Particle Diffusion Research Articles

A Numerical Study of the Effects of a Corotating Interaction Region on Cosmic-Ray Transport. II. Features of Cosmic-Ray Composition and Rigidity

We continue the numerical modeling of a corotating interaction region (CIR) and the effects it has on solar-rotational recurrent variations of galactic cosmic rays (GCRs). A magnetohydrodynamic model is adapted to simulate the background solar wind plasma with a CIR structure in the inner heliosphere, which is incorporated into a comprehensive Parker-type transport model. The focus is on the simulation of the effects of a CIR on GCR protons and the two helium isotopes as a function of heliolongitude. This is to establish whether the difference in composition affects how they are modulated by the CIR in terms of their distribution in longitude. It is demonstrated that particle diffusion and drift influence the effects of the CIR with increasing rigidity from 100 MV up to 15 GV. It is found that protons and helium isotopes are modulated differently with longitude by the CIR and that particle drift influences the modulation effects in longitude. These differences dissipate with increasing rigidity. The final results are focused on the simulated amplitude of these GCR flux variations as a function of rigidity. The amplitude displays a power-law behavior above ∼1 GV with an index similar to the power index of the rigidity dependence of the assumed diffusion coefficients. The simulations further show that below this rigidity, the amplitude at first flattens off, displaying a plateau-like profile, but it then increases systematically with decreasing rigidity below ∼0.3 GV. Again, a power-law behavior is displayed, but it is completely different from that above 1 GV.

Emergence of long-range attractive interactions between colloidal particles during curing of dispersion media.

Compared to traditional drying methods, curing processes result in lower emissions of volatile organic compounds and reduced energy consumption. Consequently, curing processes are widely used in the manufacturing of various polymer products, including inks, coatings, composites, and adhesives. In recent years, the functionality of these polymer products has been enhanced by incorporating colloids with antiviral and conductive properties. Despite the significant influence of colloidal arrangement on physical properties, the kinetics of colloidal particles during curing remains poorly understood. To explore the effective interactions between colloidal particles during curing, we investigated the dynamics of soft particles within dispersing media using molecular dynamics simulations. The likelihood of particle aggregation during curing significantly surpasses that of random contact. Our calculations reveal an effective potential, indicating a long-range attractive interaction between the large particles. This attraction results from the heterogeneity in the crosslink network of the dispersing media, distinguishing it from the classical depletion force. Since the structure of particle aggregation could be controlled by the interplay between curing rate and particle diffusion, our findings offer promising prospects for advancing the manufacturing processes of increasingly functional polymeric products.

Efficiency of Turbulent Reacceleration by Solenoidal Turbulence and Its Application to the Origin of Radio Megahalos in Cluster Outskirts

Recent radio observations with the Low Frequency Array (LOFAR) discovered diffuse emission extending beyond the scale of classical radio halos. The presence of such megahalos indicates that the amplification of the magnetic field and acceleration of relativistic particles are working in the cluster outskirts, presumably due to the combination of shocks and turbulence that dissipate energy in these regions. Cosmological magnetohydrodynamical (MHD) simulations of galaxy clusters suggest that solenoidal turbulence has a significant energy budget in the outskirts of galaxy clusters. In this paper, we explore the possibility that this turbulence contributes to the emission observed in megahalos through second-order Fermi acceleration of relativistic particles and magnetic field amplification by the dynamo. We focus on the case of A2255 and find that this scenario can explain the basic properties of the diffuse emission component that is observed under assumptions that are used in previous literature. More specifically, we conduct a numerical follow-up, solving the Fokker–Planck equation by using a snapshot of an MHD simulation and deducing the synchrotron brightness integrated along the lines of sight. We find that a volume-filling emission, ranging between 30% and almost 100% of the projected area, depending on our assumptions on the particle diffusion and transport, can be detected at LOFAR sensitivities. Assuming a magnetic field B ∼ 0.2 μG, as derived from a dynamo model applied to the emitting region, we find that the observed brightness can be matched when ∼1% of the solenoidal turbulent energy flux is channeled into particle acceleration.

Navigational bottlenecks in nonconservative diffusion dynamics on networks

<p>Diffusion is a ubiquitous process in real-world syetems. In many complex systems, ranging from neuronal networks to traffic in cities, diffusion is nonconservative (NC) in the sense that diffusive particles can be created/annihilated at the entities of the system. Here, we consider the important problem of identifying potential navigational bottlenecks in NC diffusion occurring in the networks representing skeletons of complex systems. We develop a first-principles approach based on an NC diffusion using the Lerman-Ghosh Laplacian on graphs. By solving analytically this NC diffusion equation at two different times, we get an index which characterizes the capacity of every vertex in a network to spread the diffusive particles across the network in a short time. Vertices having such capacity diminished are potential navigational bottlenecks in this kind of dynamics. We solve analytically the situations in which the vertices with the highest degree (hubs) are at different distances in the network, allowing us to understand the structural significance of the index. Using algebraic methods, we derive a Euclidean distance between vertices in the context of NC diffusion with potential navigational bottlenecks. We then apply these indices to study several real-world networks. First, we confronted our theoretical results with experimental data about traffic congestion in a city. Then, we illustrated the application of the new methodologies to the study of a neuronal system, an air transportation network and two urban street networks.</p>

Particle dispersion through porous media with heterogeneous attractions.

Porous media used in many practical applications contain natural spatial variations in composition and surface charge that lead to heterogeneous physicochemical attractions between the media and transported particles. We performed Stokesian dynamics (SD) simulations to examine the effects of heterogeneous attractions on quiescent diffusion and hydrodynamic dispersion of particles within geometrically ordered arrays of nanoposts. We find that transport under quiescent conditions occurs by two mechanisms, diffusion through the void space and intermittent hopping between the attractive wells of different nanoposts. As the attraction heterogeneity increases, the latter mechanism becomes dominant, resulting in an increase in the particle trajectory tortuosity, deviations from Gaussian behavior in the particle displacement distributions, and a decrease in the long-time particle diffusivity. Similarly, under flow conditions corresponding to low Péclet number (Pe), increased attraction heterogeneity leads to transient localization near the nanoposts, resulting in a broadening of the particle distribution and enhanced longitudinal dispersion in the direction of flow. At high Pe where advection strongly dominates, however, the longitudinal dispersion coefficient is insensitive to attraction heterogeneity and exhibits Taylor-Aris dispersion behavior. Our findings provide insight into how heterogeneous interactions may influence particle transport in complex 3-D porous media.

A conjugated equation of transport and diffusion of aerosol particles in the atmosphere considering the capture of particles by vegetation elements

A mathematical model and a numerical algorithm to study the process of transfer and diffusion of harmful substances are discussed in this article. The proposed model considers the capture of particles by plant elements, which plays a significant role in the dynamics of the process under study. The proposed mathematical model monitors and forecasts the environmental conditions of industrial areas, and helps take measures to protect them from the negative impact of anthropogenous factor. To solve the problem posed, a numerical algorithm based on an implicit finite- difference scheme was developed with a high-order accuracy; it makes it possible to conduct computational experiments on a computer.

Analysis of viral aerosol distribution characteristics in typical body positions of patients under local exhaust air

In this paper, CFD numerical simulation and experiment were used to simulate the diffusion process of human exhaled aerosol particles, and the influence of human lying down, sitting and standing posture on the diffusion of aerosol particles under local exhaust air conditions was studied. The results show that different body positions have significant effects on local exhaust air effect. Only 13.2% of the aerosol particles could be removed by local exhaust air in the lying state. The exclusion rate in sitting posture was 25.4%; The exclusion rate for standing posture was 20.6%. Local exhaust air alone is not enough to prevent infection. It is recommended to adopt the combination of mixed ventilation and local exhaust air, which may have a better effect on preventing cross infection, indoor particulate matter transmission and controlling pollution.

Collective dynamics of active dumbbells near a circular obstacle.

In this article, we present the collective dynamics of active dumbbells in the presence of a static circular obstacle using Brownian dynamics simulation. The active dumbbells aggregate on the surface of a circular obstacle beyond a critical radius. The aggregation is non-uniform along the circumference, and the aggregate size increases with the activity (Pe) and the curvature radius (Ro). The dense aggregate of active dumbbells displays persistent rotational motion with a certain angular speed, which linearly increases with activity. Furthermore, we show a strong polar ordering of the active dumbbells within the aggregate. The polar ordering exhibits long-range correlation, with the correlation length corresponding to the aggregate size. Additionally, we show that the residence time of an active dumbbell on the obstacle surface increases rapidly with area fraction due to many-body interactions that lead to a slowdown of the rotational diffusion. This article further considers the dynamical behavior of a tracer particle in the solution of active dumbbells. Interestingly, the speed of the passive tracer particle displays a crossover from monotonically decreasing to increasing with the size of the tracer particle upon increasing the dumbbells' speed. Furthermore, the effective diffusion of the tracer particle displays non-monotonic behavior with the area fraction; the initial increase in diffusivity is followed by a decrease for a larger area fraction.

Particle-wall alignment interaction and active Brownian diffusion through narrow channels.

We numerically examine the impacts of particle-wall alignment interactions on active species diffusion through a structureless narrow two-dimensional channel. We consider particle-wall interaction to depend on the self-propulsion velocity direction whereby some specific particle's alignments with respect to the boundary walls are stabilized more. Further, the alignment interaction is meaningful as long as particles are close to the confining boundaries. Unbiased diffusion of active particles for various possible stable velocity alignments against the walls has been examined. We show that for the most stable configuration leading to the self-propulsion velocity direction perpendicular to the wall, diffusivity becomes inversely proportional to the square of the alignment interaction torque. On the other hand, when the self-propulsion velocity direction making an acute angle to the channel walls is the most stable configuration, diffusion exponentially grows with strengthening alignment interaction. Hence, particle-wall interaction plays a pivotal role in the transport control of active particles through narrow channels. Moreover, the impacts of the alignment interactions on diffusion largely depend on the particle's self-propulsion properties and its chirality. Our simulation results can potentially be used to understand unbiased diffusion of artificial or living micro/nano-objects (such as virus, bacteria, Janus particles, etc.) though narrow confined structures.

Shape anisotropy induced jamming of nanoparticles at liquid interfaces: a tensiometric study.

The intersection of nanotechnology and interfacial science has opened up new avenues for understanding complex phenomena occurring at liquid interfaces. The assembly of nanoparticles at liquid/liquid interfaces provides valuable insights into their interactions with fluid interfaces, essential for various applications, including drug delivery. In this study, we focus on the shape and concentration effects of nanoscale particles on interfacial affinity. Using pendant drop tensiometry, we monitor the real-time interfacial tension between an oil droplet and an aqueous solution containing nanoparticles. We measure two different types of nanoparticles: spherical gold nanoparticles (AuNPs) and anisotropic gold nanorods (AuNRs), each functionalized with surfactants to facilitate interaction at the interface. We observe that the interface equilibrium behaviour is mediated by kinetic processes, namely, diffusion, adsorption and rearrangement of particles. For anisotropic AuNRs, we observe shape-induced jamming of particles at the interface, as evidenced by their slower diffusivity and invariant rearrangement rate. In contrast, the adsorption of spherical AuNPs is dynamic and requires more time to reach equilibrium, indicating weaker interface affinity. By detailed analysis of the interfacial tension data and interaction energy calculations, we show that the anisotropic particle shape achieves stable equilibrium inter-particle separation compared to the isotropic particles. Our findings demonstrate that anisotropic particles are a better design choice for drug delivery applications as they provide better affinity for fluid interface attachment, a crucial requirement for efficient drug transport across cell membranes. Additionally, anisotropic shapes can stabilize interfaces at low particle concentrations compared to isotropic particles, thus minimizing side effects associated with biocompatibility and toxicity.

Convolutional neural networks applied to differential dynamic microscopy reduces noise when quantifying heterogeneous dynamics.

Differential dynamic microscopy (DDM) typically relies on movies containing hundreds or thousands of frames to accurately quantify motion in soft matter systems. Using movies much shorter in duration produces noisier and less accurate results. This limits the applicability of DDM to situations where the dynamics are stationary over extended times. Here, we investigate a method to denoise the DDM process, particularly suited to when a limited number of imaging frames are available or when dynamics are quickly evolving in time. We use a convolutional neural network encoder-decoder (CNN-ED) model to reduce the noise in the intermediate scattering function that is computed via DDM. We demonstrate this approach of combining machine learning and DDM on samples containing diffusing micron-sized colloidal particles. We quantify how the particles' diffusivities change over time as the fluid they are suspended in gels. We also quantify how the diffusivity of particles varies with position in a sample containing a viscosity gradient. These test cases demonstrate how studies of non-equilibrium dynamics and high-throughput screens could benefit from a method to denoise the outputs of DDM.

Numerical simulation of particle flow around the impulsive monopile

In order to explore the particle flow field characteristics around the impulsive monopile, the particle flow field and particle distribution around impulsive monopile were simulated based on the classical impulsive flow around a monopile at medium and low Reynolds number (Re = 550). The Large Eddy Simulation (LES) technique was harnessed to capture the dynamic flow field around the monopile and to facilitate in-depth analysis of vortex evolution. Concurrently, the Lagrangian approach was employed to track and characterize particle trajectories, shedding light on the patterns of particle diffusion and congregation in the vicinity of the monopile under varying Reynolds numbers. Additionally, this investigation unveils the distinctive behavior of particle flow during the impulsive starting and stopping phases adjacent to the monopile. Leveraging these insights, the study elucidates the intricate influence of the unsteady flow field dynamics around the monopile on the progression of particle flow.

AMIDR: A Complete Pulse Method for Measuring Cathode Solid-State Diffusivity

Lithium transport within cathode material is driven by diffusion. While fast cathode diffusion is desired, solid-state diffusivity is one of the most difficult cell material properties to measure. However, it is an important property to understand as cathode capacity is often limited by poor diffusivity. GITT (Galvanostatic Intermittent Titration Technique) is a common diffusivity measurement method. This technique is accomplished by applying a series of constant current pulses to a cell and analyzing the voltage response. While easy to use, this method only measures near the surface of the active material by assuming that it takes the geometry of a semi-infinite plane. This approximation means that GITT is only accurate when measuring the very initial voltage response. However, the voltage response cannot be analyzed too early as this may amplify error produced by CDL (double layer capacitance) and RI (interface resistance) found at the cathode-electrolyte interface which GITT does not account for. EIS (Electrochemical Impedance Spectroscopy), an AC method, is better suited for distinguishing CDL/RI from diffusivity but also struggles to evaluate diffusivity beyond the surface of active material within a reasonable time frame1. In addition, instruments capable of quality EIS are often cost prohibitive compared to DC cell testers.The voltage responses after the first few moments of a pulse are relevant when evaluating cell behaviour in real-world applications and should be considered to produce comprehensive measurements of diffusivity and its performance impact. AMID (Atlung Method for Intercalant Diffusion) is a multi-rate pulse method produced by this lab which uses a series of pulses, both short/fast and long/slow, and an approximation-free analytical model (developed by Sven Atlung) for diffusion in a spherical particle, not a semi-infinite plane2. However, AMID cannot differentiate diffusion impedance from resistance and therefore requires that resistance be considered negligible. In addition, while AMID mathematics are free of approximations, the typical multi-rate pulse protocol does require that series of pulses be approximated as singular pulses3. Lastly, the multi-rate pulse protocol limits the state-of-charge resolution of diffusivity measurements.AMIDR (Atlung Method for Intercalant Diffusion and Resistance) incorporates a GITT-style single pulse protocol with AMID-style mathematics modified to account for resistance as shown in Figure 1b). AMIDR collects pulse data with a pseudo-logarithmic time distribution. This means that the same number of data points is collected in 0.1-1.0 seconds, 1.0-10 seconds, 10-100 seconds and so on. Unlike GITT which measures diffusivity at an early arbitrary point in a pulse, AMIDR considers the entire length of a pulse comprehensively. Fractional capacity is calculated for each datapoint by dividing the actual capacity with the ideal “impedance-free” capacity expected at that voltage as shown in Fig 1c). These fractional capacity values, normalized from 0-1, are fitted to the modified Atlung model. This modified Atlung model is nearly the same as the original Atlung model except an additional term is added for capacity limited by resistance. Unlike AMID, AMIDR measures both diffusivity and resistance simultaneously by using both as fitting parameters. Lastly, while CDL is usually small enough for RI to behave simply like ohmic resistance on relatively short time scales (>0.1 seconds), AMIDR has the option to account for CDL/RI when relevant such as near the bottom of a complete discharge.AMIDR is a new diffusivity measurement method tailored for cathode material in Li-ion batteries. By combining the advantages of various previous diffusivity methods, AMIDR eliminates many of their concerns and sources of error such as sampling of only the material surface, alternate confounding sources of impedance, and expensive equipment as seen in Figure 1a). When paired with proper cell design (i.e. ultra-low single layer cathode loading to remove pore impedance and reference electrodes to remove anode impedance), AMIDR can produce high resolution diffusivity measurements with improved accuracy and applicability to real-world cell performance.References C. Deng and W. Lu, Journal of Power Sources, 473, 228613 (2020).A. Liu et al., J. Electrochem. Soc., 168, 070503 (2021).M. Doyle, J. Newman, and J. Reimers, Journal of Power Sources, 52, 211–216 (1994). Figure 1. (a) Comparison of various Diffusivity measurements. (b) The modified Atlung model for diffusion in a spherical particle. (c) How pulse data is converted to the unitless Atlung model. Figure 1

Low Redox Potential InN Nanowires as High-Energy-Density Anode for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) have become the most predominant energy sources for various digital products and electric vehicles due to the rechargeable, high capacity, and no memory effect characteristics. Charging to 80% of the full capacity time is commonly required to be less than 30 minutes in the future as technological development. Thus, it is essential to improve the dynamics of the active material [1]. The current approach to increasing the energy density of LIBs is to increase the electrode loading mass. This has the effect of greatly increasing the thickness of the electrodes, making it difficult for Li-ions to quickly pass through the electrode. Graphite anode is the most widely used anode material due to its low cost, easy preparation, and applicable working voltage below 0.3 V (vs Li/Li+). However, its low capacity and disordered staging behavior at large currents limit the fast charging performance. Moreover, due to the low density of graphite, when designed as a high-energy density cell, its thickness increases considerably, significantly reducing the Li-ions diffusion. Therefore, new active materials with high capacity and fast Li-ions diffusion should be developed to achieve high-energy-density cells.As a well-known semiconductor, Indium Nitride (InN) has been widely used for various applications such as high-frequency terahertz and solar energy devices. When it is denoted as an anode for LIBs, the InN can deliver a theory specific capacity over 1200 mAh g-1, which is nearly four times of the conventional graphite anodes (~372 mAh g-1). Meanwhile, it shows a relatively low redox potential (below 0.7 V vs. Li+/Li), affording a wide voltage window for a full cell. Remarkably, the InN is a narrow band (0.69 eV) semiconductor showing good electron transfer properties. Thus, the InN could be considered as one of the most promising anode for the next-generation HED-LIBs. However, the inherent volume change of InN particles during the conversion and alloying/dealloying process with Li+ maybe lead to many adverse effects, including pulverization of InN particles electrode, tearing solid electrolyte interphase (SEI). The fact finally worsens the Li-storage performance, greatly hindering the commercial application of InN anode. In addition, the sluggish ion diffusion of InN particles also limits the rate capability. To address the issues, approaches like constructing micro/nano composite structures, which have been broadly explored to alleviate the volume expansion and improve ion diffusion [2]. Among these micro/nanostructures, the 1D nanostructure shows superior physicochemical properties in favor of improving Li-storage performance. Firstly, the One-dimensional (1D) nanostructures can accommodate the strain of Li-ions delidiation/lidiation along specified directions. Secondly, the gap of the 1D nanostructure is just enough to provide a buffer during the charge/discharge process. Thirdly, the 1D nanostructure is beneficial to the Li-ions diffusion leading to good rate performance [3]. Moreover, the 1D structure has the advantage of self-forming film, which greatly reduces the weight of the electrode. It is worth noting that the large ratio aspect of 1D nanostructure can lower the ion diffusion paths accelerating the rate performance.Herein, we elaborately constructed the single-crystal InN nanowires on carbon fibers (InN/Au-CFs). Firstly, the CFs are decorated with Au nanoparticles by a magnetron sputtering method, which can afford many stable growth sites for InN nanowires. And then, the InN nanowires in situ grown on the Au nanoparticle surface by chemical vapor deposition successfully improved the adhesive force active material and substrate. The structural properties and Li-storage performance of InN/Au-CFs are systematically studied. In addition, the density of state and adatom adsorption of InN nanowires are calculated by density functional theory. The ion diffusion, reactivity, and electrode stability are successfully enhanced based on the results. We also built two-terminal electrodes to decelerate the metal electrode fatigue at large rate for a full cell. Particularly, the as-prepared InN/Au-CFs anode can deliver the capacities of 624.2 mAh g-1 at 0.1 A g-1 after 500 cycles and 416 mAh g-1 at 30 A g-1 for the rate assessment, respectively. Our work suggests that the embellishment one dimentional InN nanowire and two-terminal electrodes are a successful strategy for HED-LIBs contributing excellent long cycling performance and large rate capability.[1] J. Kim et al, Nat. Rev. Mater. 8(1) (2023) 54-70.[2] A. M. Bates, et al, Joule 6(4) (2022) 742-755.[3] L. Xing et al, Nano Energy 79 (2021) 105384. Figure 1

Blood Cell Detection Method Using Single-Entity Electrochemistry

The study of single-entity electrochemistry on ultramicroelectrodes, also known as Nano-impact electrochemistry or Single-particle collision, has recently gained attention. This method allows for the analysis of various properties of particles in solution, such as diffusion coefficient, particle composition, size distribution, and concentration. Our group has also explored the use of this technique on soft entities like emulsions, bacteria, red blood cells, and platelets. By monitoring the increase or decrease in current signal when a single-entity collides with the electrode surface, we can gain insights into the properties and composition of the particles. For example, if there is a high concentration of redox constantly reacting in the UME and red blood cells collide with the electrode surface, we can observe a decrease in the current. As another example, when platelets collide with electrodes, the concentration of serotonin present in platelets can be analyzed. In this case, an increase in the current signal is observed. The increase or decrease of the current signal depends on the characteristics of the single-entity and the composition of the solution. As such, single-entity electrochemistry is a useful way to obtain important information about various entities.

Методический подход к исследованию магнитного взаимодействия тонкодисперсных частиц в водной суспензии методом компьютерного моделирования

The need to increase the efficiency of mining operations determines the relevance of studying the properties of fine mineral particles for their extraction and reduction of environmental pollution. The development of approaches to studying magnetic interaction of fine particles is of scientific and practical importance and is associated with the complexity of calculating the superposition of their fields when solving the problem of controlling the magnetic properties of a suspension. A computer model of the dynamics of magnetic particle interaction was developed with account for their aggregation under the impact of the magnetic dipole-dipole interaction and the destruction of aggregates during thermal (Brownian) motion in order to predict the magnetic properties of the suspension using the example of magnetite deposits of the Zaimandrovsky iron ore region. The calculation shows that the electrostatic and dispersion interactions do not have a significant effect on the interaction dynamics of micron and submicron particles of magnetite with account of the experimentally measured zeta potential and the Hamaker's constant. A procedure has been developed for calibrating a computer model of the interaction dynamics of magnetic particles using the temperature dependence of the coefficient of translational diffusion of magnetite particles and the concentration dependence of the magnetic susceptibility of a suspension. An array of calculated values of the diffusion coefficient of the model particles and the initial magnetic susceptibility of their system is formed in a wide range of computer model parameters. A procedure has been developed for linking the calculated and experimental data by varying the normalization parameters of the particle size, viscosity of the medium, and thermal energy in order to minimize the maximum discrepancy between the values. The necessity is established of taking into account the change in magnetic properties with a decrease in the size of magnetite particles during the calibration of the magnetic susceptibility of a model system. The developed methodological approach ensures good convergence of the calculated and experimental data and makes it possible to visualize the aggregation of the model particles as the result of dipole-dipole interactions. The developed computer model of the interaction dynamics of magnetic particles can be used to study the effect of an external magnetic field on the aggregation ability of fine magnetite particles in order to control their extraction in separation processes.

M13-Phage-Based Star-Shaped Particles with Internal Flexibility.

We report on the construction and the dynamics of monodisperse star-shaped particles, mimicking, at the mesoscale, star polymers. Such multiarm star-like particles result from the self-assembly of gold nanoparticles, forming the core, with tip-linked filamentous viruses (M13 bacteriophages) acting as spines in a sea urchin-like structure. By combining fluorescence and dark-field microscopy with dynamic light scattering, we investigate the diffusion of these hybrid spiny particles. We reveal the internal dynamics of the star particles by probing their central metallic core, which exhibits a hindered motion that can be described as a Brownian particle trapped in a harmonic potential. We therefore show that the filamentous viruses and specifically their tip proteins behave as entropic springs, extending the relevance of the study of such hybrid mesoscopic analogues of star polymers to phage biotechnology.

Galactic cosmic ray transport in the absence of resonant scattering

ABSTRACT Galactic cosmic ray transport relies on the existence of turbulence on scales comparable with the gyration radius of the particles and with wavenumber vector oriented along the local magnetic field. In the standard picture, in which turbulence is injected at large scales and cascades down to smaller scales, it is all but guaranteed that the turbulent fluctuations at the scales relevant for resonant scattering may be present, either because of anisotropic cascading or because of the onset of damping processes. This raises questions on the nature of cosmic ray scattering, especially at energies ≳1 TeV, where self-generation is hardly relevant. Here, by means of numerical simulations of charged test particles in a prescribed magnetic field, we perform a gedankenexperiment aimed at investigating particle diffusion in a situation in which turbulence is mainly present at large scales, and discuss possible implications of this set-up for cosmic ray transport phenomenology.

Formulation and In-Vitro Evaluation of Gabapentin Loaded Transferosomal Gel

Gabapentin is an anticonvulsant which is primarily used to treat peripheral neuropathy. It belongs to class III drugs having High solubility and low permeability. The present study is carried out on formulation and invitro evaluation of Gabapentin Transferosomal gel by using various surfactants, polymers. The Transferosomes were prepared by thin film hydration technique. Different proportions of surfactants have been utilized such as span 80 and tween 80. 8 distinctive formulations of Gabapentin transferosomes had been formulated and evaluated for parameters like FTIR, Organoleptic evaluation, Drug content, Entrapment efficiency, Invitro diffusion studies, SEM, Zeta potential and particle size analysis, pH, Spreadability, Viscosity, Stability testing and Kinetic studies. The optimized formulation Gf6 confirmed Entrapment efficiency of 86.54%, Drug content 96.11% Zeta potential of -31.64Mv, particle size of 164.2nm, In-vitro drug release ranging from (75.15- 89.37%). The drug release data of selected formulation confirmed desirable fit into Zero order and Peppas release Kinetics. Overall, it can be concluded that the transferosomal gel can overcome problems associated with convectional routes of drug delivery.
 Keywords: Gabapentin, Transferosomes, Edge activators, Thin film hydration method.

Dissolution mechanism and computational model for controlling dissolution rate of alumina particles in CaO–SiO2–Al2O3–MgO slag

In present study, a high-efficiency CaO–SiO2–Al2O3–MgO slag was designed to dissolve alumina particles, and the in situ observation for alumina particles dissolution by CSLM instrument equipped with color fast CCD camera was conducted, which broke through the limitations of the original CSLM equipment. The dynamic measurements showed that the dissolution behavior of alumina particles was controlled by diffusion. Moreover, the mechanism of dissolution of alumina particles was illustrated by combining SEM-EDS detection and thermodynamic calculations. It was found that there was an about 3 μm distinct interface layer when alumina particles were dissolved in slag, and the formation order of products of the interface layer was closely related to its thermodynamics. In addition, the apparent activation energy of reaction between Al2O3 and CaO was used to calculate the reaction time of alumina particles with different particle size, and the viscosity values of the all slags obtained by the Factsage 8.2 thermodynamic software and Stocks-Einstein theory were used to calculate the time required for diffusion of alumina particles with different particle sizes. Compared the total time of chemical reaction and the diffusion mass transfer, it is found that when the radius of alumina particles is greater than 2.5 μm, the dissolutions of alumina particles in all CaO–SiO2–Al2O3–MgO slag composition points are controlled by diffusion.