Dual growth of graphene nanoplatelets and carbon nanotubes hybrid structure via chemical vapor deposition of methane over Fe–MgO catalysts

Abstract

In this study, Fe–MgO catalyst substrates with various Fe and MgO combinations were evaluated for the growth of different types of carbon nanostructure materials (CNMs), particularly graphene nanoplatelets (GNPs) and carbon nanotubes (CNTs) via chemical vapor deposition using methane as a carbon source. The hydrogen yield was also determined as a valuable by-product in this process. Therefore, a set of Fe–MgO catalysts with different iron loadings (30, 80, 85, 90 and 100?wt %) were prepared by the combustion method to realize this target. The physicochemical properties of freshly calcined Fe–MgO catalysts were investigated by XRD, TPR and BET, while the as-grown CNMs were studied by HR-TEM, XRD and Raman spectroscopy. The results verified that the morphology of as-grown CNMs as well as the H₂ yield was directly correlated to the iron content in the catalyst composition. The XRD and TPR results showed that various FeMgO_x species with deferent levels of interactions were produced with the gradual incorporation of MgO content. TEM images indicated that GNPs were individually grown on the surface of high loaded iron-containing catalysts (90–100?wt %) due to the presence of highly aggregated iron particles. While multi-walled carbon nanotubes (MWCNTs) with uniform diameters were grown on the low iron-loaded catalyst (30%Fe/MgO) due to the formation of highly dispersed FeMgO_x particles. On the other hand, GNPs/MWCNTs hybrid materials were grown on the surface of 80%Fe and 85%Fe/MgO catalysts. This behavior can be interpreted by the co-existence of highly aggregated and highly dispersed Fe₂O₃ particles in the catalyst matrix. The results demonstrated that the catalyst composition has a notable effect on the nature of CNMs products and H₂ yield.     

Optimal Implementation of Photovoltaic and Battery Energy Storage in Distribution Networks

Recently, implementation of Battery Energy Storage (BES) with photovoltaic (PV) array in distribution networks is becoming very popular in overall the world. Integrating PV alone in distribution networks generates variable output power during 24-hours as it depends on variable natural source. PV can be able to generate constant output power during 24-hours by installing BES with it. Therefore, this paper presents a new application of a recent metaheuristic algorithm, called Slime Mould Algorithm (SMA), to determine the best size, and location of photovoltaic alone or with battery energy storage in the radial distribution system (RDS). This algorithm is modeled from the behavior of SMA in nature. During the optimization process, the total active power loss during 24-hours is used as an objective function considering the equality and inequality constraints. In addition, the presented function is based on the probabilistic for PV output and different types of system load. The candidate buses for integrating PV and BES in the distribution network are determined by the real power loss sensitivity factor (PLSF). IEEE 69-bus RDS with different types of loads is used as a test system. The effectiveness of SMA is validated by comparing its results with those obtained by other well-known optimization algorithms.     

Cell-Mimic Directional Cargo Transportation in a Visible-Light-Activated Colloidal Motor/Lipid Tube System

Active tether and transportation of cargoes on cytoskeletal highway enabled by molecular motors is key for accurate delivery of vesicles and organelles in the complex intracellular environment. Here, a hybrid system composed of colloidal motors and self-assembled lipid tubes is designed to mimic the subcellular traffic system in living cells. The colloidal motors, composed of gold-coated hematite, display light-activated self-propulsion tunable by the light intensity and the concentration of hydrogen peroxide fuel. Importantly, the motors show light-switchable binding with lipid cargoes and attachment to the lipid tubes, whereby the latter act as the motor highways. Upon assembly, the colloidal motor/lipid tube system demonstrates directional delivery of lipid vesicles, emulating intracellular transportation. The assembly and function of the hybrid system are rationalized by a cooperative action of light-triggered electrophoretic and hydrodynamic effects, supported by finite element analysis. A synthetic analog of the biological protein motor/cytoskeletal filament system is realized for the manipulation and delivery of different matter at the microscale, which is expected to be a promising platform for various applications in materials science, nanotechnology, microfluidics, and synthetic biology.     

Sampling‐based hierarchical motion planning for a reconfigurable wheel‐on‐leg planetary analogue exploration rover

Reconfigurable mobile planetary rovers are versatile platforms that may safely traverse cluttered environments by morphing their physical geometry. Planning paths for these adaptive robots is challenging due to their many degrees of freedom, and the need to consider potentially continuous platform reconfiguration along the length of the path. We propose a novel hierarchical structure for asymptotically optimal (AO) sampling‐based planners and specifically apply it to the state‐of‐the‐art Fast Marching Tree (FMT*) AO planner. Our algorithm assumes a decomposition of the full configuration space into multiple subspaces, and begins by rapidly finding a set of paths through one such subspace. This set of solutions is used to generate a biased sampling distribution, which is then explored to find a solution in the full configuration space. This technique provides a novel way to incorporate prior knowledge of subspaces to efficiently bias search within existing AO sampling‐based planners. Importantly, probabilistic completeness and asymptotic optimality are preserved. Experimental results in simulation are provided that benchmark the algorithm against state‐of‐the‐art sampling‐based planners without the hierarchical variation. Additional experimental results performed with a physical wheel‐on‐leg platform demonstrate application to planetary rover mobility and showcase how constraints such as actuator failures and sensor pointing may be easily incorporated into the planning problem. In minimizing an energy objective that combines an approximation of the mechanical work required for platform locomotion with that required for reconfiguration, the planner produces intuitive behaviors where the robot dynamically adjusts its footprint, varies its height, and clambers over obstacles using legged locomotion. These results illustrate the generality of the planner in exploiting the platform's mechanical ability to fluidly transition between various physical geometric configurations, and wheeled/legged locomotion modes, without the need for predefined configurations.     

A temporal modal defeasible logic for formalizing social commitments in dialogue and argumentation models

In this paper, we extend a temporal defeasible logic with a modal operator Committed to formalize commitments that agents undertake as a consequence of communicative actions (speech acts) during dialogues. We represent commitments as modal sentences. The defeasible dual of the modal operator Committed is a modal operator called Exempted. The logical setting makes the social-commitment based semantics of speech acts verifiable and practical; it is possible to detect if, and when, a commitment is violated and/or complied with. One of the main advantages of the proposed system is that it allows for capturing the nonmonotonic behavior of the commitments induced by the relevant speech acts.     

Enhance Energy Conservation Based on Residual Energy and Distance for WSNs

As the advancements in the field of artificial intelligence technologies continue to grow, robots are being built by the researchers as an attempt to render services to the people. In this regard, the robots can communicate effectively with the people and be able to complete all the tasks adequately given to them. These service robots while being developed requires the dialogue services to be developed to interact effectively with human beings providing far better user experience. Thus, the robot been built can provide domain-specific knowledge as well as able to provide consultations in various domains. We in this paper adopted a service-oriented approach for developing context-aware communication services for the cloud robot. The proposed work aims at training the context-aware model developed. The context-aware model is responsible for answering the questions put forward by the users and possess the ability to exploit the answers corresponding to the questions presented. An integrated framework is used to combine the contextual information and communication services. The performance of the proposed model can be evaluated based on Inverse Rank Mean (IRM). Evolutionary testing methods are used for testing the data in the proposed model. The results thus obtained shows the effectiveness of the proposed approach.

     

Efficient and dynamic scaling of fog nodes for IoT devices

It is predicted by the year 2020, more than 50 billion devices will be connected to the Internet. Traditionally, cloud computing has been used as the preferred platform for aggregating, processing, and analyzing IoT traffic. However, the cloud may not be the preferred platform for IoT devices in terms of responsiveness and immediate processing and analysis of IoT data and requests. For this reason, fog or edge computing has emerged to overcome such problems, whereby fog nodes are placed in close proximity to IoT devices. Fog nodes are primarily responsible of the local aggregation, processing, and analysis of IoT workload, thereby resulting in significant notable performance and responsiveness. One of the open issues and challenges in the area of fog computing is efficient scalability in which a minimal number of fog nodes are allocated based on the IoT workload and such that the SLA and QoS parameters are satisfied. To address this problem, we present a queuing mathematical and analytical model to study and analyze the performance of fog computing system. Our mathematical model determines under any offered IoT workload the number of fog nodes needed so that the QoS parameters are satisfied. From the model, we derived formulas for key performance metrics which include system response time, system loss rate, system throughput, CPU utilization, and the mean number of messages request. Our analytical model is cross-validated using discrete event simulator simulations.     

Formal analysis of seamless application execution in mobile cloud computing

Mobile cloud computing augments the resource-constrained mobile devices to run rich mobile applications by leveraging the cloud resources and services. Compute-intensive mobile apps require significant communication resources for migrating the code from mobile devices to the cloud. For such apps, distributed application execution frameworks (DAEF) have been proposed in the literature. These frameworks either migrate the mobile app code during runtime or keep the app synchronized with another remotely executed app on the cloud. Frameworks also support mobile app live migration to cater for compute node mobility. One key research question arises is how successful are these DAEFs in achieving the seamless application execution under various network conditions? The answer to this question entails formal analysis of the DAEFs to determine the realistic bounds on propagation delay, bandwidth and application interaction with mobile device for various types and sizes of apps. In this research, we apply formal analysis techniques to define the execution time of the app and the time required for code migration. We also define three conditions for seamless application execution. Given realistic values for processor speed, application executable size, possible number of executed instructions, network propagation delay and transmission delay, we show what components of the mobile app need to be migrated during execution to the cloud. Finally, we compute realistic bounds for the app size (that can be executed seamlessly) based on important features which include cloud and device resources, bandwidth and latency profile.     

Multi-Directional Maximum-Entropy Approach to the Evolutionary Design Optimization of Water Distribution Systems

A new multi-directional search approach that aims at maximizing the flow entropy of water distribution systems is investigated. The aim is to develop an efficient and practical maximum entropy based approach. The resulting optimization problem has four objectives, and the merits of objective reduction in the computational solution of the problem are investigated also. The relationship between statistical flow entropy and hydraulic reliability/failure tolerance is not monotonic. Consequently, a large number of maximum flow entropy solutions must be investigated to strike a balance between cost and hydraulic reliability. A multi-objective evolutionary optimization model is developed that generates simultaneously a wide range of maximum entropy values along with clusters of maximum and near-maximum entropy solutions. Results for a benchmark network and a real network in the literature are included that demonstrate the effectiveness of the procedure.