Taiyuan University of Science and Technology

Abstract Zn‐based batteries are safe, low cost, and environmentally friendly, as well as delivering the highest energy density of all aqueous battery systems. However, the application of Zn‐based batteries is being seriously hindered by the uneven electrostripping/electroplating of Zn on the anodes, which always leads to enlarged polarization (capacity fading) or even cell shorting (low cycling stability). How a porous nano‐CaCO 3 coating can guide uniform and position‐selected Zn stripping/plating on the nano‐CaCO 3 ‐layer/Zn foil interfaces is reported here. This Zn‐deposition‐guiding ability is mainly ascribed to the porous nature of the nano‐CaCO 3 ‐layer, since similar functionality (even though relatively inferior) is also found in Zn foils coated with porous acetylene black or nano‐SiO 2 layers. Furthermore, the potential application of this strategy is demonstrated in Zn|ZnSO 4 +MnSO 4 |CNT/MnO 2 rechargeable aqueous batteries. Compared with the ones with bare Zn anodes, the battery with a nano‐CaCO 3 ‐coated Zn anode delivers a 42.7% higher discharge capacity (177 vs 124 mAh g −1 at 1 A g −1 ) after 1000 cycles.

Grid‐scale energy storage systems (ESSs) that can connect to sustainable energy resources have received great attention in an effort to satisfy ever‐growing energy demands. Although recent advances in Li‐ion battery (LIB) technology have increased the energy density to a level applicable to grid‐scale ESSs, the high cost of Li and transition metals have led to a search for lower‐cost battery system alternatives. Based on the abundance and accessibility of Na and its similar electrochemistry to the well‐established LIB technology, Na‐ion batteries (NIBs) have attracted significant attention as an ideal candidate for grid‐scale ESSs. Since research on NIB chemistry resurged in 2010, various positive and negative electrode materials have been synthesized and evaluated for NIBs. Nonetheless, studies on NIB chemistry are still in their infancy compared with LIB technology, and further improvements are required in terms of energy, power density, and electrochemical stability for commercialization. Most recent progress on electrode materials for NIBs, including the discovery of new electrode materials and their Na storage mechanisms, is briefly reviewed. In addition, efforts to enhance the electrochemical properties of NIB electrode materials as well as the challenges and perspectives involving these materials are discussed.

Research progress in biomass-derived porous carbon materials with different dimensions for supercapacitor electrodes.

Abstract Broadband near-infrared (NIR)-emitting phosphors are key for next-generation smart NIR light sources based on blue LEDs. To achieve excellent NIR phosphors, we propose a strategy of enhancing the crystallinity, modifying the micromorphology, and maintaining the valence state of Cr 3+ in Ca 3 Sc 2 Si 3 O 12 garnet (CSSG). By adding fluxes and sintering in a reducing atmosphere, the internal quantum efficiency (IQE) is greatly enhanced to 92.3%. The optimized CSSG:6%Cr 3+ exhibits excellent thermal stability. At 150 °C, 97.4% of the NIR emission at room temperature can be maintained. The fabricated NIR-LED device emits a high optical power of 109.9 mW at 520 mA. The performances of both the achieved phosphor and the NIR-LED are almost the best results until now. The mechanism for the optimization is investigated. An application of the NIR-LED light source is demonstrated.

Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.

Recommendation technology is an important part of the Internet of Things (IoT) services, which can provide better service for users and help users get information anytime, anywhere. However, the traditional recommendation algorithms cannot meet user's fast and accurate recommended requirements in the IoT environment. In the face of a large-volume data, the method of finding neighborhood by comparing whole user information will result in a low recommendation efficiency. In addition, the traditional recommendation system ignores the inherent connection between user's preference and time. In reality, the interest of the user varies over time. Recommendation system should provide users accurate and fast with the change of time. To address this, we propose a novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-means), called TCCF. The clustering method can cluster similar users together for further quick and accurate recommendation. Moreover, an effective and personalized recommendation model based on preference pattern (PTCCF) is designed to improve the quality of TCCF. It can provide a higher quality recommendation by analyzing the user's behaviors. The extensive experiments are conducted on two real datasets of MovieLens and Douban, and the precision of our model have improved about 5.2 percent compared with the MCoC model. Systematic experimental results have demonstrated our models TCCF and PTCCF are effective for IoT scenarios.

Internet of Things (IoT) equipment is usually in a harsh environment, and its security has always been a widely concerned issue. Node identity authentication is an important means to ensure its security. Traditional IoT identity authentication protocols usually rely on trusted third parties. However, many IoT environments do not allow such conditions, and are prone to single point failure. Blockchain technology with decentralization features provides a new solution for distributed IoT system. In this paper, a blockchain based multi-WSN authentication scheme for IoT is proposed. The nodes of IoT are divided into base stations, cluster head nodes and ordinary nodes according to their capability differences, which are formed to a hierarchical network. A blockchain network is constructed among different types of nodes to form a hybrid blockchain model, including local chain and public chain. In this hybrid model, nodes identity mutual authentication in various communication scenarios is realized, ordinary node identity authentication operation is accomplished by local blockchain, and cluster head node identity authentication are realized in public blockchain. The analysis of security and performance shows that the scheme has comprehensive security and better performance.

Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization (PSO) algorithm and a surrogate-assisted social learning-based PSO (SL-PSO) algorithm cooperatively search for the global optimum. The cooperation between the PSO and the SL-PSO consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the SL-PSO focuses on exploration while the PSO concentrates on local search. Empirical studies on six 50-D and six 100-D benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

Abstract With the innovation of microelectronics technology, the heat dissipation problem inside the device will face a severe test. In this work, cellulose aerogel (CA) with highly enhanced thermal conductivity (TC) in vertical planes was successfully obtained by constructing a vertically aligned silicon carbide nanowires (SiC NWs)/boron nitride (BN) network via the ice template-assisted strategy. The unique network structure of SiC NWs connected to BN ensures that the TC of the composite in the vertical direction reaches 2.21 W m −1 K −1 at a low hybrid filler loading of 16.69 wt%, which was increased by 890% compared to pure epoxy (EP). In addition, relying on unique porous network structure of CA, EP-based composite also showed higher TC than other comparative samples in the horizontal direction. Meanwhile, the composite exhibits good electrically insulating with a volume electrical resistivity about 2.35 × 10 11 Ω cm and displays excellent electromagnetic wave absorption performance with a minimum reflection loss of − 21.5 dB and a wide effective absorption bandwidth (< − 10 dB) from 8.8 to 11.6 GHz. Therefore, this work provides a new strategy for manufacturing polymer-based composites with excellent multifunctional performances in microelectronic packaging applications.

Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms.

Abstract Recently, the need for miniaturization and high integration have steered a strong technical wave in developing (micro‐)electronic devices. However, excessive amounts of heat may be generated during operation/charging, severely affecting device performance and leading to life/property loss. Benefiting from their low density, easy processing and low manufacturing cost, thermally conductive polymer composites have become a research hotspot to mitigate the disadvantage of excessive heat, with potential applications in 5G communication, electronic packaging and energy transmission. By far, the reported thermal conductivity coefficient (λ) of thermally conductive polymer composite is far from expectation. Deeper understanding of heat transfer mechanism is desired for developing next generation thermally conductive composites. This review holistically scopes current advances in this field, while giving special attention to critical factors that affect thermal conductivity in polymer composites as well as the thermal conduction mechanisms on how to enhance the λ value. This review covers critical factors such as interfacial thermal resistance, chain structure of polymer, intrinsic λ value of different thermally conductive fillers, orientation/configuration of nanoparticles, 3D interconnected networks, processing technology, etc. The applications of thermally conductive polymer composites in electronic devices are summarized. The existing problems are also discussed, new challenges and opportunities are prospected.

needs a cell voltage range of 1.8-2.4 V). Thus, developing cost-effective and robust transition metal electrocatalysts working at high current density is imperative and urgent for industrial electrocatalytic water splitting. In this review, the strategies and requirements for the design of self-supported electrocatalysts are summarized and discussed. Subsequently, the fundamental mechanisms of water electrolysis (OER or HER) are analyzed, and the required important evaluation parameters, relevant testing conditions and potential conversion in exploring electrocatalysts working at high current density are also introduced. Specifically, recent progress in the engineering of self-supported transition metal-based electrocatalysts for either HER or OER, as well as overall water splitting (OWS), including oxides, hydroxides, phosphides, sulfides, nitrides and alloys applied in the alkaline electrolyte at large current density condition is highlighted in detail, focusing on current advances in the nanostructure design, controllable fabrication and mechanistic understanding for enhancing the electrocatalytic performance. Finally, remaining challenges and outlooks for constructing self-supported transition metal electrocatalysts working at large current density are proposed. It is expected to give guidance and inspiration to rationally design and prepare these electrocatalysts for practical applications, and thus further promote the practical production of hydrogen via electrochemical water splitting.

Abstract Flexible electronics have emerged as an exciting research area in recent years, serving as ideal interfaces bridging biological systems and conventional electronic devices. Flexible electronics can not only collect physiological signals for human health monitoring but also enrich our daily life with multifunctional smart materials and devices. Conductive hydrogels (CHs) have become promising candidates for the fabrication of flexible electronics owing to their biocompatibility, adjustable mechanical flexibility, good conductivity, and multiple stimuli‐responsive properties. To achieve on‐demand mechanical properties such as stretchability, compressibility, and elasticity, the rational design of polymer networks via modulating chemical and physical intermolecular interactions is required. Moreover, the type of conductive components (eg, electron‐conductive materials, ions) and the incorporation method also play an important role in the conductivity of CHs. Electron‐CHs usually possess excellent conductivity, while ion‐CHs are generally transparent and can generate ion gradients within the hydrogel matrices. This mini review focuses on the recent advances in the design of CHs, introducing various design strategies for electron‐CHs and ion‐CHs employed in flexible electronics and highlighting their versatile applications such as biosensors, batteries, supercapacitors, nanogenerators, actuators, touch panels, and displays. image

Abstract Increasing closed pore volume in hard carbon is considered to be the most effective way to enhance the electrochemical performance in sodium‐ion batteries. However, there is a lack of systematic insights into the formation mechanisms of closed pores at molecular level. In this study, a regulation strategy of closed pores via adjustment of the content of free radicals is reported. Sufficient free radicals are exposed by part delignification of bamboo, which is related to the formation of well‐developed carbon layers and rich closed pores. In addition, excessive free radicals from nearly total delignification lead to more reactive sites during pyrolysis, which competes for limited precursor debris to form smaller microcrystals and therefore compact the material. The optimal sample delivers a large closed pore volume of 0.203 cm 3 g −1 , which leads to a high reversible capacity of 350 mAh g −1 at 20 mA g −1 and enhanced Na + transfer kinetics. This work provides insights into the formation mechanisms of closed pores at molecular level, enabling rational design of hard carbon pore structures.

Summary Both the problem of class imbalance in datasets and parameter selection of Support Vector Machine (SVM) are crucial to predict software defects. However, there is no one working to solve these problems synchronously at present. To tackle this problem, a hybrid multi‐objective cuckoo search under‐sampled software defect prediction model based on SVM (HMOCS‐US‐SVM) is proposed to solve synchronously above two problems. Firstly, a hybrid multi‐objective cuckoo search with dynamical local search (HMOCS) is utilized to select synchronously the non‐defective sampling and optimize the parameters of SVM. Then, three under‐sampled methods for decision region range are proposed to select the non‐defective modules. In the simulation, the three indicators, including the false positive rate (pf), the probability of detection (pd), and G‐mean, are employed to measure the performance of the proposed algorithm. In addition, eight datasets from Promise database are selected to verify the proposed software defect predication model. Comparing with the result of eight prediction models, the proposed method comes into effect on solving software defect prediction problem.

Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion, traditional data storing or processing techniques have the problem of low efficiency, and if the data are used maliciously, the security loss may be further caused. Multicloud is a high-performance secure computing platform, which combines multiple cloud providers for data processing, and the distributed multicloud platform ensures the security of data to some extent. Based on multicloud and task scheduling in IoT, this article constructs a many-objective distributed scheduling model, which includes six objectives of total time, cost, cloud throughput, energy consumption, resource utilization, and balancing load. Furthermore, this article presents a many-objective intelligent algorithm with sine function to implement the model, which considers the variation tendency of diversity strategy in the population is similar to the sine function. The experimental results demonstrate excellent scheduling efficiency and hence enhancing the security. This work provides a new idea for addressing the difficult problem of data processing in IoT.

Thermoelectric cooling technology has important applications for processes such as precise temperature control in intelligent electronics. The bismuth telluride (Bi 2 Te 3 )–based coolers currently in use are limited by the scarcity of Te and less-than-ideal cooling capability. We demonstrate how removing lattice vacancies through a grid-design strategy switched PbSe from being useful as a medium-temperature power generator to a thermoelectric cooler. At room temperature, the seven-pair device based on n-type PbSe and p-type SnSe produced a maximum cooling temperature difference of ~73 kelvin, with a single-leg power generation efficiency approaching 11.2%. We attribute our results to a power factor of >52 microwatts per centimeter per square kelvin, which was achieved by boosting carrier mobility. Our demonstration suggests a path for commercial applications of thermoelectric cooling based on Earth-abundant Te-free selenide-based compounds.

Abstract This work reports a novel approach for the synthesis of FeCo alloy nanoparticles (NPs) embedded in the N,P‐codoped carbon coated nitrogen‐doped carbon nanotubes (NPC/FeCo@NCNTs). Specifically, the synthesis of NCNT is achieved by the calcination of graphene oxide‐coated polystyrene spheres with Fe 3+ , Co 2+ and melamine adsorbed, during which graphene oxide is transformed into carbon nanotubes and simultaneously nitrogen is doped into the graphitic structure. The NPC/FeCo@NCNT is demonstrated to be an efficient and durable bifunctional catalyst for oxygen evolution (OER) and oxygen reduction reaction (ORR). It only needs an overpotential of 339.5 mV to deliver 10 mA cm −2 for OER and an onset potential of 0.92 V to drive ORR. Its bifunctional catalytic activities outperform those of the composite catalyst Pt/C + RuO 2 and most bifunctional catalysts reported. The experimental results and density functional theory calculations have demonstrated that the interplay between FeCo NPs and NCNT and the presence of N,P‐codoped carbon structure play important roles in increasing the catalytic activities of the NPC/FeCo@NCNT. More impressively, the NPC/FeCo@NCNT can be used as the air‐electrode catalyst, improving the performance of rechargeable liquid and flexible all‐solid‐state zinc–air batteries.

In solving many real-world optimization problems, neither mathematical functions nor numerical simulations are available for evaluating the quality of candidate solutions. Instead, surrogate models must be built based on historical data to approximate the objective functions and no new data will be available during the optimization process. Such problems are known as offline data-driven optimization problems. Since the surrogate models solely depend on the given historical data, the optimization algorithm is able to search only in a very limited decision space during offline data-driven optimization. This paper proposes a new offline data-driven evolutionary algorithm to make the full use of the offline data to guide the search. To this end, a surrogate management strategy based on ensemble learning techniques developed in machine learning is adopted, which builds a large number of surrogate models before optimization and adaptively selects a small yet diverse subset of them during the optimization to achieve the best local approximation accuracy and reduce the computational complexity. Our experimental results on the benchmark problems and a transonic airfoil design example show that the proposed algorithm is able to handle offline data-driven optimization problems with up to 100 decision variables.