Search NASA⌕ Search

SEARCH · Search NASA

Results for “particle size analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simulations of biomass pyrolysis using glued-sphere CFD-DEM with 3-D intra-particle models

The shape of biomass particles influences intra-particle heat transfer, particle-particle collisions, interphase momentum, heat transfer, and pyrolysis yield. In this research, the effects of these multi-scale phenomena are captured using a glued-sphere computational fluid dynamic discrete element method (CFD-DEM) with 3-D intra-particle models. The glued-spheres resolved the shapes of particles and intra-particle temperature and species distributions. The implementation was validated using the pyrolysis data of a spherical particle and a cylindrical particle. Then, the influences of biomass shapes and sizes on pyrolysis were investigated. The results revealed the significant influence of surface areas and mass distributions on interphase heat transfer and chemical reactions. The intra-particle heat transfer is neglectable for a Biot number smaller than 0.41. Finally, the pyrolysis of irregularly shaped biomass particles in a fixed bed, fluidized bed, and spouted bed was simulated. Analysis of conversion distributions showed the largest deviations in the fixed bed and the slowest conversion rate in the spouted bed. A good balance between uniform and fast conversion is achieved in the fluidized bed. The multi-scale method proposed in this research provides a tool for the evaluation of large irregular-shaped biomass particle pyrolysis in different types of reactors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Comparing Machine Learning and Physics-Based Nanoparticle Geometry Determinations Using Far-Field Spectral Properties

Anisotropic metal nanostructures exhibit polarization-dependent light scattering, a property which has been widely studied and exploited to determine orientations of subwavelength structures using far-field microscopy. Here we explore the use of variational autoencoders (VAEs) to determine the geometries of gold nanorods (NRs) such as in-plane orientation and aspect ratio under linearly polarized dark-field illumination in an optical microscope. We enforce a shared latent space to connect two VAEs trained separately with polarized dark-field scattering spectra and electron microscopy images and achieve image prediction (shape, orientation, and size) of Au NRs using only polarized dark-field scattering spectra. We determine the geometrical parameters of orientational angle and aspect ratio quantitatively via both our dual-VAE and physics-based analysis on the input scattering spectra. We show that orientational angle prediction by dual-VAE performs well with only a small (~300 particle) training set, yielding a mean absolute error (MAE) of 14.4° and a concordance correlation coefficient (CCC) of 0.95. This performance is only marginally worse than the physics-based cos(2?) fitting approach between the scattering intensity and the polarizing angle, which achieves MAE of 8.78° and CCC of 0.99. Aspect ratio determination is also comparable for the dual-VAE and physics-based fitting comparison (MAE of 0.21 vs. 0.23 and CCC of 0.53 vs. 0.68). Here, this dual encoder-decoder architecture effectively exploits the structure-property relationships of plasmonic nanostructures to construct a cross-modal machine learning (ML) approach, providing a pathway to employ ML approaches to address other structure-property relationships in materials science.

Dark-field scattering↗
DOE OSTI2025

A quantitative imaging framework for lithium morphology: Linking deposition uniformity to cycle stability in lithium metal batteries

Characterizing the morphology of lithium (Li) is crucial for developing long-lasting lithium metal batteries. It is well established that more uniform Li deposition correlates with better cell performance. Li morphology is often characterized through qualitative analysis of scanning electron microscopy (SEM) images; however, there are no widely accepted metrics to quantitatively describe deposition uniformity. Here, we propose a framework to quantify uniformity through SEM image analysis via the index of dispersion (ID) metric, which is defined and presented in the context of Li metal batteries. We also explore experimental impacts of sampling protocols onIDmeasurements. Our results demonstrate that theIDmetric is highly sensitive to variations in deposition uniformity, including the coexistence and uniformity of multiple morphologies, uniformity within a single morphology, and particle size distribution uniformity. Furthermore, it is demonstrated that uniformity, as measured by theID, can be related to the average potential of Li||Li symmetric cells over cycling. Higher capacity cycling leads to more pronounced changes in bothIDand average cell potential. Local minima/maxima are found consistently in bothIDand average cell potential immediately before cells short-circuit, which we suggest may indicate a collapse of the microstructure prior to failure. We put forward this framework as a more robust approach to quantify Li deposition uniformity, advancing the development of Li metal batteries that are safer and longer lasting.

Science & Technology - Other Topics↗
DOE OSTI2021

In situ XAS study of the local structure of the nano-Li 2 FeSiO 4 /C cathode

Despite the challenges in achieving its full theoretical capacity of reversible extraction of two Li ions, the Li 2 FeSiO 4 (LFS) cathode shows a remarkable cycling stability once its low electronic conductivity is addressed. By studying the local structure around the iron during electrochemical cycling using in situ x-ray absorption spectroscopy (XAS), it is possible to gain insight into the factors which determine the electrochemical properties of this material. In order to practically perform in situ XAS studies, the charge/discharge of LFS was maximized using two approaches: (a) reducing the particle size of LFS samples from micro-scale to nano-scale in order to reduce the diffusion path for intercalating ions; and (b) applying a conductive coating to each nanoparticle to facilitate electron transfer. A family of LFS materials was synthesized and characterized using x-ray diffraction, and scanning electron microscopy with energy dispersive analysis for structural and morphological analysis, as well as cyclic voltammetry and cycling tests for electrochemical performance diagnosis. This material was then characterized by in situ XAS. The results provide insight into the stable electrochemical performance of LFS and suggest new synthetic routes to reaching the theoretical capacity.

25 ENERGY STORAGE↗
DOE OSTI2026

Unraveling design principles of protein landscapes in photosynthetic membranes in plant chloroplasts

The supramolecular organization of proteins within photosynthetic membranes is crucial for energy conversion in plants. Here, we introduce an analytical and computational pipeline that integrates high-resolution cryo–scanning electron microscopy, biochemical quantification, advanced Monte Carlo computer simulations, and statistical methods to elucidate the elusive protein landscapes of grana membranes in intact Arabidopsis leaves. Our integrated analysis challenges the prevailing view that particles on the exoplasmic fracture faces in freeze-fracture samples represent photosystem II exclusively. Instead, these particles also include cytochrome b 6 f complexes. Furthermore, our steric clash analysis demonstrates that stacked membranes contain a mixture of larger PSII supercomplexes (C 2 S 2 M 2 and C 2 S 2 ) in addition to a smaller complex (C 2 ). This suggests that in vivo PSII supercomplexes exist in an equilibrium distribution of differing sizes. Furthermore, we discovered that, although size exclusion effects govern the global protein arrangement, local packing exhibits orientational order indicative of lateral attractive protein-protein interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular “fingerprints”), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A Study of the Interaction, Morphology, and Structure in Trypsin-Epigallocatechin-3-Gallate Complexes

Understanding the interaction between proteins and polyphenols is of significance to food industries. The aim of this research was to investigate the mode of aggregation for trypsin-EGCG (Epigallocatechin-3-gallate) complexes. For this, the complex was characterized by fluorescence spectroscopy, circular dichroism (CD) spectra, small-angel X-ray scattering (SAXS), and atomic force microscope (AFM) techniques. The results showed that the fluorescence intensity of trypsin-EGCG complexes decreased with increasing the concentration of EGCG, indicating that the interaction between trypsin and EGCG resulted in changes in the microenvironment around fluorescent amino acid residues. The results of CD analysis showed conformational changes in trypsin after binding with EGCG. The results from SAXS analysis showed that the addition of EGCG results in the formation of aggregates of trypsin-EGCG complexes, and increasing the concentration of EGCG resulted in larger aggregates. AFM images showed that the trypsin-EGCG complex formed aggregates of irregular ellipsoidal shapes with the size of about 200 × 400 × 200 nm, with EGCG interconnecting the trypsin particles. Overall, according to these results, it was concluded that the large aggregates of trypsin-EGCG complexes are formed from several small aggregates that are interconnected. The results of this study shed some light on the interaction between digestive enzymes and EGCG.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Correlating inter-particle forces and particle shape to shear-induced aggregation/fragmentation and rheology for dilute anisotropic particle suspensions: A complementary study via capillary rheometry and in-situ small and ultra-small angle X-ray scattering

Predicting the stability of suspensions composed of anisotropic particles is difficult due to the complexity and interplay of hydrodynamic and colloidal forces. For example, it is not well understood how non-spherical particle shapes affect suspension-rheological responses. This study presents evidence showing how the stacking of boehmite platelets dispersed in aqueous solution leads to moderate shear thinning, even at low boehmite mass fractions. Wide angle X-ray, small angle X-ray, and ultra-small angle X-ray scattering were used to simultaneously monitor changes to the size and fractal dimensions of boehmite aggregates from 6 – 10,000 Å as the sample was recirculated through an in situ capillary rheometer. The latter also provided simultaneous measurements of the viscosity of the suspensions. Computational fluid dynamics modeling of the apparatus was used to provide a more rigorous analysis of the fluid flow. In addition to shear-induced aggregation/fragmentation correlated to a complicated balance between hydrodynamic and colloidal forces, the results indicate that orientationally-dependent interactions, arising from non-spherical boehmite aggregate sub-units, may explain the relatively large experimental viscosity when the hydrodynamic force is small compared to colloidal forces. Ultimately, the results from this study can provide a foundation for future work investigating particle shape effects on colloidal and hydrodynamics forces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Nanosized Organo-Silica Particles with “Built-In” Surface-Initiated Atom Transfer Radical Polymerization Capability as a Platform for Brush Particle Synthesis

A facile synthetic method was developed to prepare sub-5 nm organo-silica (oSiO 2 ) nanoparticles through the self-condensation of ATRP-initiator-containing silica precursors. The obtained oSiO 2 nanoparticles were characterized by a combination of nuclear magnetic resonance (NMR), thermogravimetric analysis (TGA), transmission electron microscopy (TEM), dynamic light scattering (DLS) and small-angle neutron scattering (SANS). The accessibility of the surface -Br initiating sites was evaluated by the polymerization of poly(methyl methacrylate) (PMMA) ligands from the surface of the oSiO 2 nanoparticles using surface-initiated atom transfer radical polymerization (SI-ATRP). Here, the ultra-small size, tunable composition and ease of surface modification may render these organosilica nanoparticle systems with built-in SI-ATRP capability an interesting alternative to conventional silica nanoparticles for functional material design.

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

Automated processing of environmental transmission electron microscopy images for quantification of thin film dewetting and carbon nanotube nucleation dynamics

We report scalable production of carbon nanotubes (CNTs) requires catalysts and reaction conditions that provide high nucleation efficiency. In situ characterization methods such as environmental transmission electron microscopy (ETEM) can reveal fundamental mechanisms of synthesis, but to date have primarily provided qualitative observations on small sample sizes. Here, quantitative analysis is performed using high-resolution, high-rate video capture of ETEM experimentation coupled with automated image processing, involving computer vision algorithms and convolutional neural networks. By this approach, we detect distinct nanoparticle formation from an alumina-supported iron thin film and subsequent CNT nucleation from the nanoparticles. The statistical summary of particles in each video shows that, compared to a H 2 -only atmosphere, pretreatment of the catalyst with carbon added to the H 2 atmosphere results in a smaller average particle diameter, a 2-fold increase in particle density (to 5300 particles/μm 2 ), a 3-fold increase in CNT nucleation efficiency (to 92%), and more than a 5-fold increase in CNT density (to 4800/μm 2 ). Addition of carbon during exposure to H 2 is also more effective than NH 3 at dewetting the catalyst film and increasing the CNT nucleation efficiency, in spite of NH 3 being a stronger reducing agent for iron. Insights from this study are applicable to improving CNT yield and productivity in both batch-style and continuous processes.

25 ENERGY STORAGE↗
DOE OSTI2025

Titanium nitride prepared by the urea-glass synthesis gives an active electrocatalyst for the oxygen reduction reaction

Rock-salt-structured titanium nitride (TiN) has emerged as a leading earth-abundant electrocatalyst for the oxygen reduction reaction (ORR). We compare TiN prepared in three ways starting from urea: the urea–glass method, by a direct reaction between urea and titanium(IV) chloride, and through a discrete monomeric complex. In the urea-glass route, a new-found [Ti 4 (µ-O) 6 (OC(NH 2 ) 2 ) 12 ] 4+ oxo-bridged titanium–urea precursor can be synthesized in a single-pot reaction at room temperature starting from titanium(IV) chloride, urea, and ethanol with a urea-to-titanium ratio of 6. Subsequent annealing of the polymeric gel that results at 750 °C in an N 2 atmosphere yields phase-pure, TiN particles on the 100 µm size scale. TiN can be deposited as an ink with PiperlON®anion exchange dispersion onto a glassy carbon rotating disk electrode (RDE), and of the three synthesis methods, the urea-glass method gives the most active ORR catalyst. The onset potential for ORR activity is −131 mV vs. Hg/HgO, and Koutecký–Levich analysis of linear-sweep voltammetry recorded at varying rotation rates supports a two-electron reduction pathway to H 2 O 2 , with a rate constant of 0.0172 cm s −1 . The higher activity is ascribed to a more oxygen-rich surface—boh defect sites and active oxygen species—afforeded by the oxo-bridged precursor, corrorborated by X-ray photoelectron spectroscopy (XPS) analysis.

Xie, Wanrui [University of Michigan, Ann Arbor, MI↗
DOE OSTI2023

Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many models to describe the varying cell sizes and arrangements, without the ability to generalize to other geometries. Here, we develop a geometry-aware autoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline unaware model by over 50% in several metrics such as the Wasserstein distance between the generated and the true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. This proof-of-concept study motivates the design of a foundational model that will be a crucial tool for the study of future detectors, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

A mechanistic study of mesoporous TiO 2 nanoparticle negative electrode materials with varying crystallinity for lithium ion batteries

Nanoscale oxide-based negative electrodes are of great interest for lithium ion batteries due to their high energy density, power density and enhanced safety. In this work, we conducted a case study on mesoporous TiO 2 nanoparticle negative electrodes with uniform size and varying crystallinity in order to investigate the trend in the electrochemical properties of oxide-based nanoscale negative electrodes with varying crystallinity. Furthermore, mesoporous solid spherical TiO 2 nanoparticles with a uniform particle size and varying crystallinity, i.e. , amorphous TiO 2 (A-TiO 2 ), partially crystalline TiO 2 (PC-TiO 2 ) and fully crystalline TiO 2 (FC-TiO 2 ) nanoparticles were studied. At low current rate (quasi steady-state), the specific capacity of the samples drops with the decrease of crystallinity. Ex situ synchrotron pair distribution function analysis reveals that the 1D zigzag Li ion diffusion pathway becomes expanded with the increase of crystallinity, which promotes ion mobility and charge storage. At high current rates (away from equilibrium states), however, the A-TiO 2 sample demonstrates slightly larger capacity than the FC-TiO 2 sample, both of which show larger capacities than that of the PC-TiO 2 sample. Both A-TiO 2 and FC-TiO 2 samples exhibit higher capacitive contribution to the charge storage and larger Li + diffusivity than those of the PC-TiO 2 sample, which explains their better rate capability. Moreover, the larger Li + diffusivity of the A-TiO 2 sample leads to the slightly larger specific capacity than the FC-TiO 2 sample at the highest current rate.

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

Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Long-range transported continental aerosol in the eastern North Atlantic: three multiday event regimes influence cloud condensation nuclei

Abstract. The eastern North Atlantic (ENA) is a region dominated by pristine marine environment and subtropical marine boundary layer clouds. Under unperturbed atmospheric conditions, the regional aerosol regime in the ENA varies seasonally due to different seasonal surface-ocean biogenic emissions, removal processes, and meteorological regimes. However, during periods when the marine boundary layer aerosol in the ENA is impacted by particles transported from continental sources, aerosol properties within the marine boundary layer change significantly, affecting the concentration of cloud condensation nuclei (CCN). Here, we investigate the impact of long-range transported continental aerosol on the regional aerosol regime in the ENA using data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility on Graciosa Island in 2017 during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign. We develop an algorithm that integrates number concentrations of particles with optical particle dry diameter (Dp) between 100 and 1000 nm, single scattering albedo, and black carbon concentration to identify multiday events (with duration >24 consecutive hours) of long-range continental aerosol transport in the ENA. In 2017, we detected nine multiday events of long-range transported particles that correspond to ∼ 7.5 % of the year. For each event, we perform HYSPLIT 10 d backward trajectories analysis, and we evaluate CALIPSO aerosol products to assess, respectively, the origins and compositions of aerosol particles arriving at the ENA site. Subsequently, we group the events into three categories, (1) mixture of dust and marine aerosols, (2) mixture of marine and polluted continental aerosols from industrialized areas, and (3) biomass burning aerosol from North America and Canada, and we evaluate their influence on aerosol population and cloud condensation nuclei in terms of potential activation fraction and concentrations at supersaturation of 0.1 % and 0.2 %. The arrival of plumes dominated by the mixture of dust and marine aerosol in the ENA in the winter caused significant increases in baseline Ntot. Simultaneously, the baseline particle size modes and CCN potential activation fraction remained almost unvaried, while cloud condensation nuclei concentrations increased proportionally to Ntot. Events dominated by a mixture of marine and polluted continental aerosols in spring, fall, and winter led to a statistically significant increase in baseline Ntot, a shift towards larger particular sizes, a higher CCN potential activation fractions, and cloud condensation nuclei concentrations of >170 % and up to 240 % higher than during baseline regime. Finally, the transported aerosol plumes characterized by elevated concentration of biomass burning aerosol from continental wildfires detected in the summertime did not statistically contribute to increase baseline aerosol particle concentrations in the ENA. However, particle diameters were larger than under baseline conditions, and CCN potential activation fractions were >75 % higher. Consequentially, cloud concentration nuclei concentrations increased by ∼ 115 % during the period affected by the biomass burning events. Our results suggest that, through the year, multiday events of long-range continental aerosol transport periodically affect the ENA and represent a significant source of CCN in the marine boundary layer. Based on our analysis, in 2017, the multiday aerosol plume transport dominated by a mixture of dust and marine aerosol, a mixture of marine and polluted continental aerosols, and biomass burning aerosols caused increases in the NCCN baseline regime of, respectively, 6.6 %, 8 %, and 7.4 % at SS 0.1 % (and, respectively, 6.5 %, 8.2 %, and 7.3 % at SS 0.2 %) in the ENA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Colloidal State Machines as Smart Tracers for Chemical Reactor Analysis

A widely utilized tool in reactor analysis is passive tracers that report the residence time distribution, allowing estimation of the conversion and other properties of the system. Recently, advances in microrobotics have introduced powered and functional entities with sizes comparable to some traditional tracers. This has motivated the concept of Smart Tracers that could record the local chemical concentrations, temperature, or other conditions as they progress through reactors. Herein, the design constraints and advantages of Smart Tracers by simulating their operation in a laminar flow reactor model conducting chemical reactions of various orders are analyzed. It is noted that far fewer particles are necessary to completely map even the most complex concentration gradients compared with their conventional counterparts. Design criteria explored herein include sampling frequency, memory storage capacity, and ensemble number necessary to achieve the required accuracy to inform a reactor model. Cases of severe particle diffusion and sensor noise appear to bind the functional upper limit of such probes and require consideration for future design. The results of the study provide a starting framework for applying the new technology of microrobotics to the broad and impactful set of problems classified as chemical reactor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

An Anisotropic Yield and Damage Material Model to Improve the Contact Pressure Analysis in a Biomass Shredding System

Size reduction systems used in biomass processing break biomass into smaller pieces by utilizing the kinetic energy from the sharp rotating blades. Abrasive and/or erosive wear caused by biomass comminution results in blade wear of the sharp edged cutters, deteriorating the process efficiency. Here, this study aims to optimize the blade design and improve the system efficiency by attempting to understand the interactions between the blades and biomass particles. Since real-time monitoring of these interactions is impractical during operation, mechanical simulations offer a viable alternative for investigating the shredding process. Yet, the irregular geometry and complex mechanical properties of biomass—such as the anisotropic nature of woodchips and their nonlinear fracture behavior—pose significant challenges for accurately simulating contact pressure. In this work an anisotropic yield material model, along with a damage initiation and evolution function, is applied to the woodchip particle to study the contact pressure on shredder blade, offering a scientific basis for improved blade design and process efficiency. This approach can be extended to other biomass processing systems with similar anisotropic feedstocks, making it a valuable tool for advancing sustainable biomass utilization.

09 - BIOMASS FUELS↗
DOE OSTI2022

Nanotomographic observation and statistical analysis of overcharging induced cracks in LiCoO 2 single crystalline particles

Particle fracture represents an important mode of battery electrode degradation. Extensive cracking occurs in the layered oxide cathodes of lithium-ion rechargeable batteries when they are charged beyond safe operational voltages. In this paper, we analyze the morphology and distribution of cracks in over 100 overcharged LiCoO 2 (LCO) single-crystalline particles imaged via X-ray nanotomography. Herein, we observe a strong particle size dependence in both nucleation and growth of cracks, with percentage of cracked particles, crack size, and crack surface area per unit volume all increasing significantly with particle volume. Increasing the cutoff voltage above 4.6V is found to trigger much more pronounced crack growth, which we attribute to the large misfit stress caused by the H1-3 to O1 phase transition. This study offers valuable statistical insights into the fracture behavior of layered oxide cathodes and reveals the importance of particle size control on electromechanical degradation of battery electrodes.

25 ENERGY STORAGE↗