Dataset Information

Autocatalytic oxidization of nanosilver and its application to spectral analysis.

ABSTRACT: The stable yellow nanosilver (AgNP) and blue nanosilver (AgNPB) sols were prepared by the NaBH4 procedure. The new nanocatalytic reaction of AgNP-NaCl-H2O2 was investigated by surface plasmon resonance (SPR) absorption, resonance Rayleigh scattering (RRS), surface-enhanced Raman scattering (SERS) and scanning electron microscope (SEM) techniques. The autocatalytic oxidization of Ag on AgNP surface by H2O2 was observed firstly and the AgNP/AgCl nanoparticles were characterized. The [Ag(+)] from AgNP is different to the Ag(+) from AgNO3 that adsorb on the AgNP surface. An autocatalytic oxidization mechanism was proposed to explain experimental phenomena. The relationship between the SPR absorption peaks and the RRS peaks of AgNPB was studied, and three characteristic RRS peaks called as out-of-plane quadrupole, out-of-plane dipole and in-plane dipole RRS peaks were observed firstly. Using AgNP as nanoprobe, a simple, sensitive and selective RRS method was developed for assay of H2O2 in the range of 2.0 × 10(-8)-8.0 × 10(-5) mol/L.

SUBMITTER: Wen G

PROVIDER: S-EPMC3913915 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Autocatalytic oxidization of nanosilver and its application to spectral analysis.

Wen Guiqing G Luo Yanghe Y Liang Aihui A Jiang Zhiliang Z

Scientific reports 20140205

The stable yellow nanosilver (AgNP) and blue nanosilver (AgNPB) sols were prepared by the NaBH4 procedure. The new nanocatalytic reaction of AgNP-NaCl-H2O2 was investigated by surface plasmon resonance (SPR) absorption, resonance Rayleigh scattering (RRS), surface-enhanced Raman scattering (SERS) and scanning electron microscope (SEM) techniques. The autocatalytic oxidization of Ag on AgNP surface by H2O2 was observed firstly and the AgNP/AgCl nanoparticles were characterized. The [Ag(+)] from A ...[more]

PMID: 24496486

Similar Datasets

Project description:BackgroundGraph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks.ResultsAvailable both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors).ConclusionSpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Project description:Fusarium wilt of tomato (Lycopersicon esculentum) caused by Fusarium oxysporum f. sp. lycopersici is one of the most important diseases that affect this crop worldwide. This study aimed to biosynthesise nanosilver (AgNPs) using Chaetomium globosum, to evaluate its in vitro antifungal activity against pathogenic F. oxysporum and in vivo control of tomato seedlings wilt in the greenhouse. AgNPs was tested for its in vitro antifungal potential against F. oxysporum using poisoned food technique on three different growth media, agar well diffusion assay, inhibition of colony formation (CFU), and tested for its potency to control seedlings wilt upon its use at different concentrations (50, 100 and 500 mg/l) and for different incubation periods (0, 1, 2 and 4 h). Results indicated that C. globosum succeeded to biosynthesise AgNPs with maximum UV/vis absorbance around 420–450 nm, spherical in shape with particle size of 11–14 nm according to Transmittance electron microscope and displayed high purity recorded through X‐ray diffraction (XRD). In vitro studies revealed high antifungal activity of AgNPs against F. oxysporum noticed especially at a concentration of 500 mg/l and after incubation period for 4 h. The CFU of F. oxysporum on potato dextrose agar (PDA) medium decreased significantly on increasing the concentration and time of incubation with AgNPs. In the greenhouse, AgNPs caused appreciable enhancement in the growth parameters of tomato seedlings such as; root, shoot fresh weight, and height of seedlings in soil infested with F. oxysporum compared with the control. In addition, AgNPs reduced the severity of wilt disease by 90% observed through decreasing the number of wilted seedlings especially after placing their roots in 500 mg/l of AgNPs suspension for 4 h prior to soil infestation with the pathogen. This study recorded for the first time that C. globosum has the ability to synthesise AgNPs which showed significant in vivo antifungal potential observed through control of Fusarium wilt of tomato seedlings, in addition to enhancing their growth parameters in the greenhouse.

Project description:Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Dataset Information

Autocatalytic oxidization of nanosilver and its application to spectral analysis.

Publications

Autocatalytic oxidization of nanosilver and its application to spectral analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets