Synthesis of talc/Fe3O4 magnetic nanocomposites using chemical co-precipitation method

Katayoon Kalantari,1 Mansor Bin Ahmad,1,* Kamyar Shameli,1,2,* Roshanak Khandanlou11Department of Chemistry, Universiti Putra Malaysia, Serdang, Malaysia; 2Nanotechnology and Advance Materials Department, Materials and Energy Research Center, Karaj, Alborz, Karaj, Iran*These authors contributed equally to this workAbstract: The aim of this research was to synthesize and develop a new method for the preparation of iron oxide (Fe3O4) nanoparticles on talc layers using an environmentally friendly process. The Fe3O4 magnetic nanoparticles were synthesized using the chemical co-precipitation method on the exterior surface layer of talc mineral as a solid substrate. Ferric chloride, ferrous chloride, and sodium hydroxide were used as the Fe3O4 precursor and reducing agent in talc. The talc was suspended in deionized water, and then ferrous and ferric ions were added to this solution and stirred. After the absorption of ions on the exterior surface of talc layers, the ions were reduced with sodium hydroxide. The reaction was carried out under a nonoxidizing oxygen-free environment. There were not many changes in the interlamellar space limits (d-spacing = 0.94–0.93 nm); therefore, Fe3O4 nanoparticles formed on the exterior surface of talc, with an average size of 1.95–2.59 nm in diameter. Nanoparticles were characterized using different methods, including powder X-ray diffraction, transmission electron microscopy, emission scanning electron microscopy, energy dispersive X-ray spectroscopy, and Fourier transform infrared spectroscopy. These talc/Fe3O4 nanocomposites may have potential applications in the chemical and biological industries.Keywords: nanocomposites, Fe3O4 nanoparticles, talc, powder X-ray diffraction, scanning electron microscop

Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN)

The artificial neural network (ANN) modeling of adsorption of Pb(II) and Cu(II) was carried out for determination of the optimum values of the variables to get the maximum removal efficiency. The input variables were initial ion concentration, adsorbent dosage, and removal time, while the removal efficiency was considered as output. The performed experiments were designed into two data sets including training, and testing sets. To acquire the optimum topologies, ANN was trained by quick propagation (QP), Batch Back Propagation (BBP), Incremental Back Propagation (IBP), genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were defined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the IBP-3-9-2 was selected as the optimized topologies for heavy metal removal, due to the minimum RMSE and maximum R-squared

Optimization of process parameters for rapid adsorption of Pb(II), Ni(II), and Cu(II) by magnetic/talc nanocomposite using wavelet neural network

Artificial neural networks have been widely used to solve problems because of their reliable, robust, and salient characteristics in capturing nonlinear relationships between variables in complex systems. In this study, a wavelet neural network (WNN) based on the incremental backpropagation (IBP) algorithm was used in conjunction with an experimental design. To optimize the network, independent variables including ion concentration, adsorbent dose, and removal time were used as input parameters, while the removal percentage of Pb(II), Ni(II), and Cu(II) by magnetic/talc nanocomposite were selected as outputs. The network was trained by the IBP and four other algorithms as a model. To determine the number of hidden-layer nodes in the model, the root-mean-square error of a testing set was minimized. After minimizing this error, the topologies of the algorithms were compared based on the coefficient of determination and absolute average deviation. This comparison indicated that the IBP algorithm had the minimum root-mean-square error and absolute average deviation, and maximum coefficient of determination, for the test dataset. The importance values included 35.16 % for initial ion concentration, 32.74 % for adsorbent dose, and 32.11 % for removal time, showing that none of these were negligible. These results show that the WNN has great potential ability for prediction of removal of heavy-metal ions from aqueous solution with residual standard error less than 1.2 %

Ultrasmall superparamagnetic Fe3O4 nanoparticles: honey-based green and facile synthesis and in vitro viability assay

Elisa Rasouli,1 Wan Jeffrey Basirun,2 Majid Rezayi,3,4 Kamyar Shameli,5 Esmail Nourmohammadi,6 Roshanak Khandanlou,7 Zahra Izadiyan,5 Hoda Khoshdel Sarkarizi8 1Nanotechnology & Catalysis Research Centre, Institute of Postgraduate Studies, University of Malaya, Kuala Lumpur, Malaysia; 2Department of Chemistry, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia; 3Medical Toxicology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; 4Department of Modern Sciences and Technologies, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; 5Malaysia-Japan International Institute of Technology, University Technology Malaysia, Kuala Lumpur, Malaysia; 6Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; 7School of Psychological and Clinical Sciences, Faculty of Engineering, Health, Science and the Environment, Charles Darwin University, Darwin, NT, Australia; 8Department of Anatomical Sciences and Cell Biology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Introduction: In the present research, we report a quick and green synthesis of magnetite nanoparticles (Fe3O4-NPs) in aqueous solution using ferric and ferrous chloride, with different percentages of natural honey (0.5%, 1.0%, 3.0% and 5.0% w/v) as the precursors, stabilizer, reducing and capping agent, respectively. The effect of the stabilizer on the magnetic properties and size of Fe3O4-NPs was also studied. Methods: The nanoparticles were characterized by X-ray diffraction (XRD) analysis, field emission scanning electron microscopy, energy dispersive X-ray fluorescence, transmission electron microscopy (TEM), vibrating sample magnetometry (VSM) and Fourier transform infrared spectroscopy. Results: The XRD analysis indicated the presence of pure Fe3O4-NPs while the TEM images indicated that the Fe3O4-NPs are spherical with a diameter range between 3.21 and 2.22 nm. The VSM study demonstrated that the magnetic properties were enhanced with the decrease in the percentage of honey. In vitro viability evaluation of Fe3O4-NPs performed by using the MTT assay on the WEHI164 cells demonstrated no significant toxicity in higher concentration up to 140.0 ppm, which allows them to be used in some biological applications such as drug delivery. Conclusion: The presented synthesis method can be used for the controlled synthesis of Fe3O4-NPs, which could be found to be important in applications in biotechnology, biosensor and biomedicine, magnetic resonance imaging and catalysis. Keywords: honey, Fe3O4 nanoparticles, green synthesis, transmission electron microscopy, magnetic properties, in vitro, viability, MTT assay, WEHI164 cell