Microwave assisted synthesis and antimicrobial activity of Fe3O4-doped ZrO2 nanoparticles

Composites play important role in dental filling by controlling shrinkage along with correction in teeth's shape and position. Rehabilitation of severely worn dentition can be achieved using mechanically strong composites. This study aims to synthesize zirconia-based composites to be used as dental fillers. Effect of microwave powers (100–900?W) along with Fe₃O₄ doping are studied on the structural, mechanical and magnetic properties of stabilized zirconia. SEM and TEM reveal formation of spherical nanoparticles with diameter of ～30?nm. XRD results shows phase pure tetragonal zirconia (t-ZrO₂) at microwave power of 500?W without any post heat treatment. Crystallite size calculated from XRD data (～23?nm) matches well with the previously reported value for stabilization of t-ZrO₂. Microwave energy dissipation results in stresses causing volume shrinkage leading to monoclinic to tetragonal phase transformation with higher X-ray density and hardness of ～1347HV. VSM results show ferromagnetic response with low coercivity (600Oe) value and saturation magnetization (～2emu/g). It is worth mentioning here that this is one of its kind study reporting synthesis of room temperature stabilized Fe₃O₄ doped zirconia composites at microwave power of 500?W. Antibacterial studies reveal inhibition zone of ～32?mm against bacillus bacteria suggesting their potential use as dental filler.     

Synthesis and characterization of metal ion-imprinted polymers

In this study, ion-imprinted polymeric materials from an aqueous solution were prepared to remove metal ions \((\hbox {M}^{2+})\) like \(\hbox {Ni}^{2+}\), \(\hbox {Co}^{2+}\), etc. To prepare ion-imprinted polymers (IIPs), acrylic acid derivatives (XA) like acrylamide (AAm), methacrylic acid and itaconic acid were chosen as monomers. Metal ions formed binary chelate complexes with the help of 8-hydroxyquinoline (8-HQ). The solution containing functional monomers and the binary complex were polymerized with the help of various cross-linkers [ethylene glycol dimethylacrylate (EGDMA) or trimethylpropane trimethacrylate (TMPTMA)] and the target ion was removed from this polymer through an applicable method to produce IIPs. Furthermore, the control polymers (CPs) were synthesized through the same method without using metal ion. Characterization of the polymeric materials was investigated through spectroscopic (FTIR/ATR), thermal (TGA, DSC) and surface (SEM and XRD) analyses. Functional monomers, cross-linkers and the target ion’s species were changed during preparation of IIPs to observe their effect on imprinting of the target ion. Adsorption of the target ion to the prepared IIPs and the factors affecting the adsorption like concentration, pH, temperature, mass, etc. were studied.     

Simulation,Modeling, and Optimization of Intelligent Kidney Disease Predication Empowered with Computational Intelligence Approaches

Artificial intelligence (AI) is expanding its roots in medical diagnostics. Various acute and chronic diseases can be identified accurately at the initial level by using AI methods to prevent the progression of health complications. Kidney diseases are producing a high impact on global health and medical practitioners are suggested that the diagnosis at earlier stages is one of the foremost approaches to avert chronic kidney disease and renal failure. High blood pressure, diabetes mellitus, and glomerulonephritis are the root causes of kidney disease. Therefore, the present study is proposed a set of multiple techniques such as simulation, modeling, and optimization of intelligent kidney disease prediction (SMOIKD) which is based on computational intelligence approaches. Initially, seven parameters were used for the fuzzy logic system (FLS), and then twenty-five different attributes of the kidney dataset were used for the artificial neural network (ANN) and deep extreme machine learning (DEML). The expert system was proposed with the assistance of medical experts. For the quick and accurate evaluation of the proposed system, Matlab version 2019 was used. The proposed SMOIKD-FLS-ANN-DEML expert system has shown 94.16% accuracy. Hence this study concluded that SMOIKD-FLS-ANN-DEML system is effective to accurately diagnose kidney disease at initial levels.     

Selection of solar tracking system using extended TOPSIS technique with interval type-2 pythagorean fuzzy numbers

Optimization and Engineering - The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is considered among the most frequently used techniques to deal with multi-criteria group...     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Fast-forward solver for inhomogeneous media using machine learning methods: artificial neural network,support vector machine and fuzzy logic

Encountering with a nonlinear second-order differential equation including ϵ _r and μ _r spatial distributions, while computing the fields inside inhomogeneous media, persuaded us to find their known distributions that give exact solutions. Similarities between random distributions of electric properties and known functions lead us to estimate them using three mathematical tools of artificial neural networks (ANNs), support vector machines (SVMs) and Fuzzy Logic (FL). Assigning known functions after fitting with minimum error to arbitrary inputs using results of machine learning networks leads to achieve an approximate solution for the field inside materials considering boundary conditions. A comparative study between the methods according to the complexity of the structures as well as the accuracy and the calculation time for testing of unforeseen inputs, including classification, prediction and regression is presented. We examined the extracted pairs of ϵ _r and μ _r with ANN, SVM networks and FL and got satisfactory outputs with detailed results. The application of the presented method in zero reflection subjects is exemplified.

     

Thermally Resistive Electrospun Composite Membranes for Low‐Grade Thermal Energy Harvesting

In this work, thermally insulating composite mats of poly(vinylidene fluoride) (PVDF) and polyacrylonitrile (PAN) blends are used as the separator membranes. The membranes improve the thermal‐to‐electrical energy conversion efficiency of a thermally driven electrochemical cell (i.e., thermocell) up to 95%. The justification of the improved performance is an intricate relationship between the porosity, electrolyte uptake, electrolyte uptake rate of the electrospun fibrous mat, and the actual temperature gradient at the electrode surface. When the porosity is too high (87%) in PAN membranes, the electrolyte uptake and electrolyte uptake rate are significantly high as 950% and 0.53 µL s^?1, respectively. In such a case, the convective heat flow within the cell is high and the power density is limited to 32.7 mW m^?2. When the porosity is lesser (up to 81%) in PVDF membranes, the electrolyte uptake and uptake rate are relatively low as 434% and 0.13 µL s^?1, respectively. In this case, the convective flow shall be low, however, the maximum power density of 63.5 mW m^?2 is obtained with PVDF/PAN composites as the aforementioned parameters are optimized. Furthermore, multilayered membrane structures are also investigated for which a bilayered architecture produces highest power density of 102.7 mW m^?2.     

Industrial symbiosis: high purity recovery of metals from Waelz sintering waste by aqueous SO2 solution

Sintering operation in the production of Zn, Cd, and Pb by Waelz process produces a powdery waste containing mainly (about 70%) ZnO, CdO, and PbO. The waste may be referred to as Waelz sintering waste (WSW). The aim of this study is to develop a process for the separation and recovery of the metals from WSW with high purities. The process is based on the dissolution of the WSW in aqueous SO2 solution. The research reported here concentrated on the effect of some important operational parameters on dissolution process. The parameters investigated and their ranges were as follows: SO(2) gas flow rate (V); 38-590 ml/min, stirring speed (W); 100-1000 rpm, reaction temperature (T); 13-60 degrees C, reaction time (t); 1-16 min, and solid-liquid ratio (S/L); 0.1-0.5 g/ml. The results showed that the dissolution rate increased with increasing W, V, and S/L and decreasing T. The best dissolution conditions were found to be V=325 ml/min, W=600 rpm, t=6 min, T=21 degrees C, and S/L=0.1g/ml. Separation of Zn from Cd involved precipitation of ZnSO3 from a mixture solution. The best pH level for the precipitation was observed to be 6.     

In situ UV‐VIS diffuse reflectance spectroscopy of reduction–reoxidation of heteropoly compounds by methanol and ethanol: a correlation between spectroscopic and catalytic data

The degree of reduction/oxidation of H₄PVMo₁₁O₄₀ (HPA) and Cs₂H₂PVMo₁₁O₄₀ (CsPA) was studied during reduction and reoxidation by methanol, ethanol and mixtures with oxygen, respectively. The peak intensity of the intervalence charge transfer (IVCT) band, the apparent band gap energy (E _g ^* ) and catalytic data were obtained by in situ UV‐VIS diffuse reflectance spectrosccopy (UV‐VIS‐DRS) and on‐line gas chromatography (GC), respectively. The peak intensity of the IVCT band and E _g ^* increase during the reduction of heteropoly compounds by the alcohols. The spectroscopic and catalytic data (conversion, selectivity) correlate in the transient state during the reoxidation process. It is shown that isolated Keggin anions act as precursors for the active states of the catalysts, which molecular structure cannot be deduced from UV‐VIS spectroscopy alone. UV‐VIS spectroscopy, however, can serve as a tool to determine the degree of reduction in future combined in situ UV‐VIS/Raman/XRD studied. This revised version was published online in July 2006 with corrections to the Cover Date.