Search

This study reports a supramolecular self-healing material as a multi-functional binder for lithium–sulfur batteries. The spontaneously damage repair ability of such a binder can be applied to overcome the short cycle-life issue of lithium–sulfur batteries under low current density with deep galvanostatic cycling. Diamines and polybasic acids are used to synthesize the supramolecular self-healing material. 10 wt% amine groups in this designed material provide a large amount of chemical adsorption sites for polysulfides which can effectively inhibit the shuttling of polysulfides and maintain the content of sulfur species in cathode. This N-rich binder is mixed with the sulfur during preparation, which can improve the effective contacting surface of N function groups and sulfur locally. The cells with pure self-healing material binder achieve an initial capacity of 918 mAh g−1, and maintain a reversible capacity of 469 mAh g−1 after 200 cycles at 0.1C, twice higher than the retention capacity of cells with polyvinylidene fluoride binder. After optimization, the cells with a hybrid binder of self-healing material and polyvinylidene fluoride (weight ratio of 1:1) with a sulfur loading of 2.65 mg cm−2 achieve an initial capacity of 993 mAh g−1, and remain a reversible capacity of 571 mAh g−1 with a capacity fade of 0.2% per cycle after 200 cycles at 0.1C.

Cactus structure MnO2/N-rich carbon nanofiber composites were synthesized via an in situ redox reaction to be utilized as the interlayer in lithium–sulfur batteries. The exposed MnO2 nanosheets vertically aligned on carbon nanofibers provide a large amount of active sites for polysulfide adsorption and conversion. Unlike physical barriers or simple surface interactions, the MnO2 nanosheets generate an anchor and transfer mediator for polysulfides to dissolve into the electrolyte, and then control the deposition of Li2S2 or Li2S on the designed interlayer. This structure induces an arranged relocation of sulfur species during cycling, resulting in a shortened electron passage and an improved utilization of the active material. With a sulfur loading of 1.65 mg cm−2 and a sulfur content of 60% at the whole electrode level, the cell with the MnO2/carbon nanofiber interlayer delivered an initial discharge capacity of 1102 mA h g−1, 965 mA h g−1, and 712 mA h g−1 at 0.1C, 1C and 5C, respectively. It could maintain a reversible capacity of 646 mA h g−1 after 100 cycles at 1C and a specific capacity of 414 mA h g−1 after 1000 cycles at 5C with a fade rate of 0.04% per cycle.

A self-standing nitrogen/oxygen dual-doped carbon nanofiber matrix is prepared on the basis of polymer chain design and electrospinning followed by a one-step carbonization. The interlayer can subs...

In order to solve the problem of uncertain cluster number and Initial cluster center in wind power data processing by traditional clustering algorithms, as well as the problem of improving the efficiency of dividing adjacent transition sample. A wind power forecasting data processing method based on density peak modified fuzzy c-means soft clustering is proposed. By using the decision graph, the clustering center and the number of clusters of the density peak clustering algorithm can be obtained automatically. Soft partition rules instead of maximum membership classification rules are used in Fuzzy c-means soft clustering algorithm, so the transition samples can be divided into different sub-sets to make sub-sets more reasonable. In this method. First, the power sample set is established by segmenting the historical power sequence; Second, the fuzzy c-mean soft clustering is improved by using density peak clustering; Finally, the improved clustering algorithm is used to cluster the sample set. The proposed method is applied to the actual power forecasting data processing. The results show that compared with the traditional clustering algorithm, the proposed method is more effective and convergent, and the transition sample division is more reasonable.

Lithium metal anodes (LMAs) have exceptional promises for rechargeable Li-metal batteries. However, their practical applications have long been hindered by the safety concerns related to excessive Li dendrite growth and huge volume expansion during cycling, especially in high-capacity cycles. In this work, we developed a new strategy to suppress the dendrite growth via a 3D reinforced lithium metal composite anode with a nanostick structured copper oxide @copper mesh. Such nanostick structured copper oxide would not only reduce the lithium nucleation overpotential to 11 mV, but also would uniformly accommodate lithium ions to suppress the dendrite effect. Moreover, the 3D structure of copper mesh could effectively alleviate the aggressive volume changes of the lithium metal electrode after hundreds of cycles. Based on CuO@copper mesh, highly reversible lithium stripping/plating are achieved for LMAs at current density of 1 mA cm–2 with a limited volume fluctuation of 13.6% after 1000 h. With ultrahigh capacity densities of 5 mAh cm−2, the CuO@Cu mesh/Li cell exhibits low overpotential of 12 mV and ultralong cycle life over 600 h, which is 2–3 times of the reported average cycling lifespan. The proposed preparation process is simple and easy to scale-up, demonstrating a promising prospect as a commercial Li metal anode with large capacity.

Neural network attracts more and more attention in many research fields. However, neural network prediction is scarce in determining the hidden layer. In this paper an algorithm for rapidly discovering hidden layer nodes in neural networks is proposed. Establish a neural network to predict future wind power. Weather forecast information is used as an input data set for neural networks. Then two test sites in the hidden layer are identified by traditional methods. The fitting degree of the two test points is compared through the fitting judgment. BP NNW is founded base on the weather-report data, and the prediction of future wind power is finally completed

The wind power prediction plays an important role in the steady run of the power system after the wind power grid. Because of the large amount of data used in the prediction process and the large number of data dimensions, the traditional prediction methods are prone to fall into local convergence and affect the accuracy of prediction. In this paper, a prediction method based on chaos theory and GABP neural network is designed. Firstly, the evolution trajectory of wind power time series in high dimensional space is recovered by phase space reconstruction. Then genetic algorithm is applied to optimize BP neural network (GABP), to make up for the randomness of BP neural network in the choice value and threshold value. Then the trajectory recovered by GABP neural network training is applied to predict the evolution trajectory in the future. Finally, taking wind farm data as an example, the results show that the proposed algorithm improves the prediction accuracy of ultra-short-term prediction.

Porous hollow carbon nanofibers (PHCNFs) using styrene-acrylonitrile copolymer (SAN) solution as core and polyacrylic acid (PAA) as shell were manufactured by co-axial electrospinning technique, taking polyvinyl pyrrolidone (PVP) as a pore inducer additive in the shell. The shell thickness of PHCNFs could be adjusted by controlling flow rates of core and shell fluids. The prepared PHCNFs showed excellent electrochemical properties with the high specific capacitance of 221 F g−1 and superior cycling stability, remaining a capacitance retention of 95% after 5000 cycles under a scan rate of 0.1 V s−1. In this system, hollow structures bring a 20% capacitance improvement, while the porous morphology brings a 47% capacitance improvement. The attractive performances exhibited by these sponge supercapacitors make them potentially promising candidates for future energy storage systems. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 43397.

The effect of Y addition on the average grain size and mechanical properties of Ni-Mn-Sn magnetic shape memory alloys is studied by means of optical microscope, compressive test and scanning electron microscope (SEM) observation in the paper. The results show that Y addition refines the grains of Ni-Mn-Sn alloy obviously and significantly enhances the compressive strength and maximum compressive strain. In addition, the fracture type changes from typical intergranular crack to transgranular crack gradually with Y addition into Ni-Mn-Sn FSMAS.