Metal complexation of crosslinked polyacrylamide-supported dithiocarbamates: Effect of the molecular character and extent of crosslinking on complexation

Dithiocarbamate functions were incorporated into different polyacrylamide matrices crosslinked with a flexible and hydrophilic crosslinking agent, tetraethyleneglycol diacrylate (TEGDA), and their complexation behaviours were investigated. Crosslinked polyacrylamides with varying extents of the tetrafunctional TEGDA crosslinks were prepared by free radical solution polymerization at 60°C using potassium persulphate as initiator in ethanol. The dithiocarbamate functionality was incorporated into these polyacrylamides by a two-step polymer-analogous reaction involving (i)trans-amidation with ethylenediamine and (ii) dithiocarbamylation of the aminopolyacrylamide with carbon disulphide and alkali. The complexations of dithiocarbamate with Cu(II), Ni(II), Zn(II), Co(II) and Hg(II) ions were followed under different conditions. The metal ion intake varied with the extent of the crosslinking agent and the observed trend in complexation is Hg(II) > Cu(II)> Zn(II)> Co(II)> Ni (II). The time-course of complexation, the possibility of recycling, swelling characteristics, and spectral and thermal analyses were carried out. The thermal stability increases upon complexation with metal ions.     

New reals: Can live with them,can live without them

We give a self‐contained proof of the preservation theorem for proper countable support iterations known as “tools‐preservation”, “Case A” or “first preservation theorem” in the literature. We do not assume that the forcings add reals. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

STUDIES ON THE PREPARATION OF ZINC-CONTAINING ACTIVATED CARBON FIBERS AND THEIR ANTIBACTERIAL ACTIVITY

1. INTRODUCTION Microbial pollution will bring about various problems in industry and other vital fields, such as causing decomposing of materials, harming people抯 health. In order to reduce these problems, new antibacterial materials have been demanded. Recently, much attention has been paid to inorganic materials including zinc oxide [1~4]. These inorganic antibacterial materials are now substituting for organic materials to avoid releasing noxious organic molecules harmful to humans;…     

Optimization of Ultrasonic-Assisted Extraction of Active Components and Antioxidant Activity from Polygala tenuifolia: A Comparative Study of the Response Surface Methodology and Least Squares Support Vector Machine

Dried roots of Polygala tenuifolia (YuanZhi in Chinese) are widely used in Chinese herbal medicine. These components in YuanZhi have significant anti-oxidation properties owing to high levels of 3,6’-disinapoylsucrose (DISS) and Polygalaxanthone III (PolyIII). In order to efficiently extract natural medicines, response surface methodology (RSM) and least squares support vector machine (LSSVM) were used for the modeling and optimization of ultrasound-assisted extraction of DISS and PolyIII together to determine the antioxidant activity of the extracts obtained from YuanZhi. For the optimal combination of the comprehensive yield of DISS and PolyIII (Y), the Box-Behnken design (BBD) was used to improve extraction time (X₁), extraction temperature (X₂), liquid–solid ratio (X₃), and ethanol concentration (X₄). The optimal process parameters were determined to be as follows: extraction time, 93 min; liquid–solid ratio, 40 mL/g; extraction temperature, 48 °C; and ethanol concentration, 67%. With these conditions, the predictive optimal combination comprehensive evaluation value is 13.0217. It was clear that the LS-SVM model had higher accuracy in predictive and optimization capabilities, with higher antioxidant activity and lower relative deviations values, than did RSM. Hence, the LS-SVM model proved to be more effective for the analysis and improvement of the extraction process.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

金催化乙炔氢氯化的密度泛函理论研究进展

乙炔氢氯化(AH)是生产氯乙烯的主要途径之一,传统上使用高毒性的汞催化剂,因此开发无汞催化剂迫在眉睫。金(Au)催化剂是最有潜力的替代催化剂之一,然而其活性Au物种、反应物的活化过程或反应过渡态结构等催化机理仍不够清晰。密度泛函理论（DFT）在研究由Au催化AH的反应机理中发挥了极其重要的作用。本文综述了DFT对金催化剂活性位点、反应物在催化剂上的吸附性质及反应机理的研究进展。重点讨论了DFT对阳离子金和金簇催化AH反应过程的模拟计算,包括Au电子状态、其它原子掺杂及金簇尺寸和形状对催化AH反应影响的模拟。结果表明DFT模拟计算在微观分子尺度上研究反应物的吸附、反应中间体及过渡态等方面发挥了关键作用,对理解Au催化AH反应机理做出了重要贡献。     

A simple zero length surface-modification approach for preparing novel bifunctional supports for co-immobilisation studies

A straightforward method of creating a bifunctional support possessing pre-defined ratios of amine and chloromethyl groups based upon a controlled conversion of standard Merrifield resin is presented. It avoids the extensive optimisation of reactant concentrations and reaction conditions required by existing methods. Furthermore, it avoids increases in the spacer/linker length that lead to detrimental reductions in the rigidity of the grafted layer. The method will allow for efficient, predictable and reproducible co-immobilisation of molecular species.     

Classification of Raman spectra using the correlation kernel

The classification of Raman spectra can be very useful in a wide range of diagnostic applications including bacterial identification. Before any form of classification can be carried out on the Raman spectra, some form of pre‐processing is commonly applied. This pre‐processing greatly affects the accuracy of the results and introduces user bias and over‐fitting effects. In this paper, we propose using support vector machines with the correlation kernel. The use of the correlation kernel on Raman spectra has not been presented before in any published work. Our results illustrate that the correlation kernel is ‘self‐normalizing’ and produces superior classification performance with minimal pre‐processing, even on highly noisy data obtained using inexpensive equipment. Such effective classification approaches can lead to clinically valuable diagnostic applications of Raman Spectroscopy. Copyright © 2010 John Wiley & Sons, Ltd.     

线性滑轨支撑微型斯特林制冷机的开发及性能调试

介绍了独立开发的采用线性滑轨支撑的微型整体式斯特林制冷机,对结构设计的要点进行了详尽的分析,指出采用线性滑轨支撑将大大改善旋转电机驱动斯特林制冷机的可靠性.文中还介绍了对影响制冷机性能特性的因素进行综合试验的情况.     

基于核方法的模拟电路故障诊断

核方法通过非线性映射将原始数据嵌入到高维特征空间,然后进行线性分析和处理,为基于知识的数据分析带来新的方法和模式;传统方法无法解决故障特征数据维数高、在故障样本交叠严重时多分类性能较差的问题,因此在电路故障特征数据预处理阶段,提出了分步骤分别在时域对电路输出电压波形进行小波包分析和在频域测量电路幅频特性的方法来提取电路故障特征;预处理后的故障特征向量只是8维向量,减少了SVM的训练时间;将该方法应用于国际标准电路中的CTSV滤波器电路的故障诊断,结果表明:该方法能突出不同故障的特性,故障诊断正确率达到98.57%(414/420)。