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The sluggish electrochemical kinetics of sulfur species has impeded the wide adoption of lithium-sulfur battery, which is one of the most promising candidates for next-generation energy storage system. Here, we present the electronic and geometric structures of all possible sulfur species and construct an electronic energy diagram to unveil their reaction pathways in batteries, as well as the molecular origin of their sluggish kinetics. By decoupling the contradictory requirements of accelerating charging and discharging processes, we select two pseudocapacitive oxides as electron-ion source and drain to enable the efficient transport of electron/Li+ to and from sulfur intermediates respectively. After incorporating dual oxides, the electrochemical kinetics of sulfur cathode is significantly accelerated. This strategy, which couples a fast-electrochemical reaction with a spontaneous chemical reaction to bypass a slow-electrochemical reaction pathway, offers a solution to accelerate an electrochemical reaction, providing new perspectives for the development of high-energy battery systems., The sluggish electrochemical kinetics of sulfur species remains a major hurdle for the broad adoption of lithium-sulfur batteries. Here, the authors construct an energy diagram of sulfur species to unveil their reaction pathways and propose a general strategy to accelerate electrochemical reactions.

Silicon is one of the most promising anode materials for lithium-ion batteries due to its high theoretical capacity and low cost. However, significant capacity fading caused by severe structural degradation during cycling limits its practical implication. To overcome this barrier, we design a covalently bonded nanocomposite of silicon and poly(vinyl alcohol) (Si-PVA) by high-energy ball-milling of a mixture of micron-sized Si and PVA. The obtained Si nanoparticles are wrapped by resilient PVA coatings that covalently bond to the Si particles. In such nanostructures, the soft PVA coatings can accommodate the volume change of the Si particles during repeated lithiation and delithiation. Simultaneously, as formed covalent bonds enhance the mechanical strength of the coatings. Due to the significantly improved structural stability, the Si-PVA composite delivers a lifespan of 100 cycles with a high capacity of 1526 mAh g-1. In addition, a high initial Coulombic efficiency of over 86% and an average value of 99.2% in subsequent cycles can be achieved. This reactive ball-milling strategy provides a low-cost and scalable route to fabricate high-performance anode materials.

In order to improve the accuracy of the gangue powder concrete compressive strength prediction model, the factor analysis method is combined with the BP neural network method to propose an improved BP neural network gangue powder concrete compressive strength prediction model. The actual data from the project site was selected to test the accuracy of the improved BP neural network gangue powder concrete compressive strength prediction model. The final result: the relative average error between the predicted value and the actual value of the 10 groups of training samples is 4.36%. The relative errors of the samples are 3.24%, 4.92%, 1.68%, 3.54%, and 2.23%, and the average relative errors are 3.122%, both of which are less than 10%, proving that the improved BP neural network prediction model has good prediction accuracy.

In order to improve the feasibility and accuracy of the concrete comprehensive performance prediction model, the factor analysis method and the BP neural network method are combined to propose an improved BP neural network concrete comprehensive performance prediction model. Example data was selected to test the improved BP neural network concrete comprehensive performance prediction model. The test results: the relative average errors of the 28d strength and slump expansion of the 14 groups of training sample predicted values and actual values were 3.398% and 1.712%, respectively. The average relative errors of 28d strength and slump expansion of each predicted sample are 4.33%, 3.80%, 5.77%, 2.07%, 1.02% and 3.55%, 0.94%, 1.48%, 0.59%, 2.00%, all of which are less than 10%, Which proves that the improved BP neural network prediction model has better prediction accuracy.

In order to improve the accuracy of the blasting vibration frequency prediction model, a factor analysis method is combined with the BP neural network method to propose an improved BP neural network blasting vibration frequency prediction model. The actual field data of the project is selected to test the accuracy of the improved BP neural network network blasting vibration frequency prediction model. The final result: the relative average error between the predicted value and the actual value of the 20 groups of training samples is 4.89%. The errors are 6.75%, 5.68%, 5.69%, 4.64%, 4.90%, 6.35%, 3.56%, and the average relative error is 5.37%, both of which are less than 10%, proving that the improved BP neural network prediction model has good prediction accuracy.

A new family of solid-like electrolytes was developed by infiltrating MIL-100(Al), an electrochemically stable metal-organic-framework (MOF) material, with liquid electrolytes that contain cations from the 3rd period (Na

Particular recent interest has been given to the Li

High-performance lithium-ion batteries (LIBs) demand efficient and selective transport of lithium ions. Inspired by ion channels in biology systems, lithium-ion channels are constructed by chemically modifying the nanoporous channels of metal-organic frameworks (MOFs) with negatively charged sulfonate groups. Analogous to the biological ion channels, such pendant anionic moieties repel free anions while allowing efficient transport of cations through the pore channels. Implementing such MOFs as an electrolyte membrane doubly enhances the lithium-ion transference number, alleviates concentration polarization, and affords striking durability of high-rate LIBs. This work demonstrates an ion-selective material design that effectively tunes the ion-transport behavior and could assist with more efficient operation of LIBs.

In order to improve the feasibility and accuracy of explosive speed prediction, a combination of factor analysis and BP neural network method is proposed to propose an improved BP neural network prediction method. According to the raw data of the main influencing factors related to the explosion speed of explosives, the factor analysis method was used to process the dimensional data of the factors affecting the explosive speed of 20 explosives, and three common factors were obtained. The explosion rate of the original 13 explosives was replaced by three common factors. Influencing factors are used as the input layer parameters of BP neural network. The explosive explosion velocity prediction model combined with factor analysis and BP neural network method is established to predict the explosion velocity of explosives. The improved BP neural network prediction method is verified by the example data. The final verification result is that the relative average error between the predicted and actual values of the 15 training samples is 1.05%, which proves that the improved BP neural network model with training has a good fitting effect. The relative errors of the five predicted samples were 1.44%, 2.14%, 3.49%, 0.78%, and 1.95%, respectively, which were less than 10%, which proved that the improved BP neural network prediction model has better prediction accuracy.

In order to improve the feasibility and accuracy of the prediction model of blasting vibration velocity, a modified BP neural network prediction model of blasting vibration velocity was proposed by combining factor analysis method and BP neural network method. Select the case data to test the improved BP neural network network blasting vibration velocity prediction model. The test results: the relative average error between the predicted value and the actual value of the 14 training samples is 7.37%, and the relative error of the five prediction samples is 2.40% 2.23%, 3.68%, 2.39%, and 2.17% are all less than 10%, which proves that the improved BP neural network prediction model has better prediction accuracy.

The low Li+ transference number (tLi+), or the relatively small proportion of Li+ conductivity with respect to total ionic conductivity, has been identified as a key drawback of binary electrolytes. The lack of approaches to restrain anion mobility results in the poor cyclability of energy-dense electrodes as the high rate of anion movement induces concentration polarization. Herein, we propose regulating the ion conduction behavior using a nanocomposite separator fabricated via the functionalization of a glass fiber (GF) separator with a metal–organic framework (MOF). The open metal sites in the MOF serve as the anchoring sites for anions, and the resulting tLi+ is increased by 100%. The MOF-functionalized nanocomposite separator with high tLi+ substantially improves the electrochemical performance of a Li metal anode, with an areal capacity exceeding 2 mA h cm−2, and intercalation-type electrodes (LiFePO4 and Li4Ti5O12), with high active material loadings of 45 mg cm−2.

A hematite/amorphous sulfur composite was prepared via simple heating hematite and α-sulfur in Teflon-lined autoclave at low temperature. The composite was characterized by X-ray diffraction (XRD), Raman spectrum, Thermal Gravity Analysis (TGA), Transmission Electron Microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The results revealed that an allotrope sulfur at 5–37% weight percent was found in the composite. After sulfuration, Sn2- or S22- was doped in the lattice of hematite, large amounts of OH and SO4 were adsorbed on the surface of hematite. Hematite/amorphous sulfur composite had superior photo-Fenton activities than pure hematite. This work also demonstrated that amorphous sulfur also had the activity of photo-Fenton catalysis. OH- and SO4 radicals facilitated dye adsorption and acted as a bridge to link H2O2. Moreover, SO4 radicals on hematite served as electron trapping center that can receive photo-induced electron from conduction band of hematite and transfer it to the adsorbed H2O2, increasing the rate of photo-Fenton reaction eventually.

In order to improve the feasibility and accuracy of steam cloud explosion overpressure prediction, a combination of factor analysis method and BP neural network method is proposed to propose an improved BP neural network prediction method. According to the original data of the main influencing factors of steam cloud explosion overpressure, the factor analysis method is used to reduce the dimensional data of the five steam cloud explosion overpressure factors, and obtain a common factor; replace the original with a common factor. As an input layer parameter of BP neural network, a steam cloud explosion overpressure prediction model is established by combining the factor analysis method with BP neural network method to predict the overpressure of steam cloud explosion. The improved BP neural network prediction method is verified by the example data. The final verification result is that the relative average error between the predicted and actual values of the 15 training samples is 2.51%, which proves that the improved BP neural network model with training has a good fitting effect. The relative errors of the five predicted samples were 0.77%, 1.34%, 2.07%, 3.96%, and 6.27%, both of which were less than 10%, which proved that the improved BP neural network prediction model has better prediction accuracy.

In order to improve the feasibility and accuracy of predicting the explosion power of fuel air mixture, a method of BP neural network prediction is proposed by combining factor analysis method with BP neural network. Using factor analysis method, the original data of 9 fuel air mixture explosion power factors were processed by dimensionality reduction data, 2 common factors were obtained, and 2 common factors were substituted for 9 fuel air mixtures as input layer parameters of BP neural network. A prediction model of coal and gas outburst with the combination of factor analysis method and BP Neural network is established to predict the explosion power of fuel air mixture. The prediction model of the explosion power of the fuel air mixture is selected to verify the improved BP neural network prediction models, and the final results are as follows: The relative error range of four prediction samples is 0.16%-7.58%, all less than 10%. The improved BP neural network prediction method is used to solve the problem of the traditional BP neural network because of the excessive number of input layer parameters, low data processing efficiency, slow iteration rate and low precision, which provides a new research way for predicting the explosion power of fuel air mixture.

In order to improve the feasibility and accuracy of the roadbed settlement prediction model, the factor analysis method is combined with the BP neural network method, and an improved BP neural network roadbed settlement prediction model is proposed. Select example data to test the improved BP neural network roadbed settlement prediction model. The test results: The relative average error of the 10 sets of training samples’ predicted and actual roadbed settlements was 4.287%, and the roads of five predicted samples The relative error of subgrade settlement is 1.79%, 1.93%, 6.62%, 7.19%, 4.05%, all less than 10%, which proves that the improved BP neural network prediction model has good prediction accuracy.

Novel composite separators containing metal-organic-framework (MOF) particles and poly(vinyl alcohol) are fabricated by the electrospinning process. The MOF particles containing opened metal sites can spontaneously adsorb anions while allowing effective transport of lithium ions in the electrolyte, leading to dramatically improved lithium-ion transference number tLi + (up to 0.79) and lithium-ion conductivity. Meanwhile, the incorporation of the MOF particles alleviates the decomposition of the electrolyte, enhances the electrode reaction kinetics, and reduces the interface resistance between the electrolyte and the electrodes. Implementation of such composite separators in conventional lithium-ion batteries leads to significantly improved rate capability and cycling durability, offering a new prospective toward high-performance lithium-ion batteries.

Task allocation is a key problem for agent to reach cooperation in multi-agent systems. Lately task flows are replacing traditional static tasks, thus real-time dynamic task allocation mechanisms draw more attention. Though scheduling single task flow is well investigated, little work on allocation of multiple task flows has been done. In this paper a distributed and self-adaptable scheduling algorithm based on Q-learning for multiple task flows is proposed. This algorithm can not only adapt to task arrival process on itself, but also fully consider the influence from task flows on other agents. Besides, its distributed property guaranteed that it can be applied to open multi-agent systems with local view. Reinforcement learning makes allocation adapt to task load and node distribution. It is verified that this algorithm improves task throughput, and decreases average execution time per task.