Targeted Synergy between Adjacent Co Atoms on Graphene Oxide as an Efficient New Electrocatalyst for Li–CO2 Batteries

Li–CO₂ batteries are an attractive technology for converting CO₂ into energy. However, the decomposition of insulating Li₂CO₃ on the cathode during discharge is a barrier to practical application. Here, it is demonstrated that a high loading of single Co atoms (≈5.3%) anchored on graphene oxide (adjacent Co/GO) acts as an efficient and durable electrocatalyst for Li–CO₂ batteries. This targeted dispersion of atomic Co provides catalytically adjacent active sites to decompose Li₂CO₃. The adjacent Co/GO exhibits a highly significant sustained discharge capacity of 17 358 mA h g^?1 at 100 mA g^?1 for >100 cycles. Density functional theory simulations confirm that the adjacent Co electrocatalyst possesses the best performance toward the decomposition of Li₂CO₃ and maintains metallic‐like nature after the adsorption of Li₂CO₃.     

High Areal Capacitance of N‐Doped Graphene Synthesized by Arc Discharge

The lack of cost effective, industrial‐scale production methods hinders the widespread applications of graphene materials. In spite of its applicability in the mass production of graphene flakes, arc discharge has not received considerable attention because of its inability to control the synthesis and heteroatom doping. In this study, a facile approach is proposed for improving doping efficiency in N‐doped graphene synthesis through arc discharge by utilizing anodic carbon fillers. Compared to the N‐doped graphene (1–1.5% N) synthesized via the arc process according to previous literature, the resulting graphene flakes show a remarkably increased doping level (≈3.5% N) with noticeable graphitic N enrichment, which is rarely achieved by the conventional process, while simultaneously retaining high turbostratic crystallinity. The electrolyte ion storage of synthesized materials is examined in which synthesized N‐doped graphene material exhibits a remarkable area normalized capacitance of 63 µF cm^?2. The surprisingly high areal capacitance, which is superior to that of most carbon materials, is attributed to the synergistic effect of extrinsic pseudocapacitance, high crystallinity, and abundance of exposed graphene edges. These results highlight the great potentials of N‐doped graphene flakes produced by arc discharge in graphene‐based supercapacitors, along with well‐studied active exfoliated graphene and reduced graphene oxide.     

A novel energy‐aware routing mechanism for SDN‐enabled WSAN

Energy consumption is one of the most important design constraints when building a wireless sensor and actuator network since each device in the network has a limited battery capacity, and prolonging the lifetime of the network depends on saving energy. Overcoming this challenge requires a smart and reconfigurable network energy management strategy. The Software‐Defined Networking (SDN) paradigm aims at building a flexible and dynamic network structure, especially in wireless sensor networks. In this study, we propose an SDN‐enabled wireless sensor and actuator network architecture that has a new routing discovery mechanism. To build a flexible and energy‐efficient network structure, a new routing decision approach that uses a fuzzy‐based Dijkstra's algorithm is developed in the study. The proposed architecture can change the existing path during data transmission, which is the key property of our model and is achieved through the adoption of the SDN approach. All the components and algorithms of the proposed system are modeled and simulated using the Riverbed Modeler software for more realistic performance evaluation. The results indicate that the proposed SDN‐enabled structure with fuzzy‐based Dijkstra's algorithm outperforms the one using the regular Dijkstra's and the ZigBee‐based counterpart, in terms of the energy consumption ratio, and the proposed architecture can provide an effective cluster routing while prolonging the network lifetime.     

SDN‐based dynamic multipath forwarding for inter–data center networking

Equal‐cost multipath (ECMP)–based traffic engineering (TE) methods are commonly used in intra–data center (DC) networks to improve the transmission performance for east‐west traffic (ie, traffic from server to server within a DC). However, applying ECMP on inter‐DC wide area network (WAN) offers limited performance enhancement as a result of irregular network topology. Since TE can be intelligently and efficiently realized with software‐defined networking (SDN), SDN‐based multipath becomes a popular option. However, SDN suffers from scalability issue caused by limited ternary content‐addressable memory (TCAM) size. In this paper, we propose an SDN‐based TE method called dynamic flow‐entry‐saving multipath (DFSM) for inter‐DC traffic forwarding. DFSM adopts source‐destination–based multipath forwarding and latency‐aware traffic splitting to reduce the consumption of flow entries and achieve load balancing. The evaluation results indicate that DFSM saves 15% to 30% of system flow entries in practical topologies and reduces the standard deviation of path latencies from 10% to 7% than do label‐switched tunneling, and also reduces average latency by 10% to 48% by consuming 6% to 20% more flow entries than do ECMP in less‐interconnected topologies. Note that the performance gain may not always be proportional to flow entry investment, with the interconnectivity between nodes being an important factor. The evaluation also indicates that per‐flow provision consumes several times the flow entries consumed by DFSM but reduces latency by 10% at most. Besides, DFSM reduces the standard deviation of path latencies from 14% to 7% than do even traffic splitting.     

On the capacity of the state‐dependent interference relay channel

This paper considers the state‐dependent interference relay channel (SIRC) in which one of the two users may operate as a secondary user and the relay has a noncausal access to the signals from both users. For discrete memoryless SIRC, we first establish the achievable rate region by carefully merging Han‐Kobayashi rate splitting encoding technique, superposition encoding, and Gelfand‐Pinsker encoding technique. Then, based on the achievable rate region that we derive, the capacity of the SIRC is established in many different scenarios including (a) the weak interference regime, (b) the strong interference regime, and (c) the very strong interference regime. This means that our capacity results contain all available known results in the literature. Next, the achievable rate region and the associated capacity results are also evaluated in the case of additive Gaussian noise. Additionally, many numerical examples are investigated to show the value of our theoretical derivations.