Diffusion Limitations and Translocation Barriers in Atomically Thin Biomimetic Pores

Ionic transport in nano- to sub-nano-scale pores is highly dependent on translocation barriers and potential wells. These features in the free-energy landscape are primarily the result of ion dehydration and electrostatic interactions. For pores in atomically thin membranes, such as graphene, other factors come into play. Ion dynamics both inside and outside the geometric volume of the pore can be critical in determining the transport properties of the channel due to several commensurate length scales, such as the effective membrane thickness, radii of the first and the second hydration layers, pore radius, and Debye length. In particular, for biomimetic pores, such as the graphene crown ether we examine here, there are regimes where transport is highly sensitive to the pore size due to the interplay of dehydration and interaction with pore charge. Picometer changes in the size, e.g., due to a minute strain, can lead to a large change in conductance. Outside of these regimes, the small pore size itself gives a large resistance, even when electrostatic factors and dehydration compensate each other to give a relatively flat—e.g., near barrierless—free energy landscape. The permeability, though, can still be large and ions will translocate rapidly after they arrive within the capture radius of the pore. This, in turn, leads to diffusion and drift effects dominating the conductance. The current thus plateaus and becomes effectively independent of pore-free energy characteristics. Measurement of this effect will give an estimate of the magnitude of kinetically limiting features, and experimentally constrain the local electromechanical conditions.     

An experimental investigation on heat transfer enhancement in circular jet impingement on hot surfaces by using Al2O3/water nano-fluids and aqueous high-alcohol surfactant solution

The present article reports the heat transfer characteristics of a vertical stainless steel foil of 0.15 mm thickness (SS304) by circular impinging jets of various fluids such as pure water, nano-fluids (Al₂O₃-water, ф = 0.15%, 0.6%), and aqueous high-alcohol surfactant (HAS, i.e., 2-ethyl-hexanol, 100–400 ppm) studied using an infrared thermal imaging camera (A655sc, FLIR System). The enhancement in the heat transfer rates for Al₂O₃-water nano-fluids with ф = 0.15%, ф = 0.60%, and aqueous surfactant solution (150ppm) is found to be 140%, 207%, and 117% higher compared to pure water results, respectively. The surface characteristics of the foil after jet impingement by various fluids are also studied using scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), and surface wettability.     

The role of higher-multipolar and repulsive forces in the calculation of collision-broadened line-widths of linear molecules

Collision-broadened line widths in CO-CO₂ and CO-O₂ collisions have been calculated by incorporating interactions due to octopoles and hexadecapoles and short range repulsive interactions into Anderson's theory. It is shown how these higher-order interactions can be manipulated to yield good agreement with experimental data. A critical evaluation of this totally empirical manipulation suggests that a thorough revision of the theory is required for all but simple dipole-dipole interactions. In the process of the evaluation, the values of the multipole moments are discussed.     

Neural networks and organizational systems: Modeling non-linear relationships

For decades, organizational researchers have employed standard statistical methods to uncover relationships among variables and constructs. However, in complex organization systems, the prevalence of non-linearity and outliers is to be expected. Under such circumstances, the use of standard statistical methods becomes unreliable and, correspondingly, results in degraded predictions of the relationships within the organizational systems. We describe the use of neural network analyses to model team effectiveness so as to provide more accurate predictions for managers.     

A novel technique for measurement of self-generated magnetic fields and the plasma density in laser produced plasmas from the Faraday rotation using two color probes

Diagnostic information about the self-generated magnetic fields (SGMF) generated in laser produced plasmas is normally obtained by measuring the Faraday rotation angle (FRA) of a linearly polarized laser probe beam passing through the plasma. Simultaneous recording of the corresponding interferogram is required to get the density information necessary for estimating the magnetic field. The problem with this method is that the visibility of the fringes in the interferogram can be poor, and the SGMF cannot be calculated in the regions where the interference fringes are not observable. In this paper, we propose a new method to obtain the density distribution and the SGMF from two simultaneous measurements of FRA using two probe beams of different colors, which allows one to calculate the SGMF without the need of interferometry.     

Spectral properties of backward stimulated scattering in liquid carbon disulfide

The spectral structure of backward stimulated scattering from a 10 cm-long CS₂-liquid cell is investigated by using Q-switched 10-ns and 532-nm laser pulses with different spectral linewidths. Under a narrow spectral line (∼0.1 cm⁻¹) pump condition, very strong sharp lines near the pump wavelength (λ ₀) position and the first-order stimulated Raman scattering (λ _s1) position can be observed. However, under a wide line (≈1 cm⁻¹) pump condition, only a strong and superbroadening spectral band can be observed mainly in the red-shift side of the pump wavelength. The different spectral features under these two conditions can be explained by a competition between stimulated Brillouin, Raman, and Rayleigh-Kerr scattering. Under both pump conditions, the broadening spectral distributions are not consistent with the predictions given by stimulated Rayleigh-wing scattering theories, but can be interpreted well utilizing the theoretical model of stimulated Rayleigh-Kerr scattering. Zh. éksp. Teor. Fiz. 112, 1563–1573 (November 1997) Published in English in the original Russian journal. Reproduced here with stylistic changes by the Translation Editor.     

A numerical technique based on B-spline for a class of time-fractional diffusion equation

This paper presents an efficient numerical technique for solving a class of time-fractional diffusion equation. The time-fractional derivative is described in the Caputo form. The L1 scheme is used for discretization of Caputo fractional derivative and a collocation approach based on sextic B-spline basis function is employed for discretization of space variable. The unconditional stability of the fully-discrete scheme is analyzed. Two numerical examples are considered to demonstrate the accuracy and applicability of our scheme. The proposed scheme is shown to be sixth order accuracy with respect to space variable and (2 − α)-th order accuracy with respect to time variable, where α is the order of temporal fractional derivative. The numerical results obtained are compared with other existing numerical methods to justify the advantage of present method. The CPU time for the proposed scheme is provided.     

Rankin–Cohen brackets on Jacobi forms and the adjoint of some linear maps

Given a fixed Jacobi cusp form, we consider a family of linear maps between the spaces of Jacobi cusp forms using the Rankin–Cohen brackets, and then we compute the adjoint maps of these linear maps with respect to the Petersson scalar product. The Fourier coefficients of the Jacobi cusp forms constructed using this method involve special values of certain Dirichlet series associated to Jacobi cusp forms. This is a generalization of the work due to Kohnen (Math Z, 207:657–660, 1991) and Herrero (Ramanujan J,  10.1007/s11139-013-9536-5, 2014) in case of elliptic modular forms to the case of Jacobi cusp forms which is also considered earlier by Sakata (Proc Japan Acad Ser A, Math Sci 74, 1998) for a special case.     

Hierarchical Bayesian autoregressive models for large space–time data with applications to ozone concentration modelling

Increasingly large volumes of space–time data are collected everywhere by mobile computing applications, and in many of these cases, temporal data are obtained by registering events, for example, telecommunication or Web traffic data. Having both the spatial and temporal dimensions adds substantial complexity to data analysis and inference tasks. The computational complexity increases rapidly for fitting Bayesian hierarchical models, as such a task involves repeated inversion of large matrices. The primary focus of this paper is on developing space–time autoregressive models under the hierarchical Bayesian setup. To handle large data sets, a recently developed Gaussian predictive process approximation method is extended to include autoregressive terms of latent space–time processes. Specifically, a space–time autoregressive process, supported on a set of a smaller number of knot locations, is spatially interpolated to approximate the original space–time process. The resulting model is specified within a hierarchical Bayesian framework, and Markov chain Monte Carlo techniques are used to make inference. The proposed model is applied for analysing the daily maximum 8‐h average ground level ozone concentration data from 1997 to 2006 from a large study region in the Eastern United States. The developed methods allow accurate spatial prediction of a temporally aggregated ozone summary, known as the primary ozone standard, along with its uncertainty, at any unmonitored location during the study period. Trends in spatial patterns of many features of the posterior predictive distribution of the primary standard, such as the probability of noncompliance with respect to the standard, are obtained and illustrated. Copyright © 2012 John Wiley & Sons, Ltd.