Rational design of low-cost and high-efficiency non-precious metal-based catalysts toward the hydrogen evolution reaction (HER) is of paramount significance for the sustainable development of the hydrogen economy. Interfacial manipulationinduced electronic modulation represents a sophisticated strategy to enhance the intrinsic activity of a non-precious electrocatalyst. Herein, we demonstrate the straightforward construction of Co/MoS 2 hetero-nanoparticles anchored on inverse opal-structured N,S-doped carbon hollow nanospheres with an ordered macroporous framework (denoted as Co/MoS 2 @N,S-CHNSs hereafter) via a templateassisted method. Systematic experimental evidence and theoretical calculations reveal that the formation of Co/MoS 2 heterojunctions can effectively modulate the electronic configuration of active sites and optimize the reaction pathways, remarkably boosting the intrinsic activity. Moreover, the inverse opal-structured carbon substrate with an ordered porous framework is favorable to enlarge the accessible surface area and provide multidimensional mass transport channels, dramatically expediting the reaction kinetics. Thanks to the compositional synergy and structural superiority, the fabricated Co/MoS 2 @N,S-CHNSs exhibit excellent HER activity with a low overpotential of 105 mV to afford a current density of 10 mA/cm 2 . The rationale of interface manipulation and architectural design herein is anticipated to be inspirable for the future development of efficient and earth-abundant electrocatalysts for a variety of energy conversion systems.
At present, people are in the era of big data, which is changing people's views of the world. However, it has the characteristics of various types, huge scale, and complex relationships. In order to solve the repeated calculation caused by streaming data in the processing of tensor-based big data, there will also be dimension disasters. Therefore, in this paper, an incremental tensor train decomposition (ITTD) method is proposed to solve multi-clustering problem in tensor-based big data analysis systems. It mainly uses results of the tensor train decomposition obtained from the original tensor to calculate and updates the results of tensor train decomposition to avoid the repetitive decomposition of the original tensor and enhance the decomposition efficiency. The performance of ITTD method is tested through theoretical analysis, a large number of simulation data and a comparative experiment on the real data of public transportation in a region. The experimental results indicate that the execution time of ITTD is significantly shorter than that of nonincremental tensor train decomposition(NTTD) with time. However, as time goes by, there is no obvious difference in the approximation error and storage space between the two.. This shows that, compared to that of the traditional nonincremental method, if the approximation error and storage space are close, the execution time of the incremental method will be greatly shortened. It can improve the processing efficiency of multi-clustering problems in the tensor-based big data analysis system.
We propose a novel cooperative localization algorithm called PSO-PF algorithm based on particle swarm optimization enhanced particle filter that fuses information from both anchors with known positions and agents with unknown positions in wireless networks. The proposed algorithm for localization is fully distributed and can eliminate the loss of diversity caused by resampling in particle filter. Simulation results show that the proposed algorithm outperforms the particle filter on the accuracy with less particles in the challenged scenarios for cooperative localization. Moreover, the proposed algorithm is superior performance on convergence to the particle filter in cooperative localization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.