Ensemble Of Particles Research Articles

Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements

Abstract. We present a novel methodology to estimate cloud condensation nuclei (CCN) concentrations from spaceborne CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations) lidar measurements. The algorithm utilizes (i) the CALIPSO-derived backscatter and extinction coefficient, depolarization ratio, and aerosol subtype information; (ii) the normalized volume size distributions and refractive indices from the CALIPSO aerosol model; and (iii) the MOPSMAP (modelled optical properties of ensembles of aerosol particles) optical modelling package. For each CALIPSO height bin, we first select the aerosol-type specific size distribution and then adjust it to reproduce the extinction coefficient derived from the CALIPSO retrieval. The scaled size distribution is integrated to estimate the aerosol number concentration, which is then used in the CCN parameterizations to calculate CCN concentrations at different supersaturations. To account for the hygroscopicity of continental and marine aerosols, we use the kappa parameterization and correct the size distributions before the scaling step. The sensitivity of the derived CCN concentrations to variations in the initial size distributions is also examined. It is found that the uncertainty associated with the algorithm can range between a factor of 2 and 3. Our results are comparable to results obtained using the POLIPHON (Polarization Lidar Photometer Networking) method for extinction coefficients larger than 0.05 km−1. An initial application to a case with coincident airborne in situ measurements for independent validation shows promising results and illustrates the potential of CALIPSO for constructing a global height-resolved CCN climatology.

Dual-pulse laser-induced incandescence to quantify carbon nanostructure and related soot particle properties in transient flows – Concept and exploratory study

In this work a dual-pulse laser-induced incandescence method is presented, which is potentially suitable for the in-situ determination of E(m,λUV)/E(m,λNIR) in transient flows. The dual-pulse LII is irradiating a particle ensemble by two subsequent laser pulses of different wavelengths with a time delay long enough to ensure cooling of the particles after the first pulse, but short enough to prevent the particles from moving out of the probe volume. In the experiment, the first pulse is kept at low and constant fluence, whereas the fluence of the second pulse is periodically modulated. If coincidence of the generated two LII signals is repeatedly achieved, E(m,λi)/E(m,λj) can be evaluated. The synchronous temperature determination of the laser-pulse heated particles even enables the quantification of the absolute values of E(m,λi) and E(m,λj). In an exploratory study using model soot particles generated in a spark discharge generator, the applicability of the method is investigated in detail. The nanostructural and optical properties of the model particles, the influence of the first pulse and other boundary conditions are studied comprehensively. Both, E(m,λ) and E(m,λi)/E(m,λj) are in excellent agreement with ex-situ analyses of the nanostructure and data reported in the literature. In the low-fluence regime of the first pulse, nanostructural modifications of the particles can be ruled out. It is finally shown that the dual-pulse LII not only offers a rapid, non-intrusive diagnostic method of averaged E(m,λi)/E(m,λj) and absolute E(m,λ) values as well as primary particle sizes, but also enables the determination of their statistical distributions, which in turn provides inferences about the nano structure and properties such as graphene-like layer lengths distribution and characteristics inferred thereof.

Single-particle characterization of polycyclic aromatic hydrocarbons in background air in northern Europe

Abstract. We investigated the distribution of polycyclic aromatic hydrocarbons (PAHs) on individual ambient aerosol particles at the Swedish western coast in a pristine environment for 10 d in October 2019. The measurements were carried out using new technology with single-particle mass spectrometry (SPMS) that reveals both the inorganic particle composition as well as the particle-bound PAHs (Schade et al., 2019). More than 290 000 particles were characterized; 4412 of them reveal PAH signatures. Most of the PAH-containing particles were internal mixtures of carbonaceous material, secondary nitrate and metals from distant sources in central and eastern Europe. We characterize the aerosol with respect to the inorganic composition, comparable to conventional SPMS, before we discuss the distribution of PAHs within this particle ensemble. Vice versa, we analyze the single-particle PAH spectra for characteristic patterns and discuss the inorganic composition, origin and atmospheric processing of the respective particles. The study period comprised different meteorological situations: clean air conditions with winds from the North Sea/Kattegat and little terrestrial air pollution, long-range transport from eastern Europe and southern Sweden, and transport of aerosols from central Europe over the sea. For all meteorological conditions, PAHs were detected in particles whose inorganic content indicates traffic emissions, such as combinations of soot, iron and calcium as well as in particles with biomass-burning signatures. However, there were variations in their amounts, dependent on the geographic origin. Because of strong mixing, rapid degradation and speciation limits, e.g., for PAHs of the same nominal mass, the application of diagnostic ratios for source apportionment is limited under the conditions of our study. Nevertheless, the combination with the inorganic content and meteorological data provides unique insights into the particles' origin, aging and mixing state. We exemplarily show how the observation of PAH profiles and inorganic secondary components on a single-particle level can open a new door to investigate aerosol aging processes. To our best knowledge, we herewith present the first comprehensive study on the single-particle distribution of PAHs in ambient air as well as the first set of combined data on PAHs and inorganic composition on a single-particle level.

Single-entity Ti3C2Tx MXene electro-oxidation

We quantify the innate electro-oxidation behavior of a single Ti3C2Tx MXene particle by Single-Entity Electrochemistry. MXene undergoes irreversible oxidation at potentials greater than 0.3 V (vs. SCE), with the extent of oxidation dependant on the applied potential. A close correlation is seen with ensembles of drop-cast particles with submonolayer coverage.

Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

We propose a new method for inferring the governing stochastic ordinary differential equations (SODEs) by observing particle ensembles at discrete and sparse time instants, i.e., multiple "snapshots". Particle coordinates at a single time instant, possibly noisy or truncated, are recorded in each snapshot but are unpaired across the snapshots. By training a physics-informed generative model that generates "fake" sample paths, we aim to fit the observed particle ensemble distributions with a curve in the probability measure space, which is induced from the inferred particle dynamics. We employ different metrics to quantify the differences between distributions, e.g., the sliced Wasserstein distances and the adversarial losses in generative adversarial networks (GANs). We refer to this method as generative "ensemble-regression" (GER), in analogy to the classic "point-regression", where we infer the dynamics by performing regression in the Euclidean space. We illustrate the GER by learning the drift and diffusion terms of particle ensembles governed by SODEs with Brownian motions and Levy processes up to 100 dimensions. We also discuss how to treat cases with noisy or truncated observations. Apart from systems consisting of independent particles, we also tackle nonlocal interacting particle systems with unknown interaction potential parameters by constructing a physics-informed loss function. Finally, we investigate scenarios of paired observations and discuss how to reduce the dimensionality in such cases by proving a convergence theorem that provides theoretical support.

Chemical Vapor Deposition of Spherical Amorphous Selenium Mie Resonators for Infrared Meta-Optics.

Applying direct growth and deposition of optical surfaces holds great promise for the advancement of future nanophotonic technologies. Here, we report on a chemical vapor deposition (CVD) technique for depositing amorphous selenium (a-Se) spheres by desorption of selenium from Bi2Se3 and re-adsorption on the substrate. We utilize this process to grow scalable, large area Se spheres on several substrates and characterize their Mie-resonant response in the mid-infrared (MIR) spectral range. We demonstrate size-tunable Mie resonances spanning the 2-16 μm spectral range for single isolated resonators and large area ensembles. We further demonstrate strong absorption dips of up to 90% in ensembles of particles in a broad MIR range. Finally, we show that ultra-high-Q resonances arise in the case where Se Mie-resonators are coupled to low-loss epsilon-near-zero (ENZ) substrates. These findings demonstrate the enabling potential of amorphous Selenium as a versatile and tunable nanophotonic material that may open up avenues for on-chip MIR spectroscopy, chemical sensing, spectral imaging, and large area metasurface fabrication.

Exp: N-body integration using basis function expansions

ABSTRACT We present the N-body simulation techniques implemented in the exp code. exp uses empirically chosen basis functions to expand the potential field of an ensemble of particles. Unlike other basis function expansions, the derived basis functions are adapted to an input mass distribution, enabling accurate expansion of highly non-spherical objects, such as Galactic discs. We measure the force accuracy in three models, one based on a spherical or aspherical halo, one based on an exponential disc, and one based on a bar-based disc model. We find that exp is as accurate as a direct-summation or tree-based calculation, and in some ways is better, while being considerably less computationally intensive. We discuss optimizing the computation of the basis function representation. We also detail numerical improvements for performing orbit integrations, including time-steps.

Вклад механизмов упрочнения при немонотонном изменении свойств в сплаве Cu–0,6Cr–0,1Zr при кручении под высоким давлением

Phase transformations play an important role in the formation of properties in the dispersion-hardened alloys, for example, such as the Cu–Cr–Zr system alloys. It is known that under severe plastic deformation, the diffusion conditions change significantly, which leads to a change in the phase transformation kinetics. In this work, the authors studied the Cu–0.6Cr–0.1Zr alloy in the low concentration solid solution state subjected to high pressure torsion (up to 10 cycles). In this case, due to the solid solution low concentration and the formed ensemble of large particles, the process of solid solution decomposition was excluded at the first stages. The preliminary work on the analysis of such structurally sensitive characteristics as electrical conductivity and lattice parameter made it possible to identify the nonmonotonic nature of a change in the alloying elements concentration in the solid solution during HPT. Nonmonotonicity is related to the significant changes in the characteristics of the second phase particles ensemble under the influence of high voltages. Such significant structural changes are reflected in the nature of the mechanical characteristics change. The authors identified that when increasing the number of HPT revolutions, changes in strength also have a nonmonotonic nature, which corresponds to the nonmonotonic nature of changes in the concentration of alloying elements and electrical conductivity. Various contributions to the Cu–0.6Cr–0.1Zr alloy hardening were analyzed. The analysis identified that the dispersion strengthening contribution plays the main role in the nonmonotonic change in the mechanical characteristics. The calculated data correlate with the obtained experimental results.

Микромагнитное моделирование композитов Fe-=SUB=-3-=/SUB=-O-=SUB=-4-=/SUB=--Fe-=SUB=-3-x-=/SUB=-Ti-=SUB=-x-=/SUB=-O-=SUB=-4-=/SUB=-

Theoretical modeling of the magnetic properties of Fe3O4-Fe3-xTixO4 composites obtained by the sol-gel method with subsequent hydrothermal treatment has been carried out. The magnetization reversal fields and the number of particles in different magnetic states of an ensemble of two-phase particles with an infinitely thin boundary between the phases and a characteristic size varying in the range from 30 to 80 nm were calculated using the "magnetic rectangles" method. Hysteresis characteristics of an ensemble of chemically inhomogeneous magnetostatically interacting particles were obtained, consistent with experimental data.

Active depletion torque between two passive rods.

The active depletion torque experienced by two anisotropic objects in an active bath is a conceptional generalization of the equilibrium entropic torque. Using Brownian dynamics simulations, we compute the active depletion torque suffered by two passive rods immersed in an ensemble of active Brownian particles. Our results demonstrate that the active depletion torque is qualitatively different from its passive counterpart. Interestingly, we find that the active depletion torque can be greatly affected by the external constraint applied on the rotational degree of freedom of the rods, and even the direction may be changed with the orientational constraint, which is in contrast to the equilibrium depletion torque. The main reason for the remarkable features of the active depletion torque is that the active particles can significantly accumulate in the vicinity of the rods due to persistent self-propulsion, which is sensitively dependent on the constraint strength and the rod configurations. Our findings could be relevant for understanding the self-assembly and dynamics of anisotropic macromolecules in living environments.

Feedback Maximum Principle for Ensemble Control of Local Continuity Equations: An Application to Supervised Machine Learning

We consider an optimal control problem for a system of local continuity equations on a space of probability measures. Such systems can be viewed as macroscopic models of ensembles of non-interacting particles or homotypic individuals, representing several different ``populations''. For the stated problem, we propose a necessary optimality condition, which involves feedback controls inherent to the extremal structure, designed via the standard Pontryagin's Maximum Principle conditions. This optimality condition admits a realization as an iterative algorithm for optimal control. As a motivating case, we discuss an application of the derived optimality condition and the consequent numeric method to a problem of supervised machine learning via dynamic systems.

Micromagnetic modeling of Fe-=SUB=-3-=/SUB=-O-=SUB=-4-=/SUB=--Fe-=SUB=-3-x-=/SUB=-Ti-=SUB=-x-=/SUB=-O-=SUB=-4-=/SUB=- composites

Theoretical modeling of the magnetic properties of Fe3O4-Fe3-xTixO4 composites obtained by the sol-gel method with subsequent hydrothermal treatment has been carried out. The magnetization reversal fields and the number of particles in different magnetic states of an ensemble of two-phase particles with an infinitely thin boundary between the phases and a characteristic size varying in the range from 30 to 80 nm were calculated using the "magnetic rectangles" method. Hysteresis characteristics of an ensemble of chemically inhomogeneous magnetostatically interacting particles were obtained, consistent with experimental data. Keywords: Composites, magnetic granulometry, micromagnetism, two-phase particles, magnetostatic interaction.

Effect of pH on the interparticle magnetic interaction in ensembles of barium hexaferrite particles

Effect of pH on the interparticle magnetic interaction in ensembles of barium hexaferrite particles

Sound classification using evolving ensemble models and Particle Swarm Optimization

Automatic sound classification attracts increasing research attention owing to its vast applications, such as robot navigation, environmental sensing, musical instrument classification, medical diagnosis, and surveillance. In this research, we propose an ensemble convolutional bidirectional Long Short-Term Memory (CBiLSTM) network with optimal hyper-parameter selection for undertaking sound classification. We first transform each audio signal into a spectrogram representation using the Short-time Fourier transform (STFT). A Particle Swarm Optimization (PSO) variant is subsequently proposed to optimize the learning rate, weight decay, numbers of filters and hidden units in the convolutional and BiLSTM layers, respectively, in order to extract effective spatial–temporal characteristics from the spectrogram inputs. To tackle the issue of stagnation in optimization, the proposed algorithm incorporates local exploitation using secant and Newton–Raphson methods, promising leader generation using regular and irregular super-ellipse formulae, and three-dimensional spherical search coefficients. Moreover, it takes into account multiple fused elite signals in conjunction with numerical analysis based exploitation to balance between diversification and intensification. A variety of CBiLSTM networks with distinctive optimized settings are devised. An ensemble model is then constructed by incorporating a set of three yielded networks based on a majority voting scheme. Evaluated using several audio data sets, our ensemble CBiLSTM networks outperform those with default and optimal settings identified by other search methods, existing deep architectures and state-of-the-art related studies. In addition to sound classification tasks, the proposed PSO algorithm also outperforms a number of classical and advanced search methods for solving diverse unimodal and multimodal benchmark functions with statistical significance.

Particle capture in a model chaotic flow.

To better understand and optimize the capture of passive scalars (particles, pollutants, greenhouse gases, etc.) in complex geophysical flows, we study capture in the simpler, but still chaotic, time-dependent double-gyre flow model. For a range of model parameters, the domain of the double-gyre flow consists of a chaotic region, characterized by rapid mixing, interspersed with nonmixing islands in which particle trajectories are regular. Capture units placed within the domain remove all particles that cross their perimeters without altering the velocity field. To predict the capture capability of a unit at an arbitrary location, we characterize the trajectories of a uniformly seeded ensemble of particles as chaotic or nonchaotic, and then use them to determine the spatially resolved fraction of time that the flow is chaotic. With this information, we can predict where to best place units for maximum capture. We also examine the time dependence of the capture process, and demonstrate that there can be a trade-off between the amount of material captured and the capture rate.

Anti-function solution of uniaxial anisotropic Stoner–Wohlfarth model

The anti-trigonometric function is used to strictly solve the uniaxial anisotropic Stoner–Wohlfarth (SW) model, which can obtain the relation of the angle α(θ) between the magnetization (the anisotropy field) and the applied magnetic field. Using this analytic solution, the hysteresis loops of uniaxial anisotropic SW particles magnetized in typical directions could be numerically calculated. Then, the hysteresis loops are obtained in randomly distributed SW particle ensembles while ignoring the dipole interaction among them with the analytic solution. Finally, the correctness of the analytic solution is verified by the exact solutions of remanence, switching field, and coercivity from the SW model. The analytic solution provides an important reference for understanding the magnetizing and magnetization reversal processes of magnetic materials.

Convergence acceleration of ensemble Kalman inversion in nonlinear settings

Many data-science problems can be formulated as an inverse problem, where the parameters are estimated by minimizing a proper loss function. When complicated black-box models are involved, derivative-free optimization tools are often needed. The ensemble Kalman filter (EnKF) is a particle-based derivative-free Bayesian algorithm originally designed for data assimilation. Recently, it has been applied to inverse problems for computational efficiency. The resulting algorithm, known as ensemble Kalman inversion (EKI), involves running an ensemble of particles with EnKF update rules so they can converge to a minimizer. In this article, we investigate EKI convergence in general nonlinear settings. To improve convergence speed and stability, we consider applying EKI with non-constant step-sizes and covariance inflation. We prove that EKI can hit critical points with finite steps in non-convex settings. We further prove that EKI converges to the global minimizer polynomially fast if the loss function is strongly convex. We verify the analysis presented with numerical experiments on two inverse problems.

Luminescent Anisotropic Wurtzite InP Nanocrystals.

Indium phosphide (InP) nanocrystals are emerging as an alternative to heavy metal containing nanocrystals for optoelectronic applications but lag behind in terms of synthetic control. Herein, luminescent wurtzite InP nanocrystals with narrow size distribution were synthesized via a cation exchange reaction from hexagonal Cu3P nanocrystals. A comprehensive surface treatment with NOBF4 was performed, which removes excess copper while generating stoichiometric In/P nanocrystals with fluoride surface passivation. The attained InP nanocrystals manifest a highly resolved absorption spectrum with a narrow emission line of 80 meV, and photoluminescence quantum yield of up to 40%. Optical anisotropy measurements on ensemble and single particle bases show the occurrence of polarized transitions directly mirroring the anisotropic wurtzite lattice, as also manifested from modeling of the quantum confined electronic levels. This shows a green synthesis path for achieving wurtzite InP nanocrystals with desired optoelectronic properties including color purity and light polarization with potential for diverse optoelectronic applications.

Influence of the Chromium Content in Low-Alloyed Cu–Cr Alloys on the Structural Changes, Phase Transformations and Properties in Equal-Channel Angular Pressing

The quantitative concentration of alloying elements in low-alloyed copper alloys is an important factor in forming electrical and mechanical characteristics. It is known that severe plastic deformation is accompanied by both a substantial refinement of the structure and changes in the kinetics of phase transformations during the deformation and the post-deformation thermal treatment. This paper presents the results of a comparative analysis of the Cu–0.2Cr and Cu–1.1Cr alloys subjected to equal-channel angular pressing at room temperature. The analysis was performed for the grain structure, solid solution, and second-phase particles using transmission electron microscopy, scanning electron microscopy, X-ray crystal analysis, and the small-angle diffraction method. It was found that the level of structure refinement and mechanical characteristics after equal-channel angular pressing was almost the same for both studied alloys. Post-deformation aging of the Cu–0.2Cr alloy leads to the development of polygonization and re-crystallization within it. The aging of the Cu–1.1Cr alloy shows a better thermal stability than that of the Cu–0.2Cr alloy. In the Cu–1.1Cr alloy, after aging, in comparison with Cu–0.2Cr, a denser-packed ensemble of fine particles with an average size of 54 ± 2 nm is formed. In this case, the average size of fragments is 270 ± 15 nm and the ultimate tensile strength reaches 485 MPa.

Diffusion treatment of quantum mechanics and its consequences

Localized ensemble of free microparticles spreads out as in a frictionless diffusion satisfying the principle of relativity. An ensemble of classical particles in a fluctuating classical scalar field diffuses in a similar way, and this analogy is used to formulate diffusion quantum mechanics (DQM). DQM reproduces quantum mechanics for homogeneous and gravity for inhomogeneous scalar field. Diffusion flux and probability density are related by Fick’s law, diffusion coefficient is constant and invariant. Hamiltonian includes a “thermal” energy, kinetic energies of drift and diffusion flux. The probability density and the action function of drift form a canonical pair and canonical equations for them lead to the Hamilton-Jacobi-Madelung and continuity equations. At canonical transformation to a complex probability amplitude they form a linear Schrödinger equation. DQM explains appearance of quantum statistics, rest energy (“thermal” energy) and gravity (“thermal” diffusion) and leads to a low mass mechanism for composite particles.