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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.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Electronic properties of Ir 3 Li and ultra-nanocrystalline lithium superoxide formation

Current lithium-oxygen (Li-O 2 ) batteries suffer from large charge overpotentials related to electronic resistivity of the insulating lithium peroxide (Li 2 O 2 ) discharge product. One potential solution to this challenge is the stabilization of the lithium superoxide (LiO 2 ) discharge intermediate, which has much higher electronic conductivity compared to Li 2 O 2 . Cathodes based on small iridium (Ir) nanoparticles have been recently used in a LiO 2 battery to successfully stabilize the LiO 2 product, however, the LiO 2 had a short lifetime. In the previous study, researchers found that the LiO 2 was stabilized on Ir 3 Li surfaces which were formed from Ir nanoparticles during battery operation. Little is known about the electronic properties of Ir 3 Li and its role in stabilizing LiO 2 product formation. This work provides the first study of the electronic properties of Ir 3 Li, which was thermally synthesized in bulk prior to implementation on the reduced graphene oxide (rGO) cathode of a Li-O 2 cell. Here, the bulk Ir 3 Li was found to have comparable electrical conductivity to Ir metal, possess metal-like magnetic properties, and has an affinity towards O 2 adsorption. The LiO 2 discharge product formed from the Li-O 2 battery discharge was characterized using Raman spectroscopy, titration, along with a comprehensive transmission electron microscopy (TEM) study. This analysis revealed the formation of ultra-nanocrystalline LiO 2 particles greater than 200 nm. This result was attributed to the use of large micron sized Ir 3 Li particles, which could stabilize larger LiO 2 particles compared to previous cathodes that utilized Ir nanoparticles that partially converted to Ir 3 Li during cycling. These results demonstrate that cathode properties can be modified to stabilize the bulk LiO 2 discharge product, which can be useful for the further development of LiO 2 -based Li-O 2 batteries.

25 ENERGY STORAGE↗
DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A self-supervised workflow for particle picking in cryo-EM

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion , cryoSPARC , cisTEM , SPHIRE and EMAN2 .

2D class averages↗
DOE OSTI2021

Aerosol Composition, Mixing State, and Phase State of Free Tropospheric Particles and Their Role in Ice Cloud Formation

The prediction of ice cloud formation in the atmosphere remains challenging. Free tropospheric aerosols can act as ice nucleating particles (INPs), affecting cloud properties and precipitation. The physicochemical properties of free tropospheric particles are modified upon long-range transport by different atmospheric processes. These modifications affect the ice formation potential of individual particles. We investigated free tropospheric particles collected at the remote Pico Mountain Observatory at 2225m a.s.l. in the North Atlantic Ocean using multimodal microspectroscopy and chemical imaging techniques. We probed their ice nucleation activity using an ice nucleation stage interfaced with an environmental scanning electron microscope. Retroplume analysis, chemical imaging, and micro-spectroscopy analysis indicated that the size-resolved chemical composition, mixing state, and phase state of the articles with similar aging times but different transport patterns were substantially different. Relative humidity-dependent glass transition temperatures estimated from meteorological conditions were consistent with the observed organic component of the particles' phase. More viscous (solid and semi-solid-like) particles are more ice active in deposition mode at temperature ranging from 205-220 K than those less viscous particles. Finally, this study provides a better understanding of the phase and mixing state of long range transported free tropospheric aerosols and their role in ice cloud formation.

54 ENVIRONMENTAL SCIENCES↗
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↗
DOE OSTI2025

Isotopic analysis of Nd nanoparticles using single particle MC-ICP-MS: A comparative study with single particle-ICP-TOF-MS

Single particle - inductively coupled plasma - mass spectrometry (SP-ICP-MS) is a powerful technique for characterization of the elemental and isotopic composition of individual particles. In this work, the capabilities of the newest generation of MC-ICP-MS with acquisition rates down to 50 ms were evaluated for single particle analysis, with a focus on isotopic precision achievable on a single-particle level. Nd (NdVO 4 ) nanoparticles (~120 nm in diameter) were used as case study and were first characterized in terms of mass (respective size) and particle number concentration by SP-ICP-TOF-MS and then by SP-MC-ICP-MS for isotopic precision. For the isotopic ratio measurements, the MC-ICP-MS performance was compared to the ICP-TOF-MS and it was found that the isotope ratio precision was increased (R 2 between 0.98 and 0.99) compared to ICP-TOF-MS (R 2 between 0.88 and 0.97). The accuracy attained on a single particle level, was compared to bulk digestion followed by MC-ICP-MS analysis, and the SP-MC-ICP-MS technique was able to determine the particle population average to be <4 %, percent relative differences for the 142 Nd/ 144 Nd, 143 Nd/ 144 Nd, 145 Nd/ 144 Nd, 146 Nd/ 144 Nd, and 148 Nd/ 144 Nd ratios The detection limit for the SP-MC-ICP-MS approach was also assessed. Here, when utilizing an all Faraday-cup based detection scheme the determined LOD for the measurements was 0.2fg for Nd, per particle. Based on these results, the newest generation of MC-ICP-MS has demonstrated its utility for performing SP measurement, particularly when high precision isotopic determination is warranted.

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

A survey study on arsenic speciation in coal fly ash and insights into the role of coal combustion conditions

Coal fly ashes (CFAs) are the low-density byproducts of the coal combustion process. Improper or uncontrolled CFA disposal poses significant environmental and health concerns due to the potential leaching of toxic heavy metals such as arsenic (As). Previous studies have investigated the content and speciation of As in different CFA samples, yet systematic information on As speciation in CFA with representative coal source and combustion conditions is still missing. Based on a recent survey study on the typical coal sources and combustion conditions across the U.S., this study selected 19 representative CFA samples to systematically investigate As speciation and potential correlations with these parameters. The composition, morphology, mineralogy, and As speciation of these CFA samples were characterized by complementary analytical, microscopic, and spectroscopic techniques. Synchrotron X-ray spectroscopy and microscopy analyses revealed the dominant As oxidation state to be As(V) and with strong associations to Ca, with the exception of 3 samples that had 19–51% As(III), likely due to the use of selective catalytic reduction (SCR) process. Principal component analysis was conducted to identify potential correlations of As concentration and oxidation state with parameters such as major element content, loss on ignition (LOI), average particle size, coal source, and combustion condition. Al 2 O 3 and FeO content were found to capture a majority of the variability. Further, results from this study provide fundamental basis for understanding the correlations between coal source, combustion conditions, CFA characteristics, and As speciation, and providing insights for downstream beneficial utilization or disposal management.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Loss circulation prevention in geothermal drilling by shape memory polymer

Geothermal formations are naturally fractured with large fracture openings and networks. This can often lead to frequent drilling fluid loss events which is a major contributor to the cost and non-productive time in geothermal drilling. Development of smarter technologies and methods to tackle problem lost circulation is vital for geothermal drilling cost reduction required for geothermal energy to be recognized as a competitive alternative energy source. Mitigation of this problem and other drilling problems such as stuck pipe can minimize overall project cost. In this report, a thermoset shape memory polymer that can be activated by formation natural heat was assessed to seal near wellbore fractures in geothermal wells. The performance of the shape memory polymer (SMP) was evaluated using artificial fractures created in aluminum discs and cylindrical granite cores. Rheology and particle size distribution were considered. A novel testing setup was built for this work, which allows testing of sealing efficiency under dynamic conditions at high temperature. Analysis showed that the SMP has efficiently succeeded in forming a strong plug inside the fractures and stopped fluid loss at high sealing pressure. This smart loss circulation material can expand within the fractures to reduces non-drilling time and strengthen the wellbore in high-temperature drilling operations.

58 GEOSCIENCES↗
DOE OSTI2023

Modeling the co-assembly of binary nanoparticles

Abstract In this work, we present a binary assembly model that can predict the co-assembly structure and spatial frequency spectra of monodispersed nanoparticles with two different particle sizes. The approach relies on an iterative algorithm based on geometric constraints, which can simulate the assembly patterns of particles with two distinct diameters, size distributions, and at various mixture ratios on a planar surface. The two-dimensional spatial-frequency spectra of the modeled assembles can be analyzed using fast Fourier transform analysis to examine their frequency content. The simulated co-assembly structures and spectra are compared with assembled nanoparticles fabricated using transfer coating method are in qualitative agreement with the experimental results. The co-assembly model can also be used to predict the peak spatial frequency and the full-width at half-maximum bandwidth, which can lead to the design of the structure spectra by selection of different monodispersed particles. This work can find applications in fabrication of non-periodic nanostructures for functional surfaces, light extraction structures, and broadband nanophotonics.

Mohanty, Saurav (ORCID:0009000302592855)↗
DOE OSTI2024

Analysis of the Surface Morphology and Chemical Composition of Zr-Nb3Sn Alloys with Zr Different Concentrations

The inclusion of zirconium (Zr) in niobium-tin (Nb3Sn) significantly enhances the performance of Nb3Sn radiofrequency cavities in high magnetic fields. This research project is dedicated to characterizing the surface properties and chemical composition of Zr-doped Nb3Sn. Two different concentrations of Zr were used for doping: approximately 0.5% and 24%. Various spectroscopy techniques were employed to analyze how the surface morphology and chemical composition of Nb3Sn change with increasing Zr content. The findings of this study indicate that the grain size decreases as the Zr concentration increases. In samples with 24% Zr, nanometer-sized particles, likely oxides, were observed. X-ray photoelectron spectroscopy (XPS) data revealed that the thickness of niobium oxides (NbOx) decreases with increasing Zr, while the thickness of tin oxides (SnOx) increases. Additionally, grain size distribution analysis showed that the average grain size is around 5300 nm , with a grain area density of about 165 grains/μm .

Sue, Micah↗
DOE OSTI2020

Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest

Atmospheric nanoparticles can significantly influence Earth’s climate if they grow to sizes large enough to nucleate cloud droplets. While the contribution of extremely low volatility vapors to nanoparticle growth has been discussed extensively, the role of semivolatile organics has been largely overlooked. Here we examine the growth and impacts of air pollution nanoparticles from an isolated metropolis amidst the Amazon rainforest. Although extremely low volatility organics are necessary for the initial growth of nanoparticles, model analysis suggests that dynamic gas-particle partitioning of semivolatile oxidation products of anthropogenic and natural hydrocarbons is predominantly responsible for the observed rapid growth to 50 nm. Furthermore, cloud-resolving simulations demonstrate that the grown particles appreciably modify shallow cloud droplet size distribution, suppress precipitation, and enhance the transition to deep clouds. With condensable semivolatile organics typically formed in greater proportion, similar nanoparticle growth and impacts on clouds could likely occur in heavily urbanized forested regions globally.

Zaveri, Rahul A↗
DOE OSTI2020

DATASET: Rapid growth of anthropogenic organic nanoparticles greatly alters cloud lifecycle in the Amazon rainforest

Atmospheric nanoparticles can significantly influence Earth’s climate if they grow to sizes large enough to nucleate cloud droplets. While the contribution of extremely low volatility vapors to nanoparticle growth has been discussed extensively, the role of semivolatile organics has been largely overlooked. Here we examine the growth and impacts of air pollution nanoparticles from an isolated metropolis amidst the Amazon rainforest. Although extremely low volatility organics are necessary for the initial growth of nanoparticles, model analysis suggests that dynamic gas-particle partitioning of semivolatile oxidation products of anthropogenic and natural hydrocarbons is predominantly responsible for the observed rapid growth to 50 nm. Furthermore, cloud-resolving simulations demonstrate that the grown particles appreciably modify shallow cloud droplet size distribution, suppress precipitation, and enhance the transition to deep clouds. With condensable semivolatile organics typically formed in greater proportion, similar nanoparticle growth and impacts on clouds could likely occur in heavily urbanized forested regions globally.

Zaveri, Rahul A↗
DOE OSTI2021

Elucidating the mechanisms of enhanced lignin bioconversion by an alkali sterilization strategy

Biological lignin valorization represents an emerging green approach to upgrade lignin for sustainable and economic biorefineries. However, lignin generally exhibits poor water solubility and inhomogeneous distribution in an aqueous medium, significantly limiting its bioconversion efficiency. Herein, we develop a novel alkali sterilization strategy to effectively enhance the dispersion and fermentation performance of lignin substrates. The colloidal particle size and molecular structure variations of lignin during the sterilization were thoroughly investigated to reveal the mechanisms of enhanced fermentation performance. Results showed that alkali sterilization achieved a completely aseptic effect when mixing lignin medium at an initial pH of 12.7 for 24 h. Dynamic light scattering (DLS) analysis demonstrated that the hydrodynamic volume of colloidal lignin particles decreased by 96.3% by alkali sterilization compared with the conventional thermal sterilization. Moreover, lignin characterizations by nuclear magnetic resonance (NMR) spectroscopy and gel permeation chromatography (GPC) suggested that alkali sterilization modified the lignin molecular structure by generating 50% more hydrophilic carboxyl groups, reducing the weight-average molecular weight (M w ) by 23.0%, and narrowing the molar-mass dispersity (D M ) by 23.8%. The generation of lignin substrates with more uniform distribution and lower molecular weight improved Rhodococcus opacus PD630 cell growth and metabolism. Microbial cell amount, lignin degradation, and lipid production in alkali sterilized medium increased by 309%, 30.3%, and 48.3%, respectively, compared to those in thermally sterilized medium. These results clearly demonstrated that alkali sterilization dramatically improved the lignin bioconversion performance. Furthermore, this work presents a facile and effective sterilization strategy to overcome inhomogeneous lignin distribution in aqueous fermentation media, showing great potentials as a platform technique for promoting biological lignin valorization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Few‐Unit‐Cell MFI Zeolite Synthesized using a Simple Di‐quaternary Ammonium Structure‐Directing Agent

Abstract Synthesis of a pentasil‐type zeolite with ultra‐small few‐unit‐cell crystalline domains, which we call FDP (few‐unit‐cell crystalline domain pentasil), is reported. FDP is made using bis‐1,5(tributyl ammonium) pentamethylene cations as structure directing agent (SDA). This di‐quaternary ammonium SDA combines butyl ammonium, in place of the one commonly used for MFI synthesis, propyl ammonium, and a five‐carbon nitrogen‐connecting chain, in place of the six‐carbon connecting chain SDAs that are known to fit well within the MFI pores. X‐ray diffraction analysis and electron microscopy imaging of FDP indicate ca. 10 nm crystalline domains organized in hierarchical micro‐/meso‐porous aggregates exhibiting mesoscopic order with an aggregate particle size up to ca. 5 μm. Al and Sn can be incorporated into the FDP zeolite framework to produce active and selective methanol‐to‐hydrocarbon and glucose isomerization catalysts, respectively.

Lu, Peng↗
DOE OSTI2021

Few‐Unit‐Cell MFI Zeolite Synthesized using a Simple Di‐quaternary Ammonium Structure‐Directing Agent

Abstract Synthesis of a pentasil‐type zeolite with ultra‐small few‐unit‐cell crystalline domains, which we call FDP (few‐unit‐cell crystalline domain pentasil), is reported. FDP is made using bis‐1,5(tributyl ammonium) pentamethylene cations as structure directing agent (SDA). This di‐quaternary ammonium SDA combines butyl ammonium, in place of the one commonly used for MFI synthesis, propyl ammonium, and a five‐carbon nitrogen‐connecting chain, in place of the six‐carbon connecting chain SDAs that are known to fit well within the MFI pores. X‐ray diffraction analysis and electron microscopy imaging of FDP indicate ca. 10 nm crystalline domains organized in hierarchical micro‐/meso‐porous aggregates exhibiting mesoscopic order with an aggregate particle size up to ca. 5 μm. Al and Sn can be incorporated into the FDP zeolite framework to produce active and selective methanol‐to‐hydrocarbon and glucose isomerization catalysts, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Aggregation-induced phosphorescence sensitization in two heptanuclear and decanuclear gold–silver sandwich clusters

The strategy of aggregation-induced emission enhancement (AIEE) has been proven to be efficient in wide areas and has recently been adopted in the field of metal nanoclusters. Furthermore, the relationship between atomically precise clusters and AIEE is still unclear. Herein, we have successfully obtained two few-atom heterometallic gold–silver hepta-/decanuclear clusters, denoted Au 6 Ag and Au 9 Ag, and determined their structures by X-ray diffraction and mass spectrometry. The nature of the AuI…AgI interactions thereof is demonstrated through energy decomposition analysis to be far-beyond typical closed-shell metal–metal interaction dominated by dispersion interaction. Furthermore, a positive correlation has been established between the particle size of the nanoaggregates and the photoluminescence quantum yield for Au 6 Ag, manifesting AIEE control upon varying the stoichiometric ratio of Au : Ag in atomically-precise clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗