Spray-Coating of Superhydrophobic Coatings for Advanced Applications

Superhydrophobic coatings are widely applicable, e.g., as self-cleaning surfaces or water–oil separation membranes, yet their wider usage is impeded due to costs of fabrication, size, or substrate limitation. Spray-coating is a versatile coating procedures and might offer a good solution for the fabrication of these superhydrophobic coatings, due to the fact that coatings can be fabricated on various materials in a simple, fast, and inexpensive manner. Most procedures rely on hybrid coatings of hydrophobized nanoparticles and a polymeric matrix, which have several drawbacks including the easy loss of nanoparticles and difficult waste handling. Here, the fabrication of the superhydrophobic material, called Fluoropor, for the first time, by spray-coating on various substrates including metals, tissues, concrete, and glass is presented. It is fabricated by spray-coating a mixture of a highly fluorinated monomer blended with porogens followed by photopolymerization. The superhydrophobicity of the material relies on the porous structure on the micro-/nanoscale across the bulk material and does not require any nanoparticles. Excellent self-cleaning ability of these coatings, resistance against thermal and abrasive impact, and their application as oil–water separation membranes are shown. This versatile applicability is highly promising for real-world application as self-cleaning coatings or oil–water separating membranes.     

Green synthesis of ZnO and ZnO/CuO nanocomposites in <Emphasis Type="Italic">Mentha longifolia</Emphasis> leaf extract: characterization and their application as anti-bacterial agents

In this work, we fabricated ZnO and ZnO/CuO nanocomposites using Mentha longifolia leaf extract as a natural, non-toxic, and efficient stabilizer. Anti-bacterial activities of the prepared samples against two pathogenic bacteria, Escherichia coli (Gram-negative), and Staphylococcus aureus (Gram-positive) were investigated. The properties of the as-prepared samples were characterized by XRD, EDX, SEM, TEM, TGA, FT-IR, UV–Vis DRS, and BET instruments. The XRD analysis indicated that the size of crystallites was decreased for the ZnO powder prepared in the presence of the leaf extract. The SEM images showed that the samples consist of spherical shaped well-distributed particles. In addition, the presence of biomolecules from the leaf extract was revealed by EDX, TGA, FT-IR, and UV–Vis DRS analyses, which are important in biosynthesis process. The highest anti-bacterial activity belonged to the ZnO/CuO (10%) nanocomposite and the other compounds, including ZnO/CuO (5%), ZnO (ext), and ZnO (W) were in the next ranks, respectively. It was observed that the viability percentages against E. coli (10.16?±?2.2) is higher than that of S. aureus (17.1?±?0.87) in the presence of the ZnO/CuO (10%) nanocomposite. Ultimately, the mechanism for the action of the ZnO/CuO (10%) nanocomposite was explored through the SEM images, which involved the disruption of the bacterial membranes.     

Modulating protein adsorption onto hydroxyapatite particles using different amino acid treatments

Hydroxyapatite (HA) is a material of choice for bone grafts owing to its chemical and structural similarities to the mineral phase of hard tissues. The combination of osteogenic proteins with HA materials that carry and deliver the proteins to the bone-defective areas will accelerate bone regeneration. The study investigated the treatment of HA particles with different amino acids such as serine (Ser), asparagine (Asn), aspartic acid (Asp) and arginine (Arg) to enhance the adsorption ability of HA carrier for delivering therapeutic proteins to the body. The crystallinity of HA reduced when amino acids were added during HA preparation. Depending on the types of amino acid, the specific surface area of the amino acid-functionalized HA particles varied from 105 to 149 m² g^–1. Bovine serum albumin (BSA) and lysozyme were used as model proteins for adsorption study. The protein adsorption onto the surface of amino acid-functionalized HA depended on the polarities of HA particles, whereby, compared with lysozyme, BSA demonstrated higher affinity towards positively charged Arg-HA. Alternatively, the binding affinity of lysozyme onto the negatively charged Asp-HA was higher when compared with BSA. The BSA and lysozyme adsorptions onto the amino acid-functionalized HA fitted better into the Freundlich than Langmuir model. The amino acid-functionalized HA particles that had higher protein adsorption demonstrated a lower protein-release rate.     

A new approach for synthesis of well-crystallized Y zeolite from bentonite and rice husk ash used in Ni-Mo/Al2O3-Y hybrid nanocatalyst for hydrocracking of heavy oil

A series of Ni-Mo/Al₂O₃-Y hybrid nanocatalysts were synthesized for hydrocracking of heavy oil. The well crystallized Y zeolite was synthesized from mineral bentonite and rice husk ash by a two-step synthesis method. The solution combustion method was applied to develop a fast and simple technique for preparing of alumina-supported NiMo catalyst with high hydrodesulfurization activity. Such activity may be due to the morphological and textural modification as a consequence of the release of a high amount of exhaust gases during the combustion process. The XRD analysis revealed that the P zeolite was a competitive phase presented in the obtained product that could be eliminated using a two-step synthesis method. Compared to a one-step method, the pore volume and external surface area of the synthesized zeolite by the two-step method increased by 74 and 62%, respectively. The hydrocracking results illustrated that the synthesized zeolite was able to convert 66% of heavy oil to lighter products and reduce the viscosity up to 60%. Furthermore, the amount of sulfur removal was found to be 58%. The spent catalyst characterization suggested that the type of deposited coke was hard coke with the unsaturated aromatic ring which could be responsible for the pores blockage after the cracking reaction.     

Localization and quantification of drugs in animal tissues by use of desorption electrospray ionization mass spectrometry imaging

Mass spectrometric imaging (MSI) has emerged as a powerful technique to obtain spatial arrangement of individual molecular ions in animal tissues. Ambient desorption electrospray ionization (DESI) technique is uniquely suited for such imaging experiments, as it can be performed on animal tissues in their native environment without prior treatments. Although MSI has become a rapid growing technique for localization of proteins, lipids, drugs, and endogenous compounds in different tissues, quantification of imaged targets has not been explored extensively. Here we present a novel MSI approach for localization and quantification of drugs in animal thin tissue sections. DESI-MSI using an Orbitrap mass analyzer in full scan mode was performed on 6 μm coronal brain sections from rats that were administered 2.5 mg/kg clozapine. Clozapine was localized and quantified in individual brain sections 45 min postdose. External calibration curves were prepared by micropipetting standards with internal standard (IS) on top of the tissues, and average response factors were calculated for the scans in which both clozapine and IS were detected. All response factors were normalized to area units. Quantifications from DESI-MSI revealed 0.2-1.2 ng of clozapine in individual brain sections, results that were further confirmed by extraction and liquid chromatography/tandem mass spectrometry (LC/MS/MS) analysis.     

Healing of double oxide film defects in A356 aluminium melt

The possibility of the bonding of the two layers of a double oxide film defect when held in liquid A356 Al alloy was investigated. The defect was modelled experimentally by maintaining two aluminium oxide layers in contact with each other and the atmosphere trapped between them in the A356 liquid alloy for varying lengths of time of between 30 s and 48 h. Any changes in the composition and morphology of these layers were studied by SEM, EDX and XRD. The results showed that the two layers of a double oxide film defect might bond to each other by two different mechanisms, one during the transformation of Al₂O₃ to MgAl₂O₄ spinel in relatively short holding times, which would cause the layers to bond at several points and the other during the gradual transformation of spinel to MgO in longer times, which would cause strong bonding between the layers. It was shown that any bonding can take place essentially only after the oxygen and nitrogen of the atmosphere within the defect are consumed.     

Parametric study of automotive composite bumper beams subjected to low-velocity impacts

Fuel efficiency and emission gas regulations are the main causes for reducing the weight of passenger cars by using composite structures. Bumper beams are one of the main structures of passenger cars that protect them from front and rear collisions. In this paper, a commercial front bumper beam made of glass mat thermoplastic (GMT) is studied and characterized by impact modeling using LS-DYNA ANSYS 5.7 according to the E.C.E. UNITED NATIONS AGREEMENT [UNITED NATIONS AGREEMENT, Uniform Provisions concerning the Approval of Vehicles with regards to their Front and Rear Protective Devices (Bumpers, etc.), E.C.E., 1994]. Three main design factors for this structure: shape, material and impact conditions are studied and the results are compared with conventional metals like steel and aluminum. Finally the aforementioned factors are characterized by proposing a high strength SMC [Composite and metallic crash performance, Hawaii University, 1999, Available from: http://www.eng.hawaii.edu/˜nejhad/BUMPER/bumper.html] (sheet molding compound) bumper instead of the current GMT. The advantages of the proposed model: equal strength and rigidity of the structure, reduction of material, ease of manufacturing by simplifying geometrical shape and reduction of production cost are studied and proved.     

A conceptual design aid environment for expert-database systems

With the actual penetration of expert systems into the business world, the question is, how the expert system idea can be used to enhance the existing information systems with more intelligence in usage and operation. This interest is not surprising due to the advancement of the fifth generation of computer technology, and avid interest in the field of Artificial Intelligence. Therefore design of an information system for an application becomes more complex, and the inability of the human designer to deal with it increases. For designing intelligent systems, we have to be able to forecast the behavior of the information system more precisely before implementing it, i.e. we'have to support the specification process.Clearly the technology, such as Data base systems, is leading on efficiency issues as those needed for the construction, retrieval and manipulation of large shared data base. On the other hand, the AI techniques have improved significantly with function such as deductive reasoning and natural language processing. It is important to find way to merge these technologies into one mainstream of computing. A meeting point for the two areas is the issue of conceptual knowledge modelling, so that models can be created that will define the role and the ways to use data in AI systems. In the framework of this study, one possible expert system design aid environment has been suggested to assist the designer in his work.In a conceptual modelling environment a model is given for analysing complex real world problems known as the Conceptual Knowledge Model (CKM), represented by a Graphical and a Formal Representation. The Graphical Representation consists of three graphs: Conceptual Requirement Graph, Conceptual Behavior Graph, and Conceptual Structure Graph. These graphs are developed by involving the expert during the design process. The graphs are then transformed into first-order predicate logic to represent the logical axioms of a theory, which constitutes the knowledge base of the Expert System. The model suggested here is a step towards closing the gap between the theory of the conventional data base theory and AI databases.     

MMLT: A mutual multilevel trust framework based on trusted third parties in multicloud environments

In this article, a mutual multilevel trust framework is proposed, which involves managing trust from the perspective of cloud users (CUs) and cloud service providers (CSPs) in a multicloud environment based on a set of trusted third parties (TTPs). These independent agents are trusted by CUs and CSPs and distributed on different clouds. The TTPs evaluate the CUs' trustworthiness based on the accuracy of feedback ratings and assess the CSPs' trustworthiness based on the quality of service monitoring information. They are connected themselves through the trusted release network, which enables a TTP to obtain trust information about CSPs and CUs from other clouds. With the objective of developing an effective trust management framework, a new approach has been provided to improve trust-based interactions, that is, able to rank the trusted cloud services (CSs) based on CU's priorities via fuzzy logic. Fuzzy logic is applied to manage the different priorities of CUs, all the CUs do not have the same priorities to use trusted CSs. Customizing service ranking allows CUs to apply trusted CSs based on their priorities. Experiments on real datasets well matched the analytical results, indicating that our proposed approach is effective and outperforms the existing approaches.     

Reducing the network overhead of user mobility–induced virtual machine migration in mobile edge computing

With the popularity of mobile devices (such as smartphones and tablets) and the development of the Internet of Things, mobile edge computing is envisioned as a promising approach to improving the computation capabilities and energy efficiencies of mobile devices. It deploys cloud data centers at the edge of the network to lower service latency. To satisfy the high latency requirement of mobile applications, virtual machines (VMs) have to be correspondingly migrated between edge cloud data centers because of user mobility. In this paper, we try to minimize the network overhead resulting from constantly migrating a VM to cater for the movement of its user. First, we elaborate on two simple migration algorithms (M-All and M-Edge), and then, two optimized algorithms are designed by classifying user mobilities into two categories (certain and uncertain moving trajectories). Specifically, a weight-based algorithm (M-Weight) and a mobility prediction–based heuristic algorithm (M-Predict) are proposed for the two types of user mobilities, respectively. Numerical results demonstrate that the two optimized algorithms can significantly lower the network overhead of user mobility–induced VM migration in mobile edge computing environments.