DNA模板自组装正电荷纳米金基底的增强SERS效应

本文将合成的直径为10 nm的正电荷金纳米颗粒通过静电作用高密度自组装到带负电荷的长链λ-DNA分子上, 形成了高密度的具有纳米间隙的金纳米颗粒网络结构。研究了孤立的金纳米颗粒和所自组装的金纳米颗粒-DNA复合材料作为表面增强拉曼散射(SERS)基底的活性。原本对SERS信号响应较弱的10 nm直径的金纳米颗粒, 在自组装到DNA上形成具有纳米间隙的金纳米颗粒网络后, 产生了均匀、一致、强烈的SERS增强响应。我们利用用该基底对罗丹明G(R6G)、吡啶(Py)和对巯基苯胺(4-ATP)等不同类型的小分子化合物进行SERS检测的结果表明, 此方法制备SERS基底产率高、均一, 具有较好的SERS增强效果好, SERS信号稳定性和重复性相对常规孤立的金纳米颗粒SERS基底有很大提高。     

Low power autocorrelation technique based on the degree-of-polarization measurement

Based on the degree-of-polarization (DOP) measurement, an autocorrelation technique to measure the pulse width of chirp-free optical short pulse with a very low optical power (<?60 dBm) is demonstrated. The impacts of chirp and misalignment of the input light are also discussed in detail. The pulse width measurement results are consistent with the conventional second-harmonic generation (SHG) autocorrelation, while our autocorrelation technique does not require high input power or rigid optical alignment.     

Molecularly imprinted polymer layer-coated silica nanoparticles toward dispersive solid-phase extraction of trace sulfonylurea herbicides from soil and crop samples

This paper reports the preparation of metsulfuron-methyl (MSM) imprinted polymer layer-coated silica nanoparticles toward analysis of trace sulfonylurea herbicides in complicated matrices. To induce the selective occurrence of surface polymerization, the polymerizable double bonds were first grafted at the surface of silica nanoparticles by the silylation. Afterwards, the MSM templates were imprinted into the polymer-coating layer through the interaction with functional monomers. The programmed heating led to the formation of uniform MSM-imprinted polymer layer with controllable thickness, and further improved the reproducibility of rebinding capacity. After removal of templates, recognition sites of MSM were exposed in the polymer layers. As a result, the maximum rebinding capacity was achieved with the use of optimal grafting ratio. There was also evidence indicating that the MSM-imprinted polymer nanoparticles compared with nonimprinted polymer nanoparticles had a higher selectivity and affinity to four structure-like sulfonylurea herbicides. Moreover, using the imprinted particles as dispersive solid-phase extraction (DSPE) materials, the recoveries of four sulfonylurea herbicides determined by high-performance liquid chromatography (HPLC) were 80.2-99.5%, 83.8-102.4%, 77.8-93.3%, and 73.8-110.8% in the spiked soil, rice, soybean, and corn samples, respectively. These results show the possibility that the highly selective separation and enrichment of trace sulfonylurea herbicides from soil and crop samples can be achieved by the molecular imprinting modification at the surface of silica nanoparticles.     

Model-free control of Lorenz chaos using an approximate optimal control strategy

In this paper, we are concerned with model-free control of the Lorenz chaotic system, where only the online input and output are available while the mathematic model of the system is unknown. The problem is formulated from an optimal control perspective and solved using an iterative method. The convergence of the iteration and the stability of the control law are proven in theory. Simulations validate theoretical conclusions and demonstrate the effectiveness of the proposed method.     

Enzyme classification using multiclass support vector machine and feature subset selection

Proteins are the macromolecules responsible for almost all biological processes in a cell. With the availability of large number of protein sequences from different sequencing projects, the challenge with the scientist is to characterize their functions. As the wet lab methods are time consuming and expensive, many computational methods such as FASTA, PSI-BLAST, DNA microarray clustering, and Nearest Neighborhood classification on protein–protein interaction network have been proposed. Support vector machine is one such method that has been used successfully for several problems such as protein fold recognition, protein structure prediction etc. Cai et al. in 2003 have used SVM for classifying proteins into different functional classes and to predict their function. They used the physico-chemical properties of proteins to represent the protein sequences. In this paper a model comprising of feature subset selection followed by multiclass Support Vector Machine is proposed to determine the functional class of a newly generated protein sequence. To train and test the model for its performance, 32 physico-chemical properties of enzymes from 6 enzyme classes are considered. To determine the features that contribute significantly for functional classification, Sequential Forward Floating Selection (SFFS), Orthogonal Forward Selection (OFS), and SVM Recursive Feature Elimination (SVM-RFE) algorithms are used and it is observed that out of 32 properties considered initially, only 20 features are sufficient to classify the proteins into its functional classes with an accuracy ranging from 91% to 94%. On comparison it is seen that, OFS followed by SVM performs better than other methods. Our model generalizes the existing model to include multiclass classification and to identify most significant features affecting the protein function.     

Non-conductive nanomaterial enhanced electrochemical response in stripping voltammetry: The use of nanostructured magnesium silicate hollow spheres for heavy metal ions detection

Nanostructured magnesium silicate hollow spheres, one kind of non-conductive nanomaterials, were used in heavy metal ions (HMIs) detection with enhanced performance for the first time. The detailed study of the enhancing electrochemical response in stripping voltammetry for simultaneous detection of ultratrace Cd²⁺, Pb²⁺, Cu²⁺ and Hg²⁺ was described. Electrochemical properties of modified electrodes were characterized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The operational parameters which have influence on the deposition and stripping of metal ions, such as supporting electrolytes, pH value, and deposition time were carefully studied. The anodic stripping voltammetric performance toward HMIs was evaluated using square wave anodic stripping voltammetry (SWASV) analysis. The detection limits achieved (0.186 nM, 0.247 nM, 0.169 nM and 0.375 nM for Cd²⁺, Pb²⁺, Cu²⁺ and Hg²⁺) are much lower than the guideline values in drinking water given by the World Health Organization (WHO). In addition, the interference and stability of the modified electrode were also investigated under the optimized conditions. An interesting phenomenon of mutual interference between different metal ions was observed. Most importantly, the sensitivity of Pb²⁺ increased in the presence of certain concentrations of other metal ions, such as Cd²⁺, Cu²⁺ and Hg²⁺ both individually and simultaneously. The proposed electrochemical sensing method is thus expected to open new opportunities to broaden the use of SWASV in analysis for detecting HMIs in the environment.     

氟化改性TiO2空心微球的制备及光催化性能

以钛酸四丁酯为钛源、氢氟酸为氟源、乙醇为溶剂,采用溶剂热法合成了氟化改性的TiO2空心微球,并利用XRD、FESEM、FTIR、XPS等手段对氟化TiO2微球的晶体结构、形貌、分子基团以及元素形态等性质进行了表征,同时将TiO2微球应用于光催化降解甲基橙溶液。结果表明:氟化TiO2空心微球由奥斯瓦尔德熟化过程获得,其中TiO2以锐钛矿存在,氟以化学吸附态存在于TiO2的表面,形成≡Ti-F基团。相比纯TiO2,氟化TiO2空心微球光催化活性有很大提高,对初始浓度为20 mg·L-1的甲基橙溶液进行光催化降解30 min,其降解率达到98%。氟化改性TiO2空心微球光催化活性的提高是源于TiO2独特的空心微球结构以及TiO2表面≡Ti-F基团的存在。TiO2表面≡Ti-F基团有很强的吸电子能力,抑制了光生电子与空穴的复合,同时有利于羟基自由基的产生。     

A novel ultrathin single-crystalline Bi2O3 nanosheet wrapped by reduced graphene oxide with improved electron transfer for Li storage

Journal of Solid State Electrochemistry - Two-dimensional (2D) metal oxides have been of great interest owning to their particular properties such as rapid electron transfer capability. Herein, we...     

A mended hybrid learning algorithm for radial basis function neural networks to improve generalization capability

A two-step learning scheme for radial basis function neural networks, which combines the genetic algorithm (GA) with the hybrid learning algorithm (HLA), is proposed in this paper. It is compared with the methods of the GA, the recursive orthogonal least square algorithm (ROLSA) and another two-step learning scheme for RBF neural networks, which combines the K-means clustering with the HLA (K-means + HLA). Our proposed method has the best generalization performance.     

A surface functional monomer-directing strategy for highly dense imprinting of TNT at surface of silica nanoparticles

This paper reports a surface functional monomer-directing strategy for the highly dense imprinting of 2,4,6-trinitrotoluene (TNT) molecules at the surface of silica nanoparticles. It has been demonstrated that the vinyl functional monomer layer of the silica surface can not only direct the selective occurrence of imprinting polymerization at the surface of silica through the copolymerization of vinyl end groups with functional monomers, but also drive TNT templates into the formed polymer shells through the charge-transfer complexing interactions between TNT and the functional monomer layer. The two basic processes lead to the formation of uniform core-shell TNT-imprinted nanoparticles with a controllable shell thickness and a high density of effective recognition sites. The high capacity and fast kinetics to uptake TNT molecules show that the density of effective imprinted sites in the nanoshells is nearly 5 times that of traditional imprinted particles. A critical value of shell thickness for the maximum rebinding capacity was determined by testing the evolution of rebinding capacity with shell thickness, which provides new insights into the effectiveness of molecular imprinting and the form of imprinted materials. These results reported here not only can find many applications in molecularly imprinting techniques but also can form the basis of a new strategy for preparing various polymer-coating layers on silica support.