures the simultaneous importance of long-range colloidal interactions (i.e., interactions consistent with simplified analytical models, already included in extant clay CG models) and short-range interactions such as ion correlation and surface and ion hydration effects. The resulting simulations correctly predict, at low solid-water ratios, the existence of ordered arrangements of parallel particles separated by water films with a thickness up to ~10 nm and, at high solid-water ratios, the coexistence of crystalline and osmotic swelling states, in agreement with experimental observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An extended three-field principle to scale-bridge the granular micromechanics of polymer-bonded particulate materials

Observed phenomena of damage, viscoelasticity, and viscoplasticity in polymer-bonded particulate materials (PBPM), like polymer-bonded explosive (PBX), are accommodated in a scale-bridging constitutive model based on granular micromechanics. An extended three-field variational principle with min - max optimization problem is introduced to homogenize the bonded aggregates’ grain-scale contact mechanics. Similarly to prior applications of a three-field principle, volume constraints are invoked to embed microscale-to-macroscale correspondences for kinematic and static volumetric measures, like mean strain and its energy-conjugate stress invariant. Unlike prior applications, our three-field principle realizes a strain-driven model for effective constitutive behavior due to bonded intergranular contact. In conclusion, the resultant micro-sphere integration-based homogenization is demonstrated in comparison to existing publicly-available measurements to capture the extreme tension/compression constitutive asymmetry exhibited by PBPMs (specifically PBX-9501) during material softening.

42 ENGINEERING↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Synthesis and thermodynamics of uranium-incorporated α-Fe 2 O 3 nanoparticles

Hematite nanoparticles were synthesized with U(VI) in circumneutral water through a coprecipitation and hydrothermal treatment process. XRD, TEM, and EXAFS analyses reveal that uranium may aggregate along grain boundaries and occupy Fe sites within hematite. The described synthesis method produces crystalline, single-phase iron oxide nanoparticles absent of surface-bound uranyl complexes. EXAFS data were comparable to spectra from existing studies whose syntheses were more representative of naturally occurring, extended aging processes. Herein this work provides and validates an accelerated method of synthesizing uranium-immobilized iron oxide nanoparticles for further mechanistic studies. High temperature oxide melt solution calorimetry measurements were performed to calculate the thermodynamic stability of uranium-incorporated iron oxide nanoparticles. Increasing uranium content within hematite resulted in more positive formation enthalpies. Standard formation enthalpies of U x Fe 2–2x O 3 were as high as 76.88 ± 2.83 kJ/mol relative to their binary oxides, or -764.04 ± 3.74 kJ/mol relative to their constituent elements, at x = 0.037. Data on the thermodynamic stability of uranium retention pathways may assist in predicting waste uranyl remobilization, as well as in developing more effective methods to retain uranium captured from aqueous environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A re-examination of the mechanism of whiting events: A new role for diatoms in Fayetteville Green Lake (New York, USA)

Whiting events—the episodic precipitation of fine-grained suspended calcium carbonates in the water column—have been documented across a variety of marine and lacustrine environments. Whitings likely are a major source of carbonate muds, a constituent of limestones, and important archives for geochemical proxies of Earth history. While several biological and physical mechanisms have been proposed to explain the onset of these precipitation events, no consensus has been reached thus far. Fayetteville Green Lake (New York, USA) is a meromictic lake that experiences annual whitings. Materials suspended in the water column collected through the whiting season were characterized using scanning electron microscopy and scanning transmission X-ray microscopy. Whitings in Fayetteville Green Lake are initiated in the spring within the top few meters of the water column, by precipitation of fine amorphous calcium carbonate (ACC) phases nucleating on microbial cells, as well as on abundant extracellular polymeric substances (EPS) frequently associated with centric diatoms. Whiting particles found in the summer consist of 5–7 μm calcite grains forming aggregates with diatoms and EPS. Simple calculations demonstrate that calcite particles continuously grow over several days, then sink quickly through the water column. In the late summer, partial calcium carbonate dissolution is observed deeper in the water column. Settling whiting particles, however, reach the bottom of the lake, where they form a major constituent of the sediment, along with diatom frustules. The role of diatoms and associated EPS acting as nucleation surfaces for calcium carbonates is described for the first time here as a potential mechanism participating in whitings at Fayetteville Green Lake. This mechanism may have been largely overlooked in other whiting events in modern and ancient environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Morphological-informed Thermal Property Prediction in the Engineering Domain

Using first-principle, atomic scale methods to predict thermal carrier (electron and phonon) behavior at nanoscale yields highly resolved thermal properties. Density functional theory simulations can model myriad effects on the transport of phonons and electrons (e.g., phonon and electron coupling, electron correlation, defect presence, carrier scattering), and yield thermal properties for small geometric domains such as thermal and electrical conductivity, or heat capacity. While these thermal properties are accurate in atomistic and nanoscopic systems, they must be scaled up to the microstructural domain to be a useful predictor for experimental basis, as the microscale is where myriad changes and physical phenomenon occur within a material (e.g., grain boundaries, precipitate aggregations, interfaces, defect clusters). My talk will discuss the necessity of thermal property predictions for nuclear fuels applications and more. Additionally I will discuss the necessity in developing multi-physics, multi-scale methods for future applications in predictions for not only nuclear fuels, but other materials applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Light scattering study of highly absorptive, non-fractal, hematite aggregates

In this paper, we present measurements of the scattered light intensity by aerosolized hematite aggregate particles. The measurements were made at a wavelength of 532 nm in the scattering angle range from 0.32° to 157°. Hematite has high values of the real and imaginary parts of the refractive index m = n + iκ = 3 + i0.5 at the studied wavelength. Scanning electron micrographs (SEM) indicated that the particles were aggregates whereas the optical microscope pictures showed that the aerosol had a bimodal distribution with effective mean diameters of roughly 1 and 10 µm. This is consistent with the light scattering results which displayed two Guinier regimes. The aggregates were composed of smaller grains with an approximate size of 200 nm. Ultra Small-Angle X-ray Scattering (USAXS) indicate that the aggregates were uniform and non-fractal. Mie calculations for a sphere equivalent to the aggregate size were compared to the experimentally observed results. The observed results showed an enhanced backscattering, whereas the Mie calculations did not due to the large imaginary part of the refractive index. Hematite aggregates were simulated by assuming they were composed of spherical monomers inside a spherical volume. Then the light scattering was calculated using the T-matrix method for these simulated aggregates. The calculated results show an enhanced backscattering. We present a dimensional analysis to estimate the extent of multiple scattering within the aggregate and find a correlation between the average number of scattering events within the aggregate and the enhancement in the backscattering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Mesoscale-architecture-based crack evolution dictating cycling stability of advanced lithium ion batteries

The cracking phenomenon of Ni-rich NMC (LiNixMnyCo1-x-yO2, x = 0.6) secondary particles is frequently discovered and believed to be one of critical reasons deteriorating the long-term cycling stability of NMC cathode in lithium ion batteries (LIBs). However, the initiation and evolution of those cracks is still controversial due to the limited quantification especially by in situ monitoring, leading to the challenge of identifying an efficient approach to inhibit the formation of the fractures during repeated cycling. Herein, the irreversible, anisotropic cycling lattice and mesoscale expansion/shrinkage of nano-grain during the first cycle, as revealed by in situ X-ray diffraction (XRD) and in situ atomic force microscopy (AFM), have been quantified and confirmed to be the dominant driving forces of microcracks initiation at the grain boundaries. These microcracks preferentially nucleates at the core region with random oriented nano-grains in early stage. The further growth and aggregation of microcracks into macrocrack eventually results in microfracture propagation radially outward to the periphery region with more uniform nano-grain orientation. This mesoscale nano-grain architecture controlled cracking process highlight the importance of predictive synthesis of cathode materials with controllable multiscale crystalline architecture for high-performance LIBs.

25 ENERGY STORAGE↗
DOE OSTI2022

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗
DOE OSTI2022

Enabling full-scale grain boundary mitigation in polycrystalline perovskite solids

There exists a considerable density of interaggregate grain boundaries (GBs) and intra-aggregate GBs in polycrystalline perovskites. Mitigation of intra-aggregate GBs is equally notable to that of interaggregate GBs as intra-aggregate GBs can also cause detrimental effects on the photovoltaic performances of perovskite solar cells (PSCs). Here, we demonstrate full-scale GB mitigation ranging from nanoscale intra-aggregate to submicron-scale interaggregate GBs, by modulating the crystallization kinetics using a judiciously designed brominated arylamine trimer. The optimized GB-mitigated perovskite films exhibit reduced nonradiative recombination, and their corresponding mesostructured PSCs show substantially enhanced device efficiency and long-term stability under illumination, humidity, or heat stress. The versatility of our strategy is also verified upon applying it to different categories of PSCs. Our discovery not only specifies a rarely addressed perspective concerning fundamental studies of perovskites at nanoscale but also opens a route to obtain high-quality solution-processed polycrystalline perovskites for high-performance optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.

Tabassum, Anika↗