The electric double layer has a life of its own

Using molecular dynamics simulations with recently developed importance sampling methods, we show that the differential capacitance of a model ionic liquid based double-layer capacitor exhibits an anomalous dependence on the applied electrical potential. Such behavior is qualitatively incompatible with standard mean-field theories of the electrical double layer, but is consistent with observations made in experiment. The anomalous response results from structural changes induced in the interfacial region of the ionic liquid as it develops a charge density to screen the charge induced on the electrode surface. These structural changes are strongly influenced by the out-of-plane layering of the electrolyte and are multifaceted, including an abrupt local ordering of the ions adsorbed in the plane of the electrode surface, reorientation of molecular ions, and the spontaneous exchange of ions between different layers of the electrolyte close to the electrode surface. The local ordering exhibits signatures of a first-order phase transition, which would indicate a singular charge-density transition in a macroscopic limit

Measurements of absolute K -shell ionization cross sections and L -shell x-ray production cross sections of Ge by electron impact

Results from measurements of absolute K -shell ionization cross sections and L α x-ray production cross sections of Ge by impact of electrons with kinetic energies ranging from the ionization threshold up to 40 keV are presented. The cross sections were obtained by measuring K α and L α x-ray intensities emitted from ultrathin Ge films deposited onto self-supporting carbon backing films. Recorded x-ray intensities were converted to absolute cross sections by using estimated values of the sample thicknesses, the number of incident electrons, and the detector efficiency. Experimental data are compared with the results of widely used simple analytical formulas, with calculated cross sections obtained from the plane-wave and distorted-wave Born approximations and with experimental data from the literature

SAR image segmentation with GMMs

This paper proposes a new approach for Synthetic Aperture Radar (SAR) image segmentation. Segmenting SAR images can be challenging because of the blurry edges and the high speckle. The segmentation proposed is based on a machine learning technique. Gaussian Mixture Models (GMMs) were already used to segment images in the visual field and are here adapted to work with single channel SAR images. The segmentation suggested is designed to be a first step towards feature and model based classification. The recall rate is the most important as the goal is to retain most target's features. A high recall rate of 88%, higher than for other segmentation methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, was obtained. The next classification stage is thus not affected by a lack of information while its computation load drops. With this method, the inclusion of disruptive features in models of targets is limited, providing computationally lighter models and a speed up in further classification as the narrower segmented areas foster convergence of models and provide refined features to compare. This segmentation method is hence an asset to template, feature and model based classification methods. Besides this method, a comparison between variants of the GMMs segmentation and a classical segmentation is provided

New Perspectives on the Charging Mechanisms of Supercapacitors.

Supercapacitors (or electric double-layer capacitors) are high-power energy storage devices that store charge at the interface between porous carbon electrodes and an electrolyte solution. These devices are already employed in heavy electric vehicles and electronic devices, and can complement batteries in a more sustainable future. Their widespread application could be facilitated by the development of devices that can store more energy, without compromising their fast charging and discharging times. In situ characterization methods and computational modeling techniques have recently been developed to study the molecular mechanisms of charge storage, with the hope that better devices can be rationally designed. In this Perspective, we bring together recent findings from a range of experimental and computational studies to give a detailed picture of the charging mechanisms of supercapacitors. Nuclear magnetic resonance experiments and molecular dynamics simulations have revealed that the electrode pores contain a considerable number of ions in the absence of an applied charging potential. Experiments and computer simulations have shown that different charging mechanisms can then operate when a potential is applied, going beyond the traditional view of charging by counter-ion adsorption. It is shown that charging almost always involves ion exchange (swapping of co-ions for counter-ions), and rarely occurs by counter-ion adsorption alone. We introduce a charging mechanism parameter that quantifies the mechanism and allows comparisons between different systems. The mechanism is found to depend strongly on the polarization of the electrode, and the choice of the electrolyte and electrode materials. In light of these advances we identify new directions for supercapacitor research. Further experimental and computational work is needed to explain the factors that control supercapacitor charging mechanisms, and to establish the links between mechanisms and performance. Increased understanding and control of charging mechanisms should lead to new strategies for developing next-generation supercapacitors with improved performances.The authors acknowledge the Sims Scholarship Cambridge (A.C.F.), the School of the Physical Sciences of the University of Cambridge (via an Oppenheimer Research Fellowship, C.M.), EPSRC (via the Supergen consortium, A.C.F. and J.M.G.), and the EU ERC (via an Advanced Fellowship to C.P.G.) for funding. We thank Nicole Trease, Andrew Ilott, Phoebe Allan, Elizabeth Humphreys, Paul Bayley, Hao Wang, Patrice Simon, Wan-Yu Tsai, Yury Gogotsi, Mathieu Salanne, Benjamin Rotenberg, Alexei Kornyshev, Svyatoslav Kondrat and Volker Presser for collaboration, and stimulating discussions and insights into supercapacitors over the course of our research on this subject.This is the final version of the article. It first appeared from the American Chemical Society via https://doi.org/10.1021/jacs.6b0211

Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201

Flight Determination of Drag of Normal-Shock Nose Inlets with Various Cowling Profiles at Mach Numbers from 0.9 to 1.5

External-drag data are presented for normal-shock nose inlets with NACA 1-series, parabolic, and conic cowling profiles. The tests were made at an angle of attack of 0 degrees by using rocket-propelled models in free flight at Mach numbers from 0.9 to 1.5. The Reynolds number based on body maximum diameter varied from 2.5 x 10 sup 6 to 5.5 x 10 sup 6. At maximum flow rate, the inlet models had about the same external drag at a Mach number of approximately 1.1, but at higher Mach numbers the sharp-lip conic cowling had the least drag. Blunting or beveling the lip of the conic cowling while keeping the fineness ratio constant resulted in drag coefficients slightly higher than for the sharp-lip conic cowling at maximum flow rate. At a mass-flow ratio of about 0.8, the conic cowlings with sharp, blunt, or beveled lips and the parabolic cowling all gave about the same drag. The higher drag of the NACA 1-49-300 cowling, compared with the blunt-lip conic cowling, is associated with the greater fullness back of the inlet

Probabilistic analysis of the upwind scheme for transport

We provide a probabilistic analysis of the upwind scheme for multi-dimensional transport equations. We associate a Markov chain with the numerical scheme and then obtain a backward representation formula of Kolmogorov type for the numerical solution. We then understand that the error induced by the scheme is governed by the fluctuations of the Markov chain around the characteristics of the flow. We show, in various situations, that the fluctuations are of diffusive type. As a by-product, we prove that the scheme is of order 1/2 for an initial datum in BV and of order 1/2-a, for all a>0, for a Lipschitz continuous initial datum. Our analysis provides a new interpretation of the numerical diffusion phenomenon