Fabrication of Co-doped ZnO nanorods for spintronic devices

Herein, single-crystalline Zn_1?xCo_xO (0.0≤x≤0.10) nanorods were prepared using a facile microwave irradiation method. Structural analyses by X-ray diffraction and transmission electron microscopy revealed the incorporation of Co²⁺ in the lattice position of Zn²⁺ ions into the ZnO matrix. Field emission scanning electron microscopy and TEM micrographs revealed that the length and diameter of the undoped ZnO nanorods were about ～2 μm and ～200 nm, respectively. For Co-doped ZnO, the length and diameter were found to increase with an increase of Co doping. The selected area electron diffraction pattern indicated that the Zn_1?xCo_xO (0.0≤x≤0.10) nanorods had a single phase nature with the preferential growth direction along the [0 0 1] plane. Raman scattering spectra confirmed the shift of the E ₂ ^high mode toward a lower wave number, suggested successful doping of Co ions at Zn site into the ZnO. Magnetic studies showed that Co doped ZnO nanorods exhibited room temperature ferromagnetism and the magnetization value increased with an increase in Co doping. The synthesis method presented here is a simple approach to prepare ZnO based diluted magnetic semiconductors nanostructures for practical application to spintronic devices.     

Facile synthesis of single-crystalline rutile TiO2 nano-rods by solution method

A convenient and scalable technique for the synthesis of rutile titanium dioxide (TiO₂) nano-rods was presented by using bulk TiO₂ powder, sodium hydroxide (NaOH) and distilled water as raw materials. X-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM) studies indicate that the prepared sample is crystalline and free from any impurities, however, it has no distinct shape and possesses a huge degree of agglomeration, and the average crystal size is around 40 nm. After annealing the sample at 600 °C for 2 h, it is observed through FESEM that nano-rods are formed. And XRD analysis shows that the nano-rods are single crystalline with distinct and smooth surfaces in different sizes with average length of about 1 µm and diameter of about 80 nm. Further UV-visible spectroscopy and Raman studies were conducted for the prepared sample and the band gap of the final product is found to be 3.40 eV.     

Inorganic Enzyme Mimics

Enzyme mimics (or artificial enzymes) have emerged as valuable alternatives to natural enzymes since the pioneering work of Ronald Breslow. They have numerous advantages over natural enzymes, such as high stability, low cost, and tailorable activity. Among varieties of materials explored to mimic enzymes, the inorganic ones, including inorganic complexes and nanomaterials, have attracted increasing interest over the last decade and have the potential to address the current challenges in energy, environment, health, etc.     

Structural,magnetic and electronic structure properties of pure and Ti doped Mg0.95Mn0.05Fe2O4 nanocrystalline thin films

Thin films of pure and Ti doped Mg_0.95Mn_0.05Fe₂O₄ deposited using pulsed laser deposition technique, have been characterized using X-ray diffraction, Raman spectroscopy, dc magnetization, atomic force microscopy, magnetic force microscopy and near edge X-ray absorption fine structure spectroscopy measurements. X-ray diffraction and Raman spectroscopy measurements indicate that both the films have single phase and the polycrystalline behavior with FCC structure. The grain size calculated using XRD data was 18 and 27 nm for pure and Ti doped films, respectively. Magnetic measurements reflect that pure film has superparamagnetic behavior while Ti doped film has soft ferrimagnetic behavior at room temperature. Atomic force microscopy measurements indicate that both the films are nanocrystalline in nature. Near edge X-ray absorption fine structure spectroscopy measurements clearly infer that Fe ions are in mixed valence state.     

Spider-net within the N6, PVA and PU electrospun nanofiber mats using salt addition: Novel strategy in the electrospinning process

Electrospun nanofibers are promising candidates in the nanotechnological applications due to the advantages of the nanofibrous morphology. Therefore, many attempts were reported to modify the electrospun mats to gain more beneficial properties. In the present study, we are introducing a strategy to synthesize electrospun polymeric nanofiber mats containing spider-net binding the main nanofibers. Addition with long stirring time of a metallic salt having tendency to ionize rather than formation of sol–gel in the host polymer solution reveals to synthesize a spider-net within the electrospun nanofibers of the utilized polymer. Nylon6, polyurethane and poly(vinyl alcohol) have been utilized; NaCl, KBr, CaCl₂ and H₂PtCl₆ have been added to the polymeric solutions. In the case of nylon6 and poly(vinyl alcohol), addition of the inorganic salts resulted in the formation of multi-layers spider-network within the electrospun nanofibers mats. The synthesized spider-nets were almost independent on the nature of the salt; the optimum salt concentration was 1.5 wt%. The metallic acid led to form trivial spider-nets within both of nylon6 and poly(vinyl alcohol) nanofibers. In a case of polyurethane, few spider-nets were formed after salt addition due to the low polarity of the utilized solvents. According to TEM analysis, the synthesized spider-net consisted of joints; the later issued from the main nanofibers at Taylor's cone zone. The spider-net improved the mechanical properties and the wetability of the nylon6 nanofiber mats, accordingly a mat having amphiphilic feature has been prepared.     

Computational design of structured chemical products

In chemical product design, the aim is to formulate a product with desired performance. Ingredients and internal product structure are two key drivers of product performance with direct impact on the mechanical, electrical, and thermal properties. Thus, there is a keen interest in elucidating the dependence of product performance on ingredients, structure, and the manufacturing process to form the structure. Design of product structure, particularly microstructure, is an intrinsically complex problem that involves different phases of different physicochemical properties, mass fraction, morphology, size distribution, and interconnectivity. Recently, computational methods have emerged that assist systematic microstructure quantification and prediction. The objective of this paper is to review these computational methods and to show how these methods as well as other developments in product design can work seamlessly in a proposed performance, ingredients, structure, and manufacturing process framework for the design of structured chemical products. It begins with the desired target properties and key ingredients. This is followed by computation for microstructure and then selection of processing steps to realize this microstructure. The framework is illustrated with the design of nanodielectric and die attach adhesive products.     

Enhanced therapeutic efficacy of clotrimazole by delivery through poly(ethylene oxide)-block-poly(ε-caprolactone) copolymer-based micelles

Amphiphilic block copolymers have been the subject of great scientific interests due to their applications in various fields including nano drug delivery. Three amphiphilic block copolymers based on poly(ε-caprolactone) as a hydrophobic segment and methoxy poly(ethylene oxide) ( as a hydrophilic part were synthesized by the ring-opening polymerization of ε-caprolactone using MeO-PEO_5K as macroinitiator by varying initial feed ratios. The synthesized polymers were further explored for their drug delivery potential using clotrimazole as model hydrophobic drug. Drug-loaded micelles were characterized for shape, size, drug encapsulation efficiency, in vitro release, and thermal stability using atomic force microscope, zetasizer, UV–visible spectrophotometry, FTIR, differential scanning calorimetry, and thermogravimetric analysis. Clotrimazole loaded in micelles were also investigated for its antifungal activity through an in vitro assay and scanning electron microscopy. The antifungal activity of drug increased significantly by delivering through polymeric micelles. Current study provides insight into different factors that can be maneuvered to achieve a variety of desired properties of micelles for improved therapeutic efficacy of drugs like clotrimazole. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47769.     

Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine

The fast-paced growth of artificial intelligence provides unparalleled opportunities to improve the efficiency of various industries, including the transportation sector. The worldwide transport departments face many obstacles following the implementation and integration of different vehicle features. One of these tasks is to ensure that vehicles are autonomous, intelligent and able to grow their repository of information. Machine learning has recently been implemented in wireless networks, as a major artificial intelligence branch, to solve historically challenging problems through a data-driven approach. In this article, we discuss recent progress of applying machine learning into vehicle networks for intelligent route decision and try to focus on this emerging field. Deep Extreme Learning Machine (DELM) framework is introduced in this article to be incorporated in vehicles so they can take human-like assessments. The present GPS compatibility issues make it difficult for vehicles to take real-time decisions under certain conditions. It leads to the concept of vehicle controller making self-decisions. The proposed DELM based system for self-intelligent vehicle decision makes use of the cognitive memory to store route observations. This overcomes inadequacy of the current in-vehicle route-finding technology and its support. All the relevant route-related information for the ride will be provided to the user based on its availability. Using the DELM method, a high degree of precision in smart decision taking with a minimal error rate is obtained. During investigation, it has been observed that proposed framework has the highest accuracy rate with 70% of training (1435 samples) and 30% of validation (612 samples). Simulation results validate the intelligent prediction of the proposed method with 98.88%, 98.2% accuracy during training and validation respectively.     

Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources. A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model. The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.