Particle Population Research Articles

Characterization of extracellular vesicles and synthetic nanoparticles with four orthogonal single-particle analysis platforms.

We compared four orthogonal technologies for sizing, counting, and phenotyping of extracellular vesicles (EVs) and synthetic particles. The platforms were: single‐particle interferometric reflectance imaging sensing (SP‐IRIS) with fluorescence, nanoparticle tracking analysis (NTA) with fluorescence, microfluidic resistive pulse sensing (MRPS), and nanoflow cytometry measurement (NFCM). EVs from the human T lymphocyte line H9 (high CD81, low CD63) and the promonocytic line U937 (low CD81, high CD63) were separated from culture conditioned medium (CCM) by differential ultracentrifugation (dUC) or a combination of ultrafiltration (UF) and size exclusion chromatography (SEC) and characterized by transmission electron microscopy (TEM) and Western blot (WB). Mixtures of synthetic particles (silica and polystyrene spheres) with known sizes and/or concentrations were also tested. MRPS and NFCM returned similar particle counts, while NTA detected counts approximately one order of magnitude lower for EVs, but not for synthetic particles. SP‐IRIS events could not be used to estimate particle concentrations. For sizing, SP‐IRIS, MRPS, and NFCM returned similar size profiles, with smaller sizes predominating (per power law distribution), but with sensitivity typically dropping off below diameters of 60 nm. NTA detected a population of particles with a mode diameter greater than 100 nm. Additionally, SP‐IRIS, MRPS, and NFCM were able to identify at least three of four distinct size populations in a mixture of silica or polystyrene nanoparticles. Finally, for tetraspanin phenotyping, the SP‐IRIS platform in fluorescence mode was able to detect at least two markers on the same particle, while NFCM detected either CD81 or CD63. Based on the results of this study, we can draw conclusions about existing single‐particle analysis capabilities that may be useful for EV biomarker development and mechanistic studies.

The iterative nonlinear contrast source method for simulating ultrasound propagation through a polydisperse microbubble population

Multiple scattering of sound by a population of particles attracts scientific interest for many decades. In contrast-enhanced echography, the simulation of ultrasound propagation through a dense cloud of nonlinearly oscillating microbubbles imposes a numerical challenge. This is particularly the case for polydisperse concentrations in which each scatterer has individual and independent properties. To address this problem, the Iterative Nonlinear Contrast Source (INCS) method has been adapted. Originally, this solved the Westervelt equation by letting its nonlinear term represent a contrast source in a “linearized” medium, and iteratively updating the pressure by computing the 4D spatiotemporal convolution between this source and the Green’s function. Because convolution allows a coarse discretization, INCS is suitable to deal with large-scale problems. In this talk, microbubbles are regarded as nonlinearly responding point sources that act as contrast sources. The total scattered pressure is computed iteratively, as in the original method, but now in each iteration the temporal signature of the contrast sources is calculated by solving every bubble’s own Marmottant equation. Physically, each iteration accounts for an order of multiple scattering. Numerical results will also be presented, demonstrating that INCS can accurately and efficiently simulate ultrasound propagation through a 3D population of polydisperse nonlinear microbubbles.

Antigen-presenting cell-derived extracellular vesicles in accelerating atherosclerosis

Extracellular vesicles (EVs) are a population of heterogeneous particles that originate from the endosomal system or plasma membrane. Antigen-presenting cells (APCs) produce and release a broad spectrum of EVs involved in the pathogenesis of atherosclerosis. APC-derived EVs contain several bioactive molecules, such as non-coding RNAs, cytokines, chemokines, active proteins, immunomodulatory factors, and growth factors. The review focuses on the role of APC-derived EVs in regulating the transformation of macrophage phenotype, shaping foam cells, driving autophagy and/or inhibiting apoptosis of Th4+ cells, T regulatory cells, endothelial and smooth muscle cells (SMCs), as well as in facilitating oxidative stress in vasculature. APC-derived EVs act as triggers of angiogenesis, neovascularization and inflammation through their participation in microvascular inflammation, angiogenesis, development of atherosclerotic plaques, and modulation of their instability.

Nonclassical effects in a nonlinear two trapped-particles system under intrinsic decoherence

Abstract In this paper, we analytically explore the dynamics of a nonlinear two-qubit system derived from a physical model that describes laser-irradiated two trapped particles in the Lamb-Dicke regime under appropriate resonance conditions and the intrinsic decoherence. The dynamics of the particle population inversion, the entanglement between the two trapped-particles and their center-of-mass modes, and the entanglement between the two trapped-particles are investigated under the Lamb-Dicke parameter and the intrinsic decoherence. In is found that, in the absence of decoherence, the generated particle-field and two trapped-particles entanglement can be enhanced by increasing the Lamb-Dicke parameter. The Lamb-Dicke nonlinearity effect on the generated stationary entanglement, the sudden death and sudden birth of the entanglement, and the intrinsic decoherence effect become more pronounced with its large values. For the high Lamb-Dicke non-linearity, the decoherence effect on the generated nonclassical effects can be weakened.

A level set approach to simulate grain growth with an evolving population of second phase particles

In numerous polycrystalline materials, grain size is controlled by second phase particles (SPPs) that hinder the grain boundaries (GBs) by pinning mechanisms. The Smith–Zener pinning (SZP) model describes the physical interaction between SPPs and GBs. Both of them can evolve when applying a heat treatment to the material. As industrial forging processes involve hot deformation steps near the solvus temperature, it is thus of prime importance to characterize the evolution of the SPPs due to their impact on the final microstructure, notably on the grain size. The level set (LS) method is classically used to describe the influence of SPPs on grain growth (GG) by considering the simulated particles as inert and represented by static holes in the used finite element (FE) mesh. A new formalism to model GG mechanism under the influence of the SZP phenomenon, able to take into account evolving particles is proposed. It involves the representation of SPPs by a LS function and a particular numerical treatment around the grain interfaces encountering SPP, making possible the modelling of SPPs evolution without altering the undergoing pinning pressure. Validation and comparison of the new method regarding previous FE-LS formulation in 2D and 3D simulations and an application on GG under the influence of dissolving particles are described.

An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations

We propose an unsupervised machine-learning checkpoint-restart (CR) algorithm for particle-in-cell (PIC) algorithms using Gaussian mixtures (GM). The algorithm compresses the particle population per spatial cell by constructing a velocity distribution function using GM. Particles are reconstructed at restart time by local resampling of the Gaussians. To guarantee fidelity of the CR process, we ensure the exact preservation of invariants such as charge, momentum, and energy for both compression and reconstruction stages, everywhere on the mesh. We also ensure the preservation of Gauss' law after particle reconstruction by exactly matching the density profile at restart time. As a result, the GM CR algorithm is shown to provide a clean, conservative restart capability while potentially affording orders of magnitude savings in input/output requirements. We demonstrate the algorithm using a recently developed exactly energy- and charge-conserving PIC algorithm using both electrostatic and electromagnetic tests. The tests demonstrate not only a high-fidelity CR capability, but also its potential for enhancing the fidelity of the PIC solution for a given particle resolution.

Flavonoid-Decorated Nano-gold for Antimicrobial Therapy Against Gram-negative Bacteria Escherichia coli.

Nano-gold (Aunps) have emerged as promising options that exhibit unique features discrete from traditional materials suited for biomedical applications. Aunps were synthesized using flavonoid quercetin (Q) as reducing agent, and resultant nanoparticles were further conjugated with the flavonoid. The resultant nano-system was expected to perform a dual role as antibacterial and as antioxidant agent. Nano-gold surface plasmon peaks were recorded at 560 nm with size around 62 nm and having slim distribution pattern. Spherical particle with smooth surface was observed under TEM and AFM studies. TEM micrographs confirmed a homogeneous particle population of size around 30 nm. Quercetin association to nano-gold was corroborated through FTIR and EDAX analysis. Antioxidant nature of nano-gold prevented rapid oxidation of brilliant cresyl blue dye, in presence of sodium hypochlorite. Antimicrobial action of QuAunp was tested against Gram-negative bacteria Escherichia coli. Nano-gold designed produced a minimum inhibitory concentration of 7.6 μg/ml and minimum bactericidal concentration 10.5 μg/ml against E. coli. Further TEM analysis and membrane permeability studies revealed the impact of QuAunps on bacterial membrane leading to cell damage.

New insights on polar mesospheric cloud particle size distributions from a two-satellite common volume study

The particle size distribution of Polar Mesospheric Clouds (PMC) is closely related to the fundamental processes of cloud formation and evolution. Still, despite substantial observational efforts, specific details about the particle size distribution have remained obscure. In this study, we aim at deriving more constraints on PMC size distributions by combining optical measurements from two satellite instruments observing a common PMC volume. We use a special set of 2D tomographic limb observations from the Optical Spectrograph and Infrared Imager System (OSIRIS) on the Odin satellite from 2010 to 2011 in the latitude range 78° N to 80° N and compare these to simultaneous PMC observations from the nadir-viewing Cloud Imaging and Particle Size (CIPS) instrument on the AIM satellite. A key goal is to find the assumption on the mathematical shape of the particle size distribution that should be applied to a vertically resolving limb-viewing instrument to reach consistent size results compared to the column-integrated ice distribution as seen by a nadir-viewing instrument. Our results demonstrate that viewing geometry and sampling volume of each instrument must be carefully considered and that the same size distribution assumption cannot simultaneously describe a column-integrated and a local height-resolved size distribution. In particular, applying the standard Gaussian assumption, used by many earlier PMC studies, to both limb and nadir observation leads to an overestimate of particle sizes seen by OSIRIS by about 10 nm as compared to CIPS. We show that the agreement can be improved if a Log-normal assumption with a broad distribution width around σ = 1.42 is adopted for OSIRIS. A reason for this broad distribution best describing the OSIRIS observations we suggest the large retrieval volume of the limb measurement. Gravity waves and other small-scale processes can cause horizontal variations and a co-existence of a wide range of particle populations in the sampling volume. Horizontal integration then leads to apparently much broader size distributions than encountered in a small horizontal sampling volume.

Drift kinetic theory of alpha transport by tokamak perturbations

Upcoming tokamak experiments fuelled with deuterium and tritium are expected to have large alpha particle populations. Such experiments motivate new attention to the theory of alpha particle confinement and transport. A key topic is the interaction of alpha particles with perturbations to the tokamak fields, including those from ripple and magnetohydrodynamic modes like Alfvén eigenmodes. These perturbations can transport alphas, leading to changed localization of alpha heating, loss of alpha power and damage to device walls. Alpha interaction with these perturbations is often studied with single-particle theory. In contrast, we derive a drift kinetic theory to calculate the alpha heat flux resulting from arbitrary perturbation frequency and periodicity (provided these can be studied drift kinetically). Novel features of the theory include the retention of a large effective collision frequency resulting from the resonant alpha collisional boundary layer, correlated interactions over many poloidal transits and finite orbit effects. Heat fluxes are considered for the example cases of ripple and the toroidal Alfvén eigenmode (TAE). The ripple heat flux is small. The TAE heat flux is significant and scales with the square of the perturbation amplitude, allowing the derivation of constraints on mode amplitude for avoidance of significant alpha depletion. A simple saturation condition suggests that TAEs in one upcoming experiment will not cause significant alpha transport via the mechanisms in this theory. However, saturation above the level suggested by the simple condition, but within numerical and experimental experience, which could be accompanied by the onset of stochasticity, could cause significant transport.

Reconciling the Size‐Dependence of Marine Particle Sinking Speed

AbstractSinking particles are critical to the ocean's “biological pump,” sequestering carbon from the atmosphere. Particles' sinking speeds are a primary factor determining fluxes and subsequent ecological and climatic impacts. While size is a key determinant of particles' sinking speeds, observations suggest a variable size‐sinking relationship, affected by other particle properties, resulting in substantial spread in parameterizations of particle sinking and fluxes. We compile particle size‐sinking observations and apply hierarchical Bayesian statistical models to resolve the size‐sinking relationship while accounting for other factors. We find an overall scaling close to the general Navier‐Stokes drag equation, and differences between particle types, open ocean versus coastal/laboratory particles, and in situ versus ex situ methods. These results can help harmonize how Earth system models parameterize particle fluxes and support a weaker size‐dependence than often assumed, with implications for the flux contribution of small particles and the predicted future shrinking of marine particle populations.

Inferred Linear Stability of Parker Solar Probe Observations Using One- and Two-component Proton Distributions

Abstract The hot and diffuse nature of the Sun’s extended atmosphere allows it to persist in non-equilibrium states for long enough that wave–particle instabilities can arise and modify the evolution of the expanding solar wind. Determining which instabilities arise, and how significant a role they play in governing the dynamics of the solar wind, has been a decades-long process involving in situ observations at a variety of radial distances. With new measurements from the Parker Solar Probe (PSP), we can study what wave modes are driven near the Sun, and calculate what instabilities are predicted for different models of the underlying particle populations. We model two hours-long intervals of PSP/SPAN-i measurements of the proton phase-space density during the PSP’s fourth perihelion with the Sun using two commonly used descriptions for the underlying velocity distribution. The linear stability and growth rates associated with the two models are calculated and compared. We find that both selected intervals are susceptible to resonant instabilities, though the growth rates and kinds of modes driven unstable vary depending on whether the protons are modeled using one or two components. In some cases, the predicted growth rates are large enough to compete with other dynamic processes, such as the nonlinear turbulent transfer of energy, in contrast with relatively slower instabilities at larger radial distances from the Sun.

A fully kinetic model for orphan gamma-ray flares in blazars

ABSTRACT Blazars emit a highly variable non-thermal spectrum. It is usually assumed that the same non-thermal electrons are responsible for the IR-optical-UV emission (via synchrotron) and the gamma-ray emission (via inverse Compton). Hence, the light curves in the two bands should be correlated. Orphan gamma-ray flares (i.e. lacking a luminous low-frequency counterpart) challenge our theoretical understanding of blazars. By means of large-scale two-dimensional radiative particle-in-cell simulations, we show that orphan gamma-ray flares may be a self-consistent by-product of particle energization in turbulent magnetically dominated pair plasmas. The energized particles produce the gamma-ray flare by inverse Compton scattering an external radiation field, while the synchrotron luminosity is heavily suppressed since the particles are accelerated nearly along the direction of the local magnetic field. The ratio of inverse Compton to synchrotron luminosity is sensitive to the initial strength of turbulent fluctuations (a larger degree of turbulent fluctuations weakens the anisotropy of the energized particles, thus increasing the synchrotron luminosity). Our results show that the anisotropy of the non-thermal particle population is key to modelling the blazar emission.

Open Markov Chains: Cumulant Dynamics, Fluctuations and Correlations.

In this work we propose a model for open Markov chains that can be interpreted as a system of non-interacting particles evolving according to the rules of a Markov chain. The number of particles in the system is not constant, because we allow the particles to arrive or leave the state space according to prescribed protocols. We describe this system by looking at the population of particles on every state by establishing the rules of time-evolution of the distribution of particles. We show that it is possible to describe the distribution of particles over the state space through the corresponding moment generating function. This description is given through the dynamics ruling the behavior of such a moment generating function and we prove that the system is able to attain the stationarity under some conditions. We also show that it is possible to describe the dynamics of the two first cumulants of the distribution of particles, which in some way is a simpler technique to obtain useful information of the open Markov chain for practical purposes. Finally we also study the behavior of the time-dependent correlation functions of the number of particles present in the system. We give some simple examples of open chains that either, can be fully described through the moment generating function or partially described through the exact solution of the cumulant dynamics.

Characterization of volcanic ash nanoparticles and study of their fate in aqueous medium by asymmetric flow field-flow fractionation-multi-detection.

Dimensional and elemental characterization of environmental nanoparticles is a challenging task that requires the use of a set of complementary analytical methods. Asymmetric flow field-flow fractionation coupled with UV-Vis, multi-angle laser light scattering and ICP-MS detection was applied to study the nanoparticle fraction of a volcanic ash sample, in a Milli-Q water suspension at pH 6.8. It has been shown that the separated by sedimentation nanoparticle fraction of the Klyuchevskoy volcano ash suspension contains 3 polydisperse populations for which size ranges (expressed in gyration radius, rG), hydrodynamic behaviours (evaluated via shape index) and elemental compositions are different. These 3 populations did not dissolve over the 72-h study but aggregated and settled out differently. Thus, the population of particles with gyration radii <140 nm (P1), which contained 6% Al2O3 and represented approximately 20% by mass of the nanoparticle fraction, remained in suspension without observable aggregation. The populations P2 and P3, which represented 67% and 13% by mass in the initial suspension, covered the rG range 25-250 nm and contained 17% and 15% Al2O3, respectively. Over time, populations P2 and P3 aggregated and their concentration in suspension at 72 h decreased by approximately 40% compared with the initial suspension. The decrease of these nanoparticle populations occurred either from the beginning of the temporal monitoring (P2) or after 30 h (P3). Aggregation generated a new population (P4) in suspension with rG up to 300 nm and mostly consisting of P2. This population represented only up to 6 to 7% of the nanoparticle fraction and decreased beyond 50 h. As a result, the trace elements present in the nanoparticle fraction and monitored (Cu and La) were also no longer found in the suspension. The results obtained can offer additional insights into the fate of volcanic ash nanoparticles in the environment.

Development and experimental validation of a texture-based 3D liberation model

Prediction of mineral liberation is one of the key steps in establishing a link between ore texture and its processing behavior. With the rapid development of X-ray Microcomputed Tomography (µCT), the extension of liberation modeling into 3D realms becomes possible. Liberation modeling allows for the generation of particle population from 3D texture data in a completely non-destructive manner. This study presents a novel texture-based 3D liberation model that is capable of predicting liberation from 3D drill core image acquired by µCT. The model takes preferential, phase-boundary, and random breakage into account with differing relative contributions to the liberation depending on the ore texture itself. The model was calibrated using experimental liberation data measured in 3D µCT. After calibration, the liberation model was found to be capable of explaining on average of around 84% of the variance in the experimental liberation data. The generated particle population can be used for particle-based process simulation to evaluate the process responses of various ore textures subjected to various modes of breakage.

MHD-kinetic hybrid code based on structure-preserving finite elements with particles-in-cell

We present a STRUcture-Preserving HYbrid code - STRUPHY - for the simulation of magneto-hydrodynamic (MHD) waves interacting with a population of energetic particles far from thermal equilibrium (kinetic species). The implemented model features linear, ideal MHD equations in curved, three-dimensional space, coupled nonlinearly to the full-orbit Vlasov equations via a current coupling scheme. The algorithm is based on finite element exterior calculus (FEEC) for MHD and particle-in-cell (PIC) methods for the kinetic part; it provably conserves mass, energy, and the divergence-free magnetic field, irrespective of metric (= space curvature), mesh parameters and chosen order of the scheme. These properties enable reliable long-time simulations of energetic particle physics in complex geometries, covering the whole range of MHD waves. In STRUPHY, the finite element spaces are built from tensor products of univariate B-splines on the logical cuboid and can be made high-order by increasing the polynomial degree. Time-stepping is based on a skew-symmetric splitting with implicit sub-steps, mitigating CFL conditions from fast magneto-acoustic waves. High-order time splitting schemes can be used in this regard.

Analysis of complex particle mixtures by asymmetrical flow field-flow fractionation coupled to inductively coupled plasma time-of-flight mass spectrometry

Asymmetrical flow field-flow fractionation (AF4) hyphenated with inductively coupled plasma-mass spectrometry (ICP-MS) has been widely used to characterize metal containing particles. This study demonstrates the advantages of coupling AF4 with ICP-time-of-flight mass spectrometry (ICP-TOFMS) in standard and single particle modes to determine size distribution, elemental composition, and number concentration of composite particles. The coupled system was used to characterize two complex particle mixtures. The first mixture consisted of particles extracted from micro-alloyed steels with two size populations of different elemental composition. The second mixture consisted of particles extracted from soil spiked with various engineered nanoparticles (ENPs). The equivalent hydrodynamic sizes of individual micro-alloyed steel particles were up to 6 times larger than the sizes determined by single particle (sp)-ICP-TOFMS. The larger AF4 sizes were attributed to the presence of a surface coating, which is not reflected in the core size determined by sp-ICP-TOFMS. Two particle populations could not be separated by AF4 due to their broad size distributions but were resolved by sp-ICP-TOFMS using their unique elemental signatures. Multi-angle light scattering and ICP-TOFMS signals of soil suspensions increased with the spiked ENP concentrations. However, only after conducting full element screening and single particle fingerprinting by ICP-TOFMS could this increase be attributed to enhanced extraction efficiency of natural particles and the risk for false conclusions be eliminated. In this study, we describe how AF4 coupled to ICP-TOFMS can be applied to study complex samples of inorganic particles which contain organic compounds.

Tutorial: Langevin Dynamics methods for aerosol particle trajectory simulations and collision rate constant modeling

The Langevin Dynamics (LD) method (also known in the literature as Brownian Dynamics) is routinely used to simulate aerosol particle trajectories for transport rate constant calculations as well as to understand aerosol particle transport in internal and external fluid flows. This tutorial intends to explain the methodological details of setting up a LD simulation of a population of aerosol particles and to deduce rate constants from an ensemble of classical trajectories. We discuss the applicability and limitations of the translational Langevin equation to model the combined stochastic and deterministic motion of particles in fields of force or fluid flow. The drag force and stochastic “diffusion” force terms that appear in the Langevin equation are discussed elaborately, along with a summary of common forces relevant to aerosol systems (electrostatic, gravity, van der Waals, …); a commonly used first order and a fourth order Runge-Kutta time stepping schemes for linear stochastic ordinary differential equations are presented. A MATLAB® implementation of a LD computer code for simulating particle settling under gravity using the first order scheme is included for illustration. Scaling analysis of aerosol transport processes and the selection of timestep and domain size for trajectory simulations are demonstrated through two specific aerosol processes: particle diffusion charging and coagulation. Fortran® implementations of the first order and fourth order time-stepping schemes are included for simulating the 3D motion of a particle in a periodic domain. Potential applications and caveats to the usage of LD are included as a summary.

Pasting, gelatinization, and rheological properties of wheat starch in the presence of sucrose and gluten

The effect of starch/sucrose/gluten interactions on the thermal and rheological behavior of starch–gluten slurries was analyzed. Starch:gluten mixtures were dispersed in water and in a sucrose 50% (w/w) solution. The presence of sucrose significantly modified the pasting profiles of starch. Starch and starch:gluten slurries in sucrose solution showed a continuous increase of viscosity with the temperature rise, in contrast to samples in water, which presented the typical curve shape for starch. A decrease of starch viscosity during pasting was observed with increasing gluten proportion. The onset temperature of starch gelatinization was substantially increased with the addition of sucrose. Gluten addition slightly increased the gelatinization onset temperature in water, but not in sucrose solution. The presence of sucrose caused liquid-like viscoelastic properties of the starch gels, compared to gels made in water. Gluten addition decreased the gel viscoelastic properties in samples dispersed in water and sucrose solution. New particles populations were distinguished in gel samples with gluten. These results are an important basis to understand the effect of flour’s main components on the behavior of sugar-rich batter systems.

Chemical and physical heterogeneity within native gold: implications for the design of gold particle studies

Studies of populations of gold particles are becoming increasingly common; however, interpretation of compositional data may not be straightforward. Natural gold is rarely homogenous. Alloy heterogeneity is present as microfabrics formed either during primary mineralization or by modification of pre-existing alloys by chemical and physical drivers during subsequent residence in either hypogene or surficial environments. In electron-probe-microanalysis (EPMA)-based studies, the combination of Cu, Hg, and Pd values and mineral inclusion suites may be diagnostic for source style of mineralization, but Ag alone is rarely sufficient. Gold characterization studies by laser-ablation-ICP mass spectrometry linked to both quadrupole and Time-of-Flight (ToF-MS) systems show that only Ag, Cu, and Hg form homogenous alloys with Au sufficiently often to act as generic discriminants. Where present, other elements are commonly distributed highly heterogeneously at the micron or submicron scale, either as mineral inclusions or in highly localized, but low concentrations. Drawing upon our own data derived from individual inspection and analyses of approximately 40,000 gold particles from 526 placer and in situ localities worldwide, we show that adequate characterization of gold from a specific locality normally requires study of a minimum of 150 particles via a two-stage approach comprising spatial characterization of compositional heterogeneity, plus crystallographic orientation mapping, that informs subsequent targeted acquisition of quantitative compositional data by EPMA and/or laser-ablation ICP-MS methods. Such data provide the platform to review current understanding of the genesis of gold particle characteristics, elevating future compositional studies from empirical descriptions to process-focused interpretations.