Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

Incompatibility networks as models of scale-free small-world graphs

We make a mapping from Sierpinski fractals to a new class of networks, the incompatibility networks, which are scale-free, small-world, disassortative, and maximal planar graphs. Some relevant characteristics of the networks such as degree distribution, clustering coefficient, average path length, and degree correlations are computed analytically and found to be peculiarly rich. The method of network representation can be applied to some real-life systems making it possible to study the complexity of real networked systems within the framework of complex network theory.     

基于人工神经网络的施工安全性预警模型研究

在研究施工安全性特征的基础上,提出了施工安全性评价指标体系,结合人工神经网络模型,提出了基于人工神经网络的施工安全性预警模型,最后结合实例验证了该安全预警模型的可行性。     

Exploring artificial neural networks to model interatomic and intermolecular potential energy surfaces

We demonstrate how a back-propagation artificial neural network can be trained to represent a potential energy surface (PES) in a formless manner with limited data points and exploited to predict interaction energies for configurations not included in the training set. A similar exercise is undertaken for predicting the eigenvalues and eigenvectors of a model Hamiltonian matrix that delicately depends on parameters and exhibits crossing of eigen values.     

过渡金属电催化剂在硅基光阳极分解水产氧中的作用

光电化学分解水可将太阳能转换为绿色的氢能,为目前的能源危机和环境问题提供了一种理想的解决方案.在分解水反应中,涉及四空穴过程的产氧半反应是制约性能的关键步骤,往往需要在半导体表面沉积电催化剂以加速产氧反应动力学.因此,全面理解电催化剂在光电化学分解水体系中的作用至关重要.在目前的产氧电催化剂中,过渡金属羟基氧化物电催化剂(MOOH,M=Fe,Co,Ni)因其环保、廉价、高效以及稳定的特性,已被广泛用于半导体光阳极分解水器件中.而且,MOOH可用简单的电沉积方法沉积在光电极表面,易于大面积制备.然而,电沉积法制备的MOOH具有复杂的结构,对其作用机制的全面理解更加困难.因此,本文以电沉积MOOH修饰的硅基光阳极(n⁺p-Si/SiO_x/Fe/FeOx/MOOH)作为模型,研究了不同电催化剂对硅光阳极光电化学产氧性能的影响.实验发现电催化剂的界面优化在电催化剂修饰的光电极中发挥着重要作用,这是因为优化的界面可以提升界面电荷传输,提供更多的催化反应活性位点以及更高的本征催化活性,从而更有利于光解水性能的提升.该项研究揭示了电催化剂在光解水器件中的作用,并为今后高效光解水器件的设计提供了一定指导.首先在多晶n⁺p-Si基底上热蒸镀了一层30 nm的金属Fe膜,并通过电化学活化将Fe膜表面转换为FeOx得到Fe/FeOx(记作aFe)界面层,然后利用电沉积方法制备MOOH表面修饰层,最终得到n⁺p-Si/SiO_x/aFe:MOOH光阳极.X射线光电子能谱、拉曼光谱以及扫描电子显微镜表面元素成像的表征结果均证实电极表面由于界面层金属Fe元素的掺杂而形成了Fe1-xNixOOH.在模拟太阳光下用于光解水产氧时,n⁺p-Si/SiO_x/aFe:NiOOH电极的起始电位为~1.01 VRHE(相对于可逆氢电极的电势),在1.23 VRHE下的光电流为38.82 mA cm^-2,显著优于n⁺p-Si/SiO_x/aFe、n⁺p-Si/SiO_x/aFe:FeOOH以及n⁺p-Si/SiO_x/aFe:CoOOH三个对比样品,且其稳定性达到75 h.另外,我们发现n⁺p-Si/SiO_x/aFe:MOOH电极的光电化学产氧性能均显著高于n⁺p-Si/SiO_x/aFe电极,且p++-Si/SiO_x/aFe:MOOH的电催化产氧性能也高于p++-Si/SiO_x/MOOH,不仅证明了aFe界面层对Si与MOOH层之间的界面接触作用的有效调控,而且表明双电催化剂体系(aFe:MOOH)的电催化产氧活性高于单电催化剂(MOOH).热力学分析表明,n⁺p-Si/SiO_x/aFe:MOOH光阳极的光电压大小与其光解水产氧性能并不一致,从而排除了热力学因素对性能的关键影响.进一步从塔菲尔斜率、电化学活性表面积和电化学阻抗谱对各电极的动力学进行了分析,证明了动力学因素在上述光阳极产氧性能中的主导作用.同时发现,由于aFe:NiOOH双电催化剂具有更高的本征电催化产氧性能,提供了更多的表面活性位点以及更有效地促进了光生载流子的传输,对动力学的提升效果更显著,从而使n⁺p-Si/SiO_x/aFe:NiOOH光阳极表现出最高的光解水产氧性能.     

锂金属负极人造保护膜的研究进展

金属锂因其具有极高的理论容量(3860 mAh·g^?1)、最低的电极电位(?3.04 V vs.标准氢电极)和低的密度(0.534 g·cm^?3),被认为是最具潜力的负极材料。但循环过程中不可控的枝晶生长及不稳定的固体电解质相界面膜所引起的安全隐患和电池库伦效率低等问题严重阻碍了锂金属负极的发展。通过在电极表面构建人造保护膜可以有效调控锂离子沉积行为,因此人造保护膜的构建是一种简单高效抑制锂枝晶生长的策略。本综述将从聚合物保护膜、无机保护膜、有机-无机复合保护膜和合金保护膜总结了人造保护膜的构建方法、抑制锂枝晶生长机理,为促进高比能锂金属电池的商业化应用提供借鉴参考作用。     

Year Discrimination of Mild Aroma Chinese Liquors Using Three-Dimensional Fluorescence Spectroscopy Combined with Parallel Factor and Neural NetworkSCIEI北大核心CSCD

Three-dimensional fluorescence spectroscopy coupled with parallel factor analysis and neural network was applied to the year discrimination of mild aroma Chinese liquors. The excitation-emission fluorescence matrices (EEMs) of 120 samples with various years were measured by FLS920 fluorescence spectrometer. The trilinear decomposition of the data array was performed and the loading scores of and the excitation-emission profiles of four components were also obtained. The scores were employed as the inputs of the BP neural networks and the PARAFAC-BP identification model was constructed. 10 samples were collected from 10, 20 and 30 years of liquors respectively, and 30 samples were selected as the test sets. The remaining 90 samples were used as the training sets to build the training model. The year prediction of unknown samples was also carried out, and the prediction accuracy was 90%, 100% and 100%, respectively. Meanwhile, the discrimination analysis method and the multi way partial least squares discriminant analysis were compared, namely PARAFAC-BP and NPLS-DA. The results indicated that parallel factor combined with the neural network (PARAFAC-BP) has higher prediction accuracy. The proposed method can effectively extract the spectral characteristics, and also reduce the dimension of the input variables of neural network. A good year discrimination result was finally achieved.     

Uncertain multiobjective traveling salesman problem

Traveling salesman problem is a fundamental combinatorial optimization model studied in the operations research community for nearly half a century, yet there is surprisingly little literature that addresses uncertainty and multiple objectives in it. A novel TSP variation, called uncertain multiobjective TSP (UMTSP) with uncertain variables on the arc, is proposed in this paper on the basis of uncertainty theory, and a new solution approach named uncertain approach is applied to obtain Pareto efficient route in UMTSP. Considering the uncertain and combinatorial nature of UMTSP, a new ABC algorithm inserted with reverse operator, crossover operator and mutation operator is designed to this problem, which outperforms other algorithms through the performance comparison on three benchmark TSPs. Finally, a new benchmark UMTSP case study is presented to illustrate the construction and solution of UMTSP, which shows that the optimal route in deterministic TSP can be a poor route in UMTSP.     

Numerical and empirical modeling of peak deceleration and stress analysis of polyurethane elastomer under impact loading test

In this work, we present the modeling of the peak deceleration (PD) using data of the experimental drop test. Specimens with different thicknesses and areas tested in the drop test device which has adaptable height and weight. In the empirical modeling of the PD, the thickness, area, drop mass and drop height considered as separable functions. An analytical model and Neural Network (NN) was used as the empirical models. Further, the stress on the material was calculated using differential equations and the Finite Element Method (FEM). The Obtained PD from the experimental test, analytical and NN models was converted to the stress on the material using a derived differential equation. Finally, the best model for analyzing the PD and Stress on the material was presented.     

The physicochemical properties role of a functionalized alkyl-peptide in nanofibre formation and neural progenitor cells viability and survival

Amphiphilic alkyl-peptides as novel biomaterials form 3D scaffolds that are applicable in tissue engineering. Here, the nanofibre formation capability of a distinct alkyl-peptide was investigated using coarse-grained molecular dynamics simulation (CGMD) and experimental methods. The alkyl-peptide (Ace-FAQRVPPEEEGGGAAAAK-Nhe(C16)) was functionalized with a peptide epitope (FAQRVPPP) which can help to maintenance and differentiation of neural stem cells. Two alkyl-peptide systems were investigated: the all-functionalized system (with only bioactive alkyl-peptides) and the distributed system (a combination of bioactive and non-bioactive alkyl-peptides with ratio 1:2). The CGMD and TEM results confirm elongated nanofibres for all-functionalized system and cylindrical nanofibres for the distributed one. Furthermore, PC12 cells show a reliable growth on both 2D alkyl-peptides coated surfaces. Because of the nanofibres negative surface charges, the cell morphologies show clustered form in the distributed system and rounded shape in the all-functionalized one. Since the stem cell state preserves in cluster form, the physicochemical property of these nanofibres allows a potential advantage in stem cell long-time maintains.