Determination of the electron density in methyl (±)‐(1S,2S,3R)‐2‐methyl‐1,3‐diphenylcyclopropanecarboxylate using refinements with X‐ray scattering factors from wavefunction calculations of the whole molecule

The molecule of the title compound, C₁₈H₁₈O₂, is a substituted cyclopropane ring. The electron density in this molecule has been determined by refining single‐crystal X‐ray data using scattering factors derived from quantum mechanical calculations. Topological analysis of the electron densities in the three cyclopropane C—C bonds was carried out. The results show the effects of this substitution on these C—C bonds.     

Flow injection analysis simulations and diffusion coefficient determination by stochastic and deterministic optimization methods

Stochastic and deterministic simulations of dispersion in cylindrical channels on the Poiseuille flow have been presented. The random walk (stochastic) and the uniform dispersion (deterministic) models have been used for computations of flow injection analysis responses. These methods coupled with the genetic algorithm and the Levenberg–Marquardt optimization methods, respectively, have been applied for determination of diffusion coefficients. The diffusion coefficients of fluorescein sodium, potassium hexacyanoferrate and potassium dichromate have been determined by means of the presented methods and FIA responses that are available in literature. The best-fit results agree with each other and with experimental data thus validating both presented approaches.     

Rotamer decomposition and protein dynamics: Efficiently analyzing dihedral populations from molecular dynamics

Molecular mechanics methods have matured into powerful methods to understand the dynamics and flexibility of macromolecules and especially proteins. As multinanosecond to microsecond length molecular dynamics (MD) simulations become commonplace, advanced analysis tools are required to generate scientifically useful information from large amounts of data. Some of the key degrees of freedom to understand protein flexibility and dynamics are the amino acid residue side chain dihedral angles. In this work, we present an easily automated way to summarize and understand the relevant dihedral populations. A tremendous reduction in complexity is achieved by describing dihedral timeseries in terms of histograms decomposed into Gaussians. Using the familiar and widely studied protein lysozyme, it is demonstrated that our approach captures essential properties of protein structure and dynamics. A simple classification scheme is proposed that indicates the rotational state population for each dihedral angle of interest and allows a decision if a given side chain or peptide backbone fragment remains rigid during the course of an MD simulation, adopts a converged distribution between conformational substates or has not reached convergence yet. © 2012 Wiley Periodicals, Inc.     

Determination of Drop Sizes in Annular Gas/Liquid Flows in Vertical and Horizontal Pipes

Measurement of drop sizes play vital role in applications dealing with gas/liquid flow mixtures. In the present work, drop sizes in vertical and horizontal pipe flows were determined using Malvern 2600HSD system that applies laser diffraction method. From the analysis of the experimental data obtained at two different pipe orientations, two separate expressions were developed to determine Sauter mean diameter, d ₃₂. Except for the 20 m/s superficial gas velocity in vertical flow case, a good agreement was found between the developed expressions and the experimental data.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.     

锌电极放电过程及失效机制数值分析

锌-空气电池是一种高能量的电池体系.实验表明, 在大功率工作条件下, 锌电极的材料利用率随电流密度的增加而急剧下降. 为探索其在大功率工作条件下的放电机理, 本文针对这一过程建立了一维数学模型, 通过数值求解模拟多个物理量如离子浓度、传递电流密度、电极孔隙度、固体氧化锌等在电极内部的分布变化情况, 在此基础上分析电极的性能. 数值结果分析表明, 固体氧化锌对电极内质量传输过程的限制是导致电极失效的根本原因. 其析出时间及在电极内部的集中分布位置对电极性能有显著影响; 而仅当其体积分数超过30%-35%的范围后才开始显著限制传质过程. 讨论了电极的优化措施, 模拟表明更高的溶液电导率,更大的电极孔隙度有利于增加大功率工作条件下电极的材料利用率. 但最重要的是保持电极内部氢氧根离子的浓度在一个较高的值,对于封闭式电极可以通过补液实现, 理想情况为设计一个电解液循环式的锌电极.     

Mathematical Model and Numerical Simulation of Conductometric Biosensor of Urea

In this article, a mathematical model was developed to describe and optimize the configuration of the urea biosensor. The biosensor is based on interdigitated gold microelectrodes modified with a urease enzyme membrane. The model presented here focuses on the enzymatic reaction and/or diffusion phenomena that occur in the enzyme membrane and in the diffusion layer. Numerical resolution of differential equations was performed using the finite difference technique. The mathematical model was validated using experimental biosensor data. The responses of the biosensor to various conditions were simulated to guide experiments, improve analytical performance, and reduce development costs.     

Local Structure Analysis and Modelling of Lignin-Based Carbon Composites through the Hierarchical Decomposition of the Radial Distribution Function

Carbonized lignin has been proposed as a sustainable and domestic source of activated, amorphous, graphitic, and nanostructured carbon for many industrial applications as the structure can be tuned through processing conditions. However, the inherent variability of lignin and its complex physicochemical structure resulting from feedstock and pulping selection make the Process-Structure-Property-Performance (PSPP) relationships hard to define. In this work, radial distribution functions (RDFs) from synchrotron X-ray and neutron scattering of lignin-based carbon composites (LBCCs) are investigated using the Hierarchical Decomposition of the Radial Distribution Function (HDRDF) modelling method to characterize the local atomic environment and develop quantitative PSPP relationships. PSPP relationships for LBCCs defined by this work include crystallite size dependence on lignin feedstock as well as increasing crystalline volume fraction, nanoscale composite density, and crystallite size with increasing reduction temperature.     

The evolution of numerical methods for predicting the distribution of surfactant in the bubble-scale dynamics of foams

Many numerical methods now exist to simulate the structure and dynamics of surface-tension-dominated aqueous foams at the level of the individual films and the liquid structures where they meet. We review these methods, focusing in particular on bubble-scale simulations of foam rheology. We highlight methods that allow the distribution of surfactant during flow to be taken into account.