Regions Of High Concentration Research Articles

Plasmon scattering at a junction in double-layer two-dimensional electron gas plasmonic waveguide

Abstract The scattering of plasmons at a junction within a double-layer two-dimensional electron gas plasmonic waveguide is studied via a full electromagnetic method. The dispersion relation is derived by utilizing the transfer matrix method and can be extended to the situation of an arbitrary number of layers. By numerically solving the dispersion equations, both the acoustic and optical plasmon modes are identified in this double layer system, and the unstable plasmon modes arising from plasmon coupling in different layers are discussed elaborately. Subsequently, the total fields are expanded with eigenmodes and matched at the interface to analyze the scattering characteristics at the junction. The results indicate that the total power of the plasmon mode is amplified when the electron fluid flows from a high concentration region to a low concentration region, and the amplification is more evident at a higher drift velocity. Additionally, we address the scattering of unstable plasmons caused by the two-stream instability and find that the transmitted plasmons are excited intensively at the incidence of the growing plasmon, leading to the plasmon amplification. The detailed examination of plasmon scattering at junction is the prerequisite for studying more complex structures of terahertz plasmonic devices and comprehending the corresponding amplification mechanism.

Diffusiophoresis of ionic catalytic particles

A migration of charged particles relative to a solvent, caused by a gradient of salt concentration and termed a diffusiophoresis, is of much interest being exploited in many fields. Existing theories deal with diffusiophoresis of passive inert particles. In this paper, we extend prior models by focusing on a particle, which is both passive and catalytic, by postulating an uniform ion release over its surface. We derive an expression for a particle velocity depending on a dimensionless ion flux (Damköhler number Da) and show that a charged region is formed at distances of the order of the particle size, provided the diffusion coefficients of anions and cations are unequal. When Da becomes large enough, the contribution of this (outer) region to the particle velocity dominates. In this case, the speed of catalytic passive particles augments linearly with Da and is inversely proportional to the square of electrolyte concentration. As a result, they always migrate toward a high concentration region and, in dilute solutions, become much faster than inert (non-catalytic) ones.

Experimental and numerical studies on hydrogen leakage and dispersion in underground parking garages: Impact of leakage direction on safety considerations

Hydrogen leakage in confined spaces poses significant safety risks in hydrogen energy applications, potentially leading to fires and explosions. This paper focuses on the accident scene of hydrogen leakage and dispersion in underground parking garages. A medium-scale model (1/10) of an underground parking garage is designed and built to study the characteristics of the dispersion of hydrogen leaked from hydrogen fuel cell vehicles (HFCVs) in underground garages using experimental and numerical simulation methods. This paper focused on analyzing the dispersion characteristics of hydrogen leaked from hydrogen fuel cell vehicles (HFCVs) within these environments, with particular attention given to the influence of leakage direction—upward, horizontal, and downward—on hydrogen concentration distribution over space and time. For instance, it was observed that under equivalent flow rates, upward leakage tends to result in higher hydrogen concentrations at the leakage site. Conversely, downward leakage promotes wider hydrogen diffusion around the source, particularly evident at higher flow rates. Horizontal leakage, comparatively, exhibits lower risk due to greater air volume absorption. Complementing the experimental data, numerical simulations revealed consistent patterns in hydrogen diffusion. Specifically, the simulations illustrated a cyclic accumulation-diffusion-accumulation process. Notably, vertical upward leakage demonstrated the largest volume of regions exceeding a 4 % hydrogen volume fraction, whereas vertical downward leakage resulted in the greatest volume of regions surpassing an 18.3 % hydrogen volume fraction. This disparity arises from hydrogen accumulation beneath the vehicle in cases of vertical downward leakage. Conversely, regions of high concentration induced by horizontal leakage were the smallest. The study offers valuable insights for the design and construction of HFCVs and parking facilities, providing a theoretical framework for enhancing safety measures.

Design optimization of lightweight automotive seatback through additive manufacturing compression overmolding of metal polymer composites

With the growing demand for enhanced automotive fuel efficiency and environmental sustainability, there is a need for lightweighting automotive components through innovative design and manufacturing processes. This study leverages a combination of numerical iterative design optimization and hybrid additive manufacturing–compression molding (AM-CM) technique for metal polymer composites to lightweight an automotive seatback. The AM-CM process enables robust mechanical interlocking between metals and composites, boasting high stiffness and strength with low overall density. Replacing metallic components with such metal polymer composites allows for comparable mechanical performance while significantly reducing the overall weight. First, the automotive seatback design space is reduced to critical load carrying regions using topology optimization and high stress concentration areas are identified using finite element analysis. Next, a lightweight metal polymer subcomponent is designed for a high stress concentration region. The full seatback frame with spatially heterogeneous material-specific design is then iteratively optimized to enable enhanced stiffness with minimal weight. Overall, the automotive seatback frame designed with location-specific metal, polymer, and metal polymer composite materials weighs 20% less than the metal-only design while exhibiting similar stiffness.

Clustering Methods for the Characterization of Synchrotron Radiation X‐Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque

This study employs computational algorithms to automatically identify and classify features in X‐Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X‐Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X‐Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

A novel synthesis route with large-scale sublattice asymmetry in boron doped graphene on Ni(111)

One of the promising ways to functionalize graphene is incorporation of heteroatoms in carbon sp2 lattice, as it is proven to be an efficient and versatile method for controllably tuning chemistry of graphene. We present unique, contamination-free method for selectively doping graphene with B dopants, which are incorporated in layer from a reservoir created in the bulk of Ni(111) single crystal, during standard CVD growth process, leading to clean, versatile and efficient method for creating B-doped graphene. We combine experimental (STM, XPS) and theoretical (DFT, simulated STM) studies to understand structural and chemical properties of substitutional B dopants. Along with previously reported substitutional B in fcc sites, we have observed, for the first time, two more defects, namely substitutional B in top sites and interstitial B in octahedral subsurface sites. Extensive STM investigations confirm presence of low and high concentration regions of B dopants in as-prepared B-doped graphene, indicating non-uniform boron incorporation. Among two substitutional sites, no preference is observed in low-concentration B-doped regions, whereas in high B concentration regions, one of the sublattices is preferred for incorporation, along with alignment of defects. This generates an asymmetric sublattice doping in as-grown B-doped graphene, which is theoretically predicted to result in notable band gap.

Parameter Optimization in Cluster Identification Algorithms for Characterizing Nanoclusters in Al-Mg-Si-Cu Alloys.

Optimization of user-defined parameters (Dmax, Nmin, order (K)) in the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, used to characterize nanoclusters in Al-0.9% Mg-1.0% Si-0.3% Cu (mass %), was conducted. Ten combinations of parameters with a given K were considered for samples naturally aged (NA) and preaged (PA) at 100°C. We confirmed four types of unphysical clusters, artificially formed, by analyzing composition with size, atomic density, and atomic arrangement inside clusters. The optimum combinations minimizing those unphysical clusters were obtained for both NA and PA samples. Meanwhile, to evaluate the reliability of the optimum combination, volume rendering and isosurfacing were performed. As a result, regions of high solute concentration were confirmed, and those regions are in good agreement with the position of the clusters obtained by applying the optimum combination in DBSCAN. Furthermore, by comparing the optimum combinations with the fixed parameters widely used until now, we showed that for each dataset, considering independent parameters obtained in the same method is desirable rather than using fixed parameters. Consequently, an idea of determining the algorithm parameters for characterizing the nanoclusters in Al-Mg-Si(-Cu) alloys was introduced.

Potassium-mediated bacterial chemotactic response.

Bacteria in biofilms secrete potassium ions to attract free swimming cells. However, the basis of chemotaxis to potassium remains poorly understood. Here, using a microfluidic device, we found that Escherichia coli can rapidly accumulate in regions of high potassium concentration on the order of millimoles. Using a bead assay, we measured the dynamic response of individual flagellar motors to stepwise changes in potassium concentration, finding that the response resulted from the chemotaxis signaling pathway. To characterize the chemotactic response to potassium, we measured the dose-response curve and adaptation kinetics via an Förster resonance energy transfer (FRET) assay, finding that the chemotaxis pathway exhibited a sensitive response and fast adaptation to potassium. We further found that the two major chemoreceptors Tar and Tsr respond differently to potassium. Tar receptors exhibit a biphasic response, whereas Tsr receptors respond to potassium as an attractant. These different responses were consistent with the responses of the two receptors to intracellular pH changes. The sensitive response and fast adaptation allow bacteria to sense and localize small changes in potassium concentration. The differential responses of Tar and Tsr receptors to potassium suggest that cells at different growth stages respond differently to potassium and may have different requirements for potassium.

Potassium-mediated bacterial chemotactic response

Bacteria in biofilms secrete potassium ions to attract free swimming cells. However, the basis of chemotaxis to potassium remains poorly understood. Here, using a microfluidic device, we found that Escherichia coli can rapidly accumulate in regions of high potassium concentration on the order of millimoles. Using a bead assay, we measured the dynamic response of individual flagellar motors to stepwise changes in potassium concentration, finding that the response resulted from the chemotaxis signaling pathway. To characterize the chemotactic response to potassium, we measured the dose–response curve and adaptation kinetics via an Förster resonance energy transfer (FRET) assay, finding that the chemotaxis pathway exhibited a sensitive response and fast adaptation to potassium. We further found that the two major chemoreceptors Tar and Tsr respond differently to potassium. Tar receptors exhibit a biphasic response, whereas Tsr receptors respond to potassium as an attractant. These different responses were consistent with the responses of the two receptors to intracellular pH changes. The sensitive response and fast adaptation allow bacteria to sense and localize small changes in potassium concentration. The differential responses of Tar and Tsr receptors to potassium suggest that cells at different growth stages respond differently to potassium and may have different requirements for potassium.

Dimensionless dynamics: Multipeak and envelope solitons in perturbed nonlinear Schrödinger equation with Kerr law nonlinearity

In this work, the dimensionless form of the improved perturbed nonlinear Schrödinger equation with Kerr law of fiber nonlinearity is solved for distinct exact soliton solutions. We examined the multi-wave solitons and rational solitons of the governing equation using the logarithmic transformation and symbolic computation using an ansatz functions approach. Multi-wave solitons in fluid dynamics describe the situation in which a fluid flow shows several different regions (or peaks) of high concentration or intensity of a particular variable (e.g., velocity, pressure, or vorticity). Multi-wave solitons in turbulent flows might indicate the existence of several coherent structures, like eddies or vortices. These formations are areas of concentrated energy or vorticity in the turbulent flow. Understanding how these peaks interact and change is essential to comprehending the energy cascade and dissipation in turbulent systems. Furthermore, a sub-ordinary differential equation approach is used to create solutions for the Weierstrass elliptic function, periodic function, hyperbolic function, Chirped free, dark-bright (envelope solitons), and rational solitons, as well as the Jacobian elliptic function, periodic function, and rational solitons. Also, as the Jacobian elliptic function's' modulus m approaches values of 1 and 0, we find trigonometric function solutions, solitons-like solutions, and computed chirp free-solitons. Envelope solitons can arise in stratified fluids and spread over the interface between layers, such as layers in the ocean with varying densities. Their research aids in the management and prediction of wave events in artificial and natural fluid settings. In fluids, periodic solitons are persistent, confined wave structures that repeat on a regular basis, retaining their form and velocity over extended distances. These structures occur in a variety of settings, including internal waves in stratified fluids, shallow water waves, and even plasma physics.

Effect of composition gradient on detonation initiation in fuel-rich hydrogen-air mixtures in an obstructed channel

Numerical simulations were performed to study the effect of composition gradient and obstacle arrangement on detonation initiation in fuel-rich hydrogen-air mixtures. A third-order WENO method with adaptive mesh refinement (AMR) was used to solve the unsteady, fully-compressible, reactive Navier-Stokes equations coupled to a calibrated chemical-diffusive model (CDM). An obstructed channel at a blockage ratio of 0.3 filled with non-uniform hydrogen-air mixtures of an average H2 concentration 50 vol% was considered. The results show that unilateral obstacle arrangement is more conducive to flame acceleration (FA) and DDT than bilateral obstacle arrangement for both homogeneous and inhomogeneous mixtures. For inhomogeneous mixtures, the flame accelerates faster, and the detonation onset time is shorter when obstacles are placed on the sidewall with a hydrogen concentration closer to the stoichiometric ratio than the those when obstacles are placed on the other sidewall. However, the placement of unilateral obstacles on either the upper or lower sidewall does not significantly affect the DDT run-up distance. Besides, for fuel-rich inhomogeneous hydrogen-air mixtures, detonation tends to be initiated in the regions of high H2 concentration. Idealized models were introduced to simplify the intricate interactions of the reaction waves and flow fields to better understand the connection between H2 concentration distribution and detonation initiation. The analysis suggests that the minimum shock strength required for detonation initiation decreases with increasing equivalence ratio. This is supported by a kinetics analysis with detailed reaction model, which shows that ignition delay increases with decreasing equivalence ratio when detonation is about to be initiated.

A model with multiple intracranial aneurysms: possible hemodynamic mechanisms of aneurysmal initiation, rupture and recurrence

BackgroundHemodynamic factors play an important role in aneurysm initiation, growth, rupture, and recurrence, while the mechanism of the hemodynamic characteristics is still controversial. A unique model of multiple aneurysms (initiation, growth, rupture, and recurrence) is helpful to avoids the confounders and further explore the possible hemodynamic mechanisms of aneurysm in different states.MethodsWe present a model with multiple aneurysms, and including the states of initiation, growth, rupture, and recurrence, discuss the proposed mechanisms, and describe computational fluid dynamic model that was used to evaluate the likely hemodynamic effect of different states of the aneurysms.ResultsThe hemodynamic analysis suggests that high flow impingement and high WSS distribution at normal parent artery was found before aneurysmal initiation. The WSS distribution and flow velocity were decreased in the new sac after aneurysmal growth. Low WSS was the risk hemodynamic factor for aneurysmal rupture. High flow concentration region on the neck plane after coil embolization still marked in recanalized aneurysm.ConclusionsAssociations have been identified between high flow impingement and aneurysm recanalization, while low WSS is linked to the rupture of aneurysms. High flow concentration and high WSS distribution at normal artery associated with aneurysm initiation and growth, while after growth, the high-risk hemodynamics of aneurysm rupture was occurred, which is low WSS at aneurysm dome.

Experimental confirmation of the formation of collective modes of the magnetization motion of paramagnetic particles in dilute solutions due to spin exchange

Experimental confirmation of the manifestations of new spin exchange paradigm in EPR spectra of 14N nitroxide radical solutions is presented. It was shown that in the region of relatively low concentrations of radicals, the two side components of the spectrum have a mixed shape (the sum of the absorptive line and dispersive line). The dispersion contributions in these two lines have opposite signs. As the concentration of radicals increases, the contribution of dispersion passes through an extremum and in the region of maximum contribution of dispersion, the contribution of absorption to these two lines changes sign. In the region of high concentrations of radicals, when one homogeneously broadened line is practically observed, it turns out that these side components have resonant frequencies that do not coincide with the frequency of the center of gravity of the spectrum.

Application of Deep Learning on Structure Displacement Measurement and Accuracy Analysis

Displacement serves as a crucial indicator in structural health monitoring within civil engineering, with computer vision being a commonly employed tool due to its cost-effectiveness and convenience. This paper introduces a methodology for tracking feature points computed via computer vision techniques SuperPoint and SuperGlue. Two test scenarios are conducted: a bridge loading test characterized by a low uniform stress state, and a tensile strength test marked by a high stress state. However, a notable drawback of this method is the potential misalignment between the survey point (the point under measurement) and the feature point, leading the errors. To address this issue, a fundamental principle for selecting feature points is presented. In the bridge loading test, the displacement of the survey point is recorded, and the influence of misalignment between survey point and feature point on accuracy is analyzed under low-stress uniform conditions. The findings demonstrate the feasibility of this method for field measurements in civil engineering. Subsequently, a tensile strength test is conducted to assess the influence of different feature point selection criteria on accuracy under high-stress conditions. Concurrently, the DIC software Ncorr is applied to analyze the displacement field of feature points near the survey point. The results highlight the significant influence of selecting feature points within regions of high stress concentration on measurement accuracy.

Effect of hydrogen ratio on leakage and explosion characteristics of hydrogen-blended natural gas in utility tunnels

Based on computational fluid dynamics technology, the effect of hydrogen blending ratio (HBR) on the concentration distribution of leaked gas diffusion in utility tunnels was investigated, and based on this, the explosion characteristics of hydrogen-blended natural gas (HBNG) were studied in an inhomogeneous concentration field. The results showed that the concentration field of HBNG gradually stabilized in the downstream of the leak site, and the peak concentration increased linearly with HBR. When HBR≤5%, a high concentration region above the upper explosive limit is formed inside the gas cloud. This characteristic of the distribution induces secondary combustion in utility tunnels. The maximum flame velocity and steady acceleration distance increase with the increase of HBR. The flame shape is significantly affected by the concentration field distribution. The explosion overpressure induced by the real concentration field created multiple peak structures that exacerbated the explosion disaster complexity. The first overpressure peak at the ends was formed by the superposition of the precursor shock wave and its reflected wave, and those at other locations are caused by the shock waves. However, all maximum overpressure peaks are formed by the superposition of multiple reflections of the explosion wave. The maximum peak overpressure at both ends of the tunnel is higher than that in the middle. The explosion overpressure induced by the inhomogeneous gas cloud is lower than that caused by homogeneous gas cloud under the same volume. The research results can provide an effective reference for the anti-explosion design and risk assessment of utility tunnel.

Insight into the Structural and Dynamical Processes of Peptides by Means of Vibrational and Ultrasonic Relaxation Spectroscopies, Molecular Docking, and Density Functional Theory Calculations

We report a detailed investigation of the vibrational modes, structure, and dynamics of glutathione (GSH) solutions using ultrasonic relaxation spectroscopy, FT-IR vibrational spectroscopy, and electronic absorption measurements. The experimental data were analyzed using density functional theory (DFT) and molecular docking calculations. Three distinct Debye-type relaxation processes can be observed in the acoustic spectra, which are assigned to conformational changes between GSH conformers, the self-association of GSH, and protonation processes. The standard volume changes for each process were estimated both experimentally and theoretically, revealing a close resemblance among them. The higher the effect of the relaxation process in the structure, the greater the induced volume changes. From the temperature dependence of specific acoustic parameters, the thermodynamic characteristics of each process were determined. The experimental FT-IR spectra were compared with the corresponding theoretically predicted vibrational spectra, revealing that the GSH dimers and extended conformers dominate the structure of GSH solutions in the high-concentration region. The absorption spectra in the ultraviolet region confirmed the gradual aggregation mechanism that takes place in the aqueous GSH solutions. The results of the present study were discussed and analyzed in the framework of the current phenomenological status of the field.

Electrokinetic ion transport of viscoelastic fluids in a pH-regulated nanochannel

The objects of nanofluidics-based biological detection are usually viscoelastic fluids, and the rheological properties of viscoelastic fluids can have complex effects on electroosmotic flow and electrokinetic ion transport in nanochannels. However, researchers have mainly focused on Newtonian fluids. In this study, numerical simulations were performed on the electrokinetic ion transport of viscoelastic fluids in a pH-regulated nanochannel. The Oldroyd-B model is used to describe the behavior of the polyacrylamide (PAM) solution, and the Poisson-Nernst-Planck model is used to describe the potential and ion concentration in the electrolyte solution. Compared with the Newtonian fluid, at pH = 8, the PAM solution of cp = 500 ppm has a larger region of low ion concentration in front of the nanochannel entrance and has a larger region of high ion concentration behind the nanochannel exit. When the pH is increased to 8, the surface charge density |σw| increases sharply, which is about 5 times of that at pH = 6. At pH = 8, cKCl = 20 mM, and cp = 500 ppm, two distinct vortices appear behind the nanochannel exit, and rotate in opposite directions.

Optimizing collective behavior of communicating active particles with machine learning

Bacteria and other self-propelling microorganisms produce and respond to signaling molecules to communicate with each other (quorum sensing) and to direct their collective behavior. Here, we explore agents (active particles) which communicate with each other to coordinate their collective dynamics for maximizing nutrient consumption. Using reinforcement learning and neural networks, we identify three different strategies: a ‘clustering strategy’, where the agents accumulate in regions of high nutrient concentration; a ‘spreading strategy’, where particles stay away from each other to avoid competing for sparse resources; and an ‘adaptive strategy’, where the agents adaptively decide to either follow or stay away from others. Our work exemplifies the idea that machine learning can be used to determine parameters that are evolutionarily optimized in biological systems but often occur as unknown parameters in mathematical models describing their dynamics.

Study on motion behaviour of coal water slurry particles under vibration conditions

This study uses computational fluid dynamics (CFD) to simulate the motion behavior of coal-water slurry (CWS) particles under vibration conditions. Results show that without vibration, the coal particles settle to the bottom of the container due to gravity, forming distinct regions of low, transition, and high concentration. Under low vibration intensity, the slight energy activates particle motion and promotes settlement. Under high vibration intensity, violent reciprocating motion causes severe shaking and uncertainty, resulting in non-uniform particle distribution and back mixing. Particle velocity distribution is significantly influenced by vibration, with higher frequencies and amplitudes resulting in greater velocities. Additionally, particle pseudo-temperature is higher in the near-wall area due to collisions with particles and wall surfaces. Low frequency and amplitude promote particle settlement, increasing compactness in the bottom area and reducing concentration in the top area. The findings provide valuable insights into CWS behavior under vibration conditions, which can be used to optimize the design and operation of CWS handling and transportation systems.