Parvaneh Shabanzadeh

Synthesis and Characterization of Polyethylene Glycol Mediated Silver Nanoparticles by the Green Method

The roles of green chemistry in nanotechnology and nanoscience fields are very significant in the synthesis of diverse nanomaterials. Herein, we report a green chemistry method for synthesized colloidal silver nanoparticles (Ag NPs) in polymeric media. The colloidal Ag NPs were synthesized in an aqueous solution using silver nitrate, polyethylene glycol (PEG), and β-D-glucose as a silver precursor, stabilizer, and reducing agent, respectively. The properties of synthesized colloidal Ag NPs were studied at different reaction times. The ultraviolet-visible spectra were in excellent agreement with the obtained nanostructure studies performed by transmission electron microscopy (TEM) and their size distributions. The Ag NPs were characterized by utilizing X-ray diffraction (XRD), zeta potential measurements and Fourier transform infrared (FT-IR). The use of green chemistry reagents, such as glucose, provides green and economic features to this work.

Investigation of antibacterial properties silver nanoparticles prepared via green method

BackgroundThis study aims to investigate the influence of different stirring times on antibacterial activity of silver nanoparticles in polyethylene glycol (PEG) suspension. The silver nanoparticles (Ag-NPs) were prepared by green synthesis method using green agents, polyethylene glycol (PEG) under moderate temperature at different stirring times. Silver nitrate (AgNO3) was taken as the metal precursor while PEG was used as the solid support and polymeric stabilizer. The antibacterial activity of different sizes of nanosilver was investigated against Gram–positive [Staphylococcus aureus] and Gram–negative bacteria [Salmonella typhimurium SL1344] by the disk diffusion method using Müeller–Hinton Agar.ResultsFormation of Ag-NPs was determined by UV–vis spectroscopy where surface plasmon absorption maxima can be observed at 412–437 nm from the UV–vis spectrum. The synthesized nanoparticles were also characterized by X-ray diffraction (XRD). The peaks in the XRD pattern confirmed that the Ag-NPs possessed a face-centered cubic and peaks of contaminated crystalline phases were unable to be located. Transmission electron microscopy (TEM) revealed that Ag-NPs synthesized were in spherical shape. The optimum stirring time to synthesize smallest particle size was 6 hours with mean diameter of 11.23 nm. Zeta potential results indicate that the stability of the Ag-NPs is increases at the 6 h stirring time of reaction. The Fourier transform infrared (FT-IR) spectrum suggested the complexation present between PEG and Ag-NPs. The Ag-NPs in PEG were effective against all bacteria tested. Higher antibacterial activity was observed for Ag-NPs with smaller size. These suggest that Ag-NPs can be employed as an effective bacteria inhibitor and can be applied in medical field.ConclusionsAg-NPs were successfully synthesized in PEG suspension under moderate temperature at different stirring times. The study clearly showed that the Ag-NPs with different stirring times exhibit inhibition towards the tested gram-positive and gram-negative bacteria.

Synthesis and characterization of silver/montmorillonite/chitosan bionanocomposites by chemical reduction method and their antibacterial activity

Silver nanoparticles (AgNPs) of a small size were successfully synthesized using the wet chemical reduction method into the lamellar space layer of montmorillonite/chitosan (MMT/Cts) as an organomodified mineral solid support in the absence of any heat treatment. AgNO3, MMT, Cts, and NaBH4 were used as the silver precursor, the solid support, the natural polymeric stabilizer, and the chemical reduction agent, respectively. MMT was suspended in aqueous AgNO3/Cts solution. The interlamellar space limits were changed (d-spacing = 1.24–1.54 nm); therefore, AgNPs formed on the interlayer and external surface of MMT/Cts with d-average = 6.28–9.84 nm diameter. Characterizations were done using different methods, ie, ultraviolet-visible spectroscopy, powder X-ray diffraction, transmission electron microscopy, scanning electron microscopy, energy dispersive X-ray fluorescence spectrometry, and Fourier transform infrared spectroscopy. Silver/montmorillonite/chitosan bionanocomposite (Ag/MMT/Cts BNC) systems were examined. The antibacterial activity of AgNPs in MMT/Cts was investigated against Gram-positive bacteria, ie, Staphylococcus aureus and methicillin-resistant S. aureus and Gram-negative bacteria, ie, Escherichia coli, E. coli O157:H7, and Pseudomonas aeruginosa by the disc diffusion method using Mueller Hinton agar at different sizes of AgNPs. All of the synthesized Ag/MMT/Cts BNCs were found to have high antibacterial activity. These results show that Ag/MMT/Cts BNCs can be useful in different biological research and biomedical applications, including surgical devices and drug delivery vehicles.

Green Biosynthesis of Silver Nanoparticles Using Callicarpa maingayi Stem Bark Extraction

Different biological methods are gaining recognition for the production of silver nanoparticles (Ag-NPs) due to their multiple applications. The use of plants in the green synthesis of nanoparticles emerges as a cost effective and eco-friendly approach. In this study the green biosynthesis of silver nanoparticles using Callicarpa maingayi stem bark extract has been reported. Characterizations of nanoparticles were done using different methods, which include; ultraviolet-visible spectroscopy (UV-Vis), powder X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy dispersive X-ray fluorescence (EDXF) spectrometry, zeta potential measurements and Fourier transform infrared (FT-IR) spectroscopy. UV-visible spectrum of the aqueous medium containing silver nanoparticles showed absorption peak at around 456 nm. The TEM study showed that mean diameter and standard deviation for the formation of silver nanoparticles were 12.40 ± 3.27 nm. The XRD study showed that the particles are crystalline in nature, with a face centered cubic (fcc) structure. The most needed outcome of this work will be the development of value added products from Callicarpa maingayi for biomedical and nanotechnology based industries.

Green biosynthesis of silver nanoparticles using Curcuma longa tuber powder

Green synthesis of noble metal nanoparticles is a vastly developing area of research. Metallic nanoparticles have received great attention from chemists, physicists, biologists, and engineers who wish to use them for the development of a new-generation of nanodevices. In this study, silver nanoparticles were biosynthesized from aqueous silver nitrate through a simple and eco-friendly route using Curcuma longa tuber-powder extracts, which acted as a reductant and stabilizer simultaneously. Characterizations of nanoparticles were done using different methods, which included ultraviolet-visible spectroscopy, powder X-ray diffraction, transmission electron microscopy, scanning electron microscopy, energy-dispersive X-ray fluorescence spectrometry, and Fourier-transform infrared spectroscopy. The ultraviolet-visible spectrum of the aqueous medium containing silver nanoparticles showed an absorption peak at around 415 nm. Transmission electron microscopy showed that mean diameter and standard deviation for the formation of silver nanoparticles was 6.30 ± 2.64 nm. Powder X-ray diffraction showed that the particles are crystalline in nature, with a face-centered cubic structure. The most needed outcome of this work will be the development of value-added products from C. longa for biomedical and nanotechnology-based industries.

Prediction of silver nanoparticles' diameter in montmorillonite/chitosan bionanocomposites by using artificial neural networks

Artificial neural networks (ANNs) are computational tools that have found comprehensive utilization in solving many complex real world problems. Major benefits in using ANNs are their remarkable information-processing characteristics pertinent mainly to high parallelism, nonlinearity, fault and noise tolerance, and learning and generalization capabilities. An ANN approach is used to model the size of silver nanoparticles (Ag-NPs) in montmorillonite/chitosan bionanocomposites layers as a function of the silver nitrate concentration, reaction of temperature, chitosan percentage, and d-spacing of clay layers. The best ANN model is found and this final model is capable of predicting the size of nanosilver for a wide range of conditions with a mean absolute error of less than 0.004 and a regression error of about 1. Results obtained showed good ability predictive of neural network model for the prediction of the size of Ag-NPs in chemical reduction methods.

Artificial Intelligence in Numerical Modeling of Silver Nanoparticles Prepared in Montmorillonite Interlayer Space

Artificial neural network (ANN) models have the capacity to eliminate the need for expensive experimental investigation in various areas of manufacturing processes, including the casting methods. An understanding of the interrelationships between input variables is essential for interpreting the sensitivity data and optimizing the design parameters. Silver nanoparticles (Ag-NPs) have attracted considerable attention for chemical, physical, and medical applications due to their exceptional properties. The nanocrystal silver was synthesized into an interlamellar space of montmorillonite by using the chemical reduction technique. The method has an advantage of size control which is essential in nanometals synthesis. Silver nanoparticles with nanosize and devoid of aggregation are favorable for several properties. In this investigation, the accuracy of artificial neural network training algorithm was applied in studying the effects of different parameters on the particles, including the AgNO3 concentration, reaction temperature, UV-visible wavelength, and montmorillonite (MMT) d-spacing on the prediction of size of silver nanoparticles. Analysis of the variance showed that the AgNO3 concentration and temperature were the most significant factors affecting the size of silver nanoparticles. Using the best performing artificial neural network, the optimum conditions predicted were a concentration of AgNO3 of 1.0 (M), MMT d-spacing of 1.27 nm, reaction temperature of 27°C, and wavelength of 397.50 nm.

Reflection and Transmission Coefficient of Yttrium Iron Garnet Filled Polyvinylidene Fluoride Composite Using Rectangular Waveguide at Microwave Frequencies

The sol-gel method was carried out to synthesize nanosized Yttrium Iron Garnet (YIG). The nanomaterials with ferrite structure were heat-treated at different temperatures from 500 to 1000 °C. The phase identification, morphology and functional groups of the prepared samples were characterized by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR), respectively. The YIG ferrite nanopowder was composited with polyvinylidene fluoride (PVDF) by a solution casting method. The magnitudes of reflection and transmission coefficients of PVDF/YIG containing 6, 10 and 13% YIG, respectively, were measured using rectangular waveguide in conjunction with a microwave vector network analyzer (VNA) in X-band frequencies. The results indicate that the presence of YIG in polymer composites causes an increase in reflection coefficient and decrease in transmission coefficient of the polymer.

A Derivative-Free Optimization Method for Solving Classification Problem

Problem statement: The aim of data classification is to establish rul es for the classification of some observations assuming that we have a database, which includes of at least two classes. There is a training set for each class. Those problems oc cur in a wide range of human activity. One of the most promising ways to data classification is based on methods of mathematical optimization. Approach: The problem of data classification was studied as a problem of global, nonsmooth and nonconvex optimization; this approach consists of d escribing clusters for the given training sets. The data vectors are assigned to the closest cluster an d correspondingly to the set, which contains this cluster and an algorithm based on a derivative-free method is applied to the solution of this problem. Results: Proposed method had been tested on real-world data sets. Results of numerical experiments had been presented which demonstrate the effectiveness of the proposed algorithm. Conclusion: In this study we had studied a derivative-free optimiz ation approach to the classification. For optimization generalized pattern search method has been applied. The results of numerical experiments allowed us to say the proposed algorithms are effec tive for solving classification problems at least f or databases considered in this study.