Search

The rational design and development of cost-effective, high-performance, and stable bifunctional oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) electrocatalysts are essential for rechargeable zinc-air batteries. Herein, a novel FeCo composite composed of alloy nanoparticles embedded in an N, S dual-doped carbon matrix (FeCo/NSC) was prepared via one-step carbonization of amphiphilic dodecanethiol-metal salts wrapped in carbon nitride (C3N4). The compact combination of dual metal-alloys and dual-doped carbon endowed the composite with the active sites for the ORR and OER, achieving efficient electrical transmission and highly efficient bifunctional catalytic performance. The obtained FeCo-1/NSC catalyst exhibited excellent electrocatalytic activity with a half-wave potential of 0.82 V (vs. RHE) for the ORR and a low overpotential of 0.325 V at 10 mA cm−2 for the OER. The liquid Zn-air battery with FeCo-1/NSC as an air electrode displayed excellent charge-discharge performance, high power density, and robust charge-discharge stability for 150 h compared to the 20% Pt/C + RuO2 counterpart. Furthermore, the FeCo-1/NSC-based flexible solid-state Zn-air battery exhibited a higher power density and good charge-discharge stability over 10 h of operation. Thus, a promising strategy for bifunctional electrocatalyst development as part of rechargeable and wearable Zn-air batteries was provided.

A hybrid RSS/AOA indoor localization method based on error variance and measurement noise weighted least squares (ENWLS) is proposed. This method is based on three-dimensional wireless sensor networks, and achieves high-precision indoor positioning without increasing its complexity. We use the first-order Taylor approximation to approximate the linear weighted least square (WLS) error, and use the weighted least squares estimation to roughly estimate the location of the target, then determine the weight matrix by estimating the linear WLS error variance and the measured noise value on the sensor node. Simulation results show that our proposed method is better than other existing hybrid RSS/AOA localization methods.

A composite catalyst with a novel construction of bimetallic phosphide FeNiP nanoparticles embedded in an N,P double-doped carbon matrix was prepared. It was demonstrated to be a trifunctional catalyst that can efficiently catalyze the oxygen reduction reaction (ORR), oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). It was found that the introduction of oleylamine during the preparation can adjust the catalytic sites and finally lead to ideal catalytic performances. The obtained catalyst exhibited efficient ORR catalytic performance that surpassed the commercial Pt/C catalyst, with the OER performance comparable to that of RuO2 as well as excellent HER performance. The ORR half-wave potential is 0.879 V (vs. RHE) in 0.1 M KOH solution, while the OER overpotential at a current density of 10 mA cm−2 is only 280 mV in 1 M KOH solution. The potential gap between the ORR and OER was only 0.700 V in 0.1 M KOH solution. This trifunctional catalyst was further evaluated in energy devices including zinc–air batteries and water electrolysis. The liquid zinc–air battery assembly achieved a power density of 169 mW cm−2 and stably undergoes charge–discharge cycles for 210 hours. The solid-state zinc–air battery achieved a power density of 70 mW cm−2 and stably undergoes charge–discharge cycles for 40 hours. These performances surpassed the batteries assembled with a Pt/C-RuO2 mixed catalyst. This work established a foundation of composite catalysts coupled with bimetallic phosphide and hybrid carbon substrates, which will promote the development of high-performance multifunctional catalysts and their application in energy devices.

This paper addresses a target localization problem in 3-D wireless sensor networks using a hybrid system that fuses received signal strength and angle of arrival measurements. First, we formulate the received signal strength and angle of arrival measurement models as the pseudo-linear equations. Then, the bias is derived from the 3-D angle of arrival measurements that take the measurement noise into account to improve the localization performance. Furthermore, a non-convex estimator is derived based on the Least Squares criterion. Finally, semi-definite relaxation and second-order cone relaxation are applied to transform the derived non-convex estimator into a convex one. We propose a semi-definite relaxation and second-order cone relaxation-based estimator which yields the best performance under a large measurement noise or a small measurement noise. The generalization of the proposed method for known transmit power can also be applied to the case when transmit power is not know. Theoretical analysis and computer simulations corroborate the superior performance of the proposed localization methods over the existing ones.

This paper proposes a novel scheme to solve the target localization problem by using combined measurements of received signal strength and angle-of-arrival (AOA) hybrid measurements in three-dimensional (3D) wireless sensor networks (WSNs). The proposed scheme can work effectively in both cases of known and unknown target transmit power. In the known transmit power case, by using polarized identity, we transform the available AOA measurements into a norm form and establish new relationships between the AOA measurements and the unknown target location, a novel target localization method is then proposed by using the second-order cone programming relaxation technique. Furthermore, we demonstrate that by using mean inequality, the proposed method can be effectively extended to the unknown transmit power case. In this case, we propose a practical alternating estimation procedure which alternatively estimates the target location and transmit power. Computer simulations are carried out to demonstrate that comparing with other methods, the proposed method can achieve significant performance gains for different settings in 3D WSNs.

A novel time-of-arrival–based localization algorithm in mixed line-of-sight/non-line-of-sight environments is proposed. First, an optimization problem of target localization in the known distribution of line-of-sight and non-line-of-sight is established, and mixed semi-definite and second-order cone programming techniques are used to transform the original problem into a convex optimization problem which can be solved efficiently. Second, a worst-case robust least squares criterion is used to form an optimization problem of target localization in unknown distribution of line-of-sight and non-line-of-sight, where all links are treated as non-line-of-sight links. This problem is also solved using the similar techniques used in the known distribution of line-of-sight and non-line-of-sight case. Finally, computer simulation results show that the proposed algorithms have better performance in both the known distribution and the unknown distribution of line-of-sight and non-line-of-sight environments.

Transformation from crude wheat straw to the efficient bifunctional catalysts was realized to obtain a unique composite structure. The ideal designed construction of bimetallic phosphide nanoparticles embedding in N, P dual-doped carbon matrix exhibited the impressive performances for catalyzing the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). This catalyst has been used to assemble metal-air batteries. The rechargeable liquid zinc-air battery achieved the power density of 122.5 mW cm−2 and charge–discharge cycled for over 110 h. The rechargeable solid-state zinc-air battery exhibited the power density of 55.6 mW cm−2 and charge–discharge cycled for over 35 h. In addition, it was also investigated in the aluminum-air battery and exhibited excellent performances. This work has paved the way of preparing efficient metal-air battery catalyst from low-cost agricultural by-products.

A novel method for the wireless localization through received signal strength (RSS) measurements is proposed in this paper. In this method, the original non-convex estimator based on the least squares (LS) criterion is transformed into a semi-definite programming (SDP) problem by applying efficient convex relaxation techniques. Furthermore, an efficient implementation algorithm is proposed through a standard interior-point method for solving the convex optimization problem. The corresponding Cramer-Rao lower bound (CRLB) is also derived to verify the performance of the proposed method. Simulation results confirm the efficiency of the proposed SDP estimator in solving the localization problem and its advantage over the state-of-the-art approach.

Received signal strength–based target localization methods normally employ radio propagation path loss model, in which the log-normal shadowing noise is generally assumed to follow a zero-mean Gaussian distribution and is uncorrelated. In this article, however, we represent the simplified additive noise by the spatially correlated log-normal shadowing noise. We propose a new convex localization estimator in wireless sensor networks by using received signal strength measurements under spatially correlated shadowing environment. First, we derive a new non-convex estimator based on weighted least squares criterion. Second, by using the equivalence of norm, the derived estimator can be reformulated as its equivalent form which has no logarithm in the objective function. Then, the new estimator is relaxed by applying efficient convex relaxation that is based on second-order cone programming and semi-definite programming technique. Finally, the convex optimization problem can be efficiently solved by a standard interior-point method, thus to obtain the globally optimal solution. Simulation results show that the proposed estimator solves the localization problem efficiently and is close to Cramer–Rao lower bound compared with the state-of-the-art approach under correlated shadowing environment.

This paper addresses the target localization problems based on received signal strength (RSS) measurements in underwater acoustic wireless sensor network (UWSN). Firstly, the problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks. A three-step procedure is also provided to enhance the estimation accuracy in the unknown target transmit power case. Furthermore, the Cramer–Rao lower bounds (CRLBs) in both cases are derived. Computer simulation results show the superior performance of the proposed methods in the underwater environment.

The received signal strength (RSS) based target localization problem in underwater acoustic wireless sensor networks (UWSNs) is considered. Two cases with respect to target transmit power are considered. For the first case, under the assumption that the reference of the target transmit power is known, we derive a novel weighted least squares (WLS) estimator by using an approximation to the RSS expressions, and then transform the originally non-convex problem into a mixed semi-definite programming/second-order cone programming (SD/SOCP) problem for reaching an efficient solution. For the second case, there is no knowledge on the target transmit power, and we treat the reference power as an additional unknown parameter. In this case, we formulate a WLS estimator by using a further approximation, and present an iterative ML and mixed SD/SOCP algorithm for solving the derived WLS problem. For both cases, we also derive the closed form expressions of the Cramer&ndash, Rao Lower Bounds (CRLBs) on root mean square error (RMSE). Computer simulation results show the superior performance of the proposed methods over the existing ones in the underwater acoustic environment.

In open and complex underwater environments, targets to be monitored are highly dynamic and exhibit great uncertainty. To optimize monitoring target coverage, the development of a method for adjusting sensor positions based on environments and targets is of crucial importance. In this paper, we propose a distributed hybrid fish swarm optimization algorithm (DHFSOA) based on the influence of water flow and the operation of an artificial fish swarm system to improve the coverage efficacy of the event set and to avoid blind movements of sensor nodes. First, by simulating the behavior of foraging fish, sensor nodes autonomously tend to cover events, with congestion control being used to match node distribution density to event distribution density. Second, the construction of an information pool is used to achieve information-sharing between nodes within the network connection range, to increase the nodes&rsquo, field of vision, and to enhance their global search abilities. Finally, we conduct extensive simulation experiments to evaluate network performance in different deployment environments. The results show that the proposed DHFSOA performs well in terms of coverage efficacy, energy efficiency, and convergence rate of the event set.

Results of three-dimensional steady flow calculations are compared with existing pressure and velocity measurements of two manifold-type junctions. The junctions consist of a main duct and a side branch, both with the same rectangular cross section, with the side branch joining the main duct at an angle of either 90 or 45 degrees. Both combining and dividing flow configurations are considered for different total mass flow rates and different side-branch-to-main-duct mass flow ratios. One objective of this investigation was to assess the effects of numerical differencing scheme and mesh refinement on solution accuracy, and both parameters showed strong influences on the computed results. It is shown that calculations should be made with the highest possible level of numerical accuracy and grid resolution in regions of flow recirculation. Comparisons of computed and measured velocities, static pressures, and flow loss coefficients are presented in this paper. For most cases considered, the model predictions are in good agreement with the measurements. Results can be used as input loss coefficients to an engine-simulation code, in addition to being used to evaluate a specific junction design.