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Discovery of a novel anticancer drug delivery agent is important to replace conventional cancer therapies which are often accompanied by undesired side effects. This study demonstrated the synthesis of superparamagnetic magnetite nanocomposites (Fe3O4-NCs) using a green method. Montmorillonite (MMT) was used as matrix support, while Fe3O4 nanoparticles (NPs) and carrageenan (CR) were used as filler and stabilizer, respectively. The combination of these materials resulted in a novel nanocomposite (MMT/CR/Fe3O4-NCs). A series of characterization experiments was conducted. The purity of MMT/CR/Fe3O4-NCs was confirmed by X-ray diffraction (XRD) analysis. High resolution transmission electron microscopy (HRTEM) analysis revealed the uniform and spherical shape of Fe3O4 NPs with an average particle size of 9.3 ± 1.2 nm. Vibrating sample magnetometer (VSM) analysis showed an Ms value of 2.16 emu/g with negligible coercivity which confirmed the superparamagnetic properties. Protocatechuic acid (PCA) was loaded onto the MMT/CR/Fe3O4-NCs and a drug release study showed that 15% and 92% of PCA was released at pH 7.4 and 4.8, respectively. Cytotoxicity assays showed that both MMT/CR/Fe3O4-NCs and MMT/CR/Fe3O4-PCA effectively killed HCT116 which is a colorectal cancer cell line. Dose-dependent inhibition was seen and the killing was enhanced two-fold by the PCA-loaded NCs (IC50–0.734 mg/mL) compared to the unloaded NCs (IC50–1.5 mg/mL). This study highlights the potential use of MMT/CR/Fe3O4-NCs as a biologically active pH-responsive drug delivery agent. Further investigations are warranted to delineate the mechanism of cell entry and cancer cell killing as well as to improve the therapeutic potential of MMT/CR/Fe3O4-NCs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Modified rice straw/Fe3O4/polycaprolactone nanocomposites (ORS/Fe3O4/ PCL-NCs) have been prepared for the first time using a solution casting method. The RS/Fe3O4-NCs were modified with octadecylamine (ODA) as an organic modifier. The prepared NCs were characterized by using X-ray powder diffraction (XRD), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), Thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FT-IR). The XRD results showed that as the intensity of the peaks decreased with the increase of ORS/Fe3O4-NCs content in comparison with PCL peaks, the Fe3O4-NPs peaks increased from 1.0 to 60.0 wt. %. The TEM and SEM results showed a good dispersion of ORS/Fe3O4-NCs in the PCL matrix and the spherical shape of the NPs. The TGA analysis indicated thermal stability of ORS/Fe3O4-NCs increased after incorporation with PCL but the thermal stability of ORS/Fe3O4/PCL-NCs decreased with the increase of ORS/Fe3O4-NCs content. Tensile strength was improved with the addition of 5.0 wt. % of ORS/Fe3O4-NCs. The antibacterial activities of the ORS/Fe3Ü4/PCL-NC films were examined against Gram-negative bacteria (Escherichia coli) and Gram-positive bacteria (Staphylococcus aureus) by diffusion method using nutrient agar. The results indicated that ORS/Fe3O4/PCL-NC films possessed a strong antibacterial activity with the increase in the percentage of ORS/Fe3O4-NCs in the PCL. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 %. [ABSTRACT FROM AUTHOR]

Fe3O4/talc nanocomposite was used for removal of Cu(II), Ni(II), and Pb(II) ions from aqueous solutions. Experiments were designed by response surface methodology (RSM) and a quadratic model was used to predict the variables. The adsorption parameters such as adsorbent dosage, removal time, and initial ion concentration were used as the independent variables and their effects on heavy metal ion removal were investigated. Analysis of variance was incorporated to judge the adequacy of the models. Optimal conditions with initial heavy metal ion concentration of 100, 92 and 270 mg/L, 120 s of removal time and 0.12 g of adsorbent amount resulted in 72.15%, 50.23%, and 91.35% removal efficiency for Cu(II), Ni(II), and Pb(II), respectively. The predictions of the model were in good agreement with experimental results and the Fe3O4/talc nanocomposite was successfully used to remove heavy metals from aqueous solutions. [ABSTRACT FROM AUTHOR]