Single-Crystalline TiO2(B) Nanobelts with Unusual Large Exposed {100} Facets and Enhanced Li-Storage Capacity

The {100} facet of single-crystalline TiO₂(B) is an ideal platform for inserting Li ions, but it is hard to be obtained due to its high surface energy. Here, the single-crystalline TiO₂(B) nanobelts from H₂Ti₃O₇ with nearly 70% {100} facets exposed are synthesized, which significantly enhances Li-storage capacity. The first-principle calculations demonstrate an ab in-plane 2D diffusion through the exposed {100} facets. As a consequence, the nanobelts can significantly accommodate Li ions in LiTiO₂ formula with specific capacity up to 335 mAh g⁻¹, which is in good agreement with the electrochemical characterizations. Coating with conductive and protective poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate), the cut-off discharge voltage is as low as 0.5 V, leading to a capacity of 160.7 mAh g⁻¹ after 1500 cycles with a retention rate of 66% at 1C. This work provides a practical strategy to increase the Li-ion capacity and cycle stability by tailoring the crystal orientation and nanostructures.     

A Distribution‐freeCUSUMChart for Monitoring Variability of AutocorrelatedProcesses

We present a distribution‐free tabular cumulative sum chart for monitoring the variability of an autocorrelated process. A quantity known as the asymptotic variance parameter is employed as a measure of the variability, and a distribution‐free tabular cumulative sum chart is applied to variance estimates calculated from batches of nonoverlapping samples. The proposed chart is applicable to a stationary process with a general marginal distribution and a general autocorrelation structure. It also determines control limits analytically without trial‐and‐error simulations. The performance of the proposed chart is tested on stationary processes with both normal and nonnormal marginals with various autocorrelation structures. Copyright © 2014 John Wiley & Sons, Ltd.     

Reducing numerical dissipation in smoke simulation

Numerical dissipation acts as artificial viscosity to make smoke viscous. Reducing numerical dissipation is able to recover visual details smeared out by the numerical dissipation. Great efforts have been devoted to suppress the numerical dissipation in smoke simulation in the past few years. In this paper we investigate methods of combating the numerical dissipation. We describe visual consequences of the numerical dissipation and explore sources that introduce the numerical dissipation into course of smoke simulation. Methods are investigated from various aspects including grid variation, high-order advection, sub-grid compensation, invariant conservation, and particle-based improvement, followed by discussion and comparison in terms of visual quality, computational overhead, ease of implementation, adaptivity, and scalability, which leads to their different applicability to various application scenarios.     

Improved formic acid oxidation using electrodeposited Pd–Cd electrocatalysts in sulfuric acid solution

In the present research, nanostructured Pd–Cd alloy electrocatalysts with different compositions were produced using the electrodeposition process. The morphology of the samples was studied by scanning electron microscopy analysis. Also, the elemental composition of the samples was determined by energy-dispersive X-ray spectroscopy and elemental mapping tests. Tafel polarization and electrochemical impedance spectroscopy methods were employed to determine the electrochemical corrosion properties of the synthesized samples in a solution containing 0.5 M sulfuric acid and 0.1 M formic acid. The linear sweep voltammetry, cyclic voltammetry, and chronoamperometry techniques were also employed to evaluate the electrocatalytic activity of prepared samples toward the oxidation of formic acid. In this respect, the influence of some factors such as formic acid and sulfuric acid concentrations and also potential scan rate was investigated. Compared to the pure Pd sample, the Pd–Cd samples were more reactive for the oxidation of formic acid. Besides, the sample with a lower amount of Pd (Pd_1·3Cd) demonstrated much higher electrocatalytic activity than the Pd_7·1Cd and Pd_2·1Cd samples. The observed high mass activity of 15.06 A mg^?1_Pd for the Pd_1·3Cd sample which is 21.1 times higher than Pd/C is an interesting result of this study.     

Model Selection for Unsupervised Learning of Visual Context

This study addresses the problem of choosing the most suitable probabilistic model selection criterion for unsupervised learning of visual context of a dynamic scene using mixture models. A rectified Bayesian Information Criterion (BICr) and a Completed Likelihood Akaike’s Information Criterion (CL-AIC) are formulated to estimate the optimal model order (complexity) for a given visual scene. Both criteria are designed to overcome poor model selection by existing popular criteria when the data sample size varies from small to large and the true mixture distribution kernel functions differ from the assumed ones. Extensive experiments on learning visual context for dynamic scene modelling are carried out to demonstrate the effectiveness of BICr and CL-AIC, compared to that of existing popular model selection criteria including BIC, AIC and Integrated Completed Likelihood (ICL). Our study suggests that for learning visual context using a mixture model, BICr is the most appropriate criterion given sparse data, while CL-AIC should be chosen given moderate or large data sample sizes.