A Stable Polymer-based Solid-State Lithium Metal Battery and its Interfacial Characteristics Revealed by Cryogenic Transmission Electron Microscopy

Solid-state lithium metal batteries (SSLMBs) are a promising candidate for next-generation energy storage systems due to their intrinsic safety and high energy density. However, they still suffer from poor interfacial stability, which can incur high interfacial resistance and insufficient cycle lifespan. Herein, a novel poly(vinylidene fluoride‑hexafuoropropylene)-based polymer electrolyte (PPE) with LiBF₄ and propylene carbonate plasticizer is developed, which has a high room-temperature ionic conductivity up to 1.15 × 10⁻³ S cm⁻¹ and excellent interfacial stability. Benefitting from the stable interphase, the PPE-based symmetric cell can operate for over 1000 h. By virtue of cryogenic transmission electron microscopy (Cryo-TEM) characterization, the high interfacial compatibility between Li metal anode and PPE is revealed. The solid electrolyte interphase is made up of an amorphous outer layer that can keep intimate contact with PPE and an inner Li₂O-dominated layer that can protect Li from continuous side reactions during battery cycling. A LiF-rich transition layer is also discovered in the region of PPE close to Li metal anode. The feasibility of investigating interphases in polymer-based solid-state batteries via Cryo-TEM techniques is demonstrated, which can be widely employed in future to rationalize the correlation between solid-state electrolytes and battery performance from ultrafine interfacial structures.     

Simulation and analysis of edge ghosting for microcapsule e-paper based on particle dynamics and light scattering model

Electronic paper (e-paper) is a reflective display technology with unique advantages, such as bistability, low-power consumption, and high ambient contrast ratio. These features make e-paper a promising candidate for future Internet of Things applications. Among different technologies of e-paper, electrophoretic display (EPD) is the most successful one for commercialization. However, the edge ghosting (also known as the fringing effect) still limits the performance of EPD. Herein, we established a model of particle dynamics of electrophoresis, simulated the edge ghosting of microcapsule EPD, analyzed the edge ghosting effect, and revealed the relationship between thicknesses, dielectric constants of the back binder layer, and the edge ghosting. Two EPD panels with different thicknesses of back binder layer were demonstrated, which verifies the accuracy of this simulation model. With the proposed model, many device mechanisms and product issues can be analyzed and illustrated, which is supposed to guide the researchers in optimizing the device structure design of EPD.     

振动抑制与能量俘获专刊序

工程系统中梁结构经常处于各种激励的作用下,因而梁结构在这种环境下不可避免地发生着各种各样的强迫振动.在梁结构发生振动的过程中,其自身会受到的温度、湿度、电磁场、裂纹等众多内外部因素的影响,而众多内外部因素就构成梁的多物理场耦合环境.在多场耦合环境下,Green函数法作为一种解析方法在研究梁的多场耦合振动问题方面具有优势,有利于讨论力、电、热、裂纹等因素作用下梁的振动特性和多场耦合特性.Green函数法相比于模态叠加法,优点在于能够得到完整且精度较高的解析解,具有收敛性好,运算快的特点.本文主要阐述Green函数在梁的强迫振动、热力耦合振动、力电耦合振动、裂纹梁振动等研究问题上取得了大量的理论和工程研究成果.本文以裂纹为内因,热、力、电为外因进行分类,阐述了在内外因影响下梁的强迫振动问题Green函数解的研究现状,从而让读者进一步系统性的了解Green函数法在振动领域中的广泛应用,以及了解该方法本身的特色和优势奠定基础.     

Event-triggered-based integral reinforcement learning output feedback optimal control for partially unknown constrained-input nonlinear systems

In this paper, an adaptive output feedback event-triggered optimal control algorithm is proposed for partially unknown constrained-input continuous-time nonlinear systems. First, a neural network observer is constructed to estimate unmeasurable state. Next, an event-triggered condition is established, and only when the event-triggered condition is violated will the event be triggered and the state be sampled. Then, an event-triggered-based synchronous integral reinforcement learning (ET-SIRL) control algorithm with critic-actor neural networks (NNs) architecture is proposed to solve the event-triggered Hamilton–Jacobi–Bellman equation under the established event-triggered condition. The critic and actor NNs are used to approximate cost function and optimal event-triggered optimal control law, respectively. Meanwhile, the event-triggered-based closed-loop system state and all the neural network weight estimation errors are uniformly ultimately bounded proved by Lyapunov stability theory, and there is no Zeno behavior. Finally, two numerical examples are presented to show the effectiveness of the proposed ET-SIRL control algorithm.     

Implicit discrete-time fast terminal sliding mode control with disturbance compensation

In this paper, an implicit discrete-time fast terminal sliding mode (DFTSM) control with disturbance compensation is designed and analyzed for uncertain high-order systems. First, a recursive discrete sliding surface is constructed based on implicit Euler technique, which can completely eliminate discretization chattering so as to significantly reduce the boundary layer of sliding mode motion. With the help of a high-order disturbance compensator, the accuracy limitation of implicit DFTSM control systems is overcome by increasing the order of sliding mode. Then the finite-time convergence of implicit DFTSM is proved for the first time, and the influence relationship of control parameters on the convergence speed and control accuracy of the algorithm is established. Finally, two numerical examples are provided to demonstrate the effectiveness and superiority of the proposed design approach.     

Energy Efficient Cluster Based Routing Protocol for WSN Using Firefly Algorithm and Ant Colony Optimization

Maximizing network lifetime is the main goal of designing a wireless sensor network. Clustering and routing can effectively balance network energy consumption and prolong network lifetime. This paper presents a novel cluster-based routing protocol called EECRAIFA. In order to select the optimal cluster heads, Self-Organizing Map neural network is used to perform preliminary clustering on the network nodes, and then the relative reasonable level of the cluster, the cluster head energy, the average distance within the cluster and other factors are introduced into the firefly algorithm (FA) to optimize the network clustering. In addition, the concept of decision domain is introduced into the FA to further disperse cluster heads and form reasonable clusters. In the inter-cluster routing stage, the inter-cluster routing is established by an improved ant colony optimization (ACO). Considering factors such as the angle, distance and energy of the node, the heuristic function is improved to make the selection of the next hop more targeted. In addition, the coefficient of variation in statistics is introduced into the process of updating pheromones, and the path is optimized by combining energy and distance. In order to further improve the network throughput, a polling control mechanism based on busy/idle nodes is introduced during the intra-cluster communication phase. The simulation experiment results prove that under different application scenarios, EECRAIFA can effectively balance the network energy consumption, extend the network lifetime, and improve network throughput.

     

基于目标容量的网络化雷达功率分配方案

针对现有网络化雷达功率资源利用率低的问题,该文提出一种基于目标容量的功率分配(TC-PA)方案以提升保精度跟踪目标个数.TC-PA方案首先将网络化雷达功率分配模型制定为非光滑非凸优化问题;而后引入Sigmoid函数将原问题松弛为光滑非凸优化问题;最后运用近端非精确增广拉格朗日乘子法(PI-ALMM)对松弛后的非凸问题进行求解.仿真结果表明,PI-ALMM对于求解线性约束非凸优化问题可以较快地收敛到一个稳态点.另外,相比传统功率均分方法和遗传算法,所提TC-PA方案可以最大限度地提升目标容量.     

Relation network based on multi-granular hypergraphs for person re-identification

Applied Intelligence - In recent years, person re-identification (re-ID) has become a widespread research topic that focuses on retrieving target pedestrians from a set of images, typically taken...     

Multi-scale Interest Dynamic Hierarchical Transformer for sequential recommendation

Neural Computing and Applications - Existing sequential recommendation methods focus on modeling the temporal relationships of users’ historical behaviors and excel in exploiting users’...     

Reducing Hysteresis and Enhancing Performance of Perovskite Solar Cells Using Low‐Temperature Processed Y‐Doped SnO2 Nanosheets as Electron Selective Layers

Despite the rapid increase of efficiency, perovskite solar cells (PSCs) still face some challenges, one of which is the current–voltage hysteresis. Herein, it is reported that yttrium‐doped tin dioxide (Y‐SnO₂) electron selective layer (ESL) synthesized by an in situ hydrothermal growth process at 95 °C can significantly reduce the hysteresis and improve the performance of PSCs. Comparison studies reveal two main effects of Y doping of SnO₂ ESLs: (1) it promotes the formation of well‐aligned and more homogeneous distribution of SnO₂ nanosheet arrays (NSAs), which allows better perovskite infiltration, better contacts of perovskite with SnO₂ nanosheets, and improves electron transfer from perovskite to ESL; (2) it enlarges the band gap and upshifts the band energy levels, resulting in better energy level alignment with perovskite and reduced charge recombination at NSA/perovskite interfaces. As a result, PSCs using Y‐SnO₂ NSA ESLs exhibit much less hysteresis and better performance compared with the cells using pristine SnO₂ NSA ESLs. The champion cell using Y‐SnO₂ NSA ESL achieves a photovoltaic conversion efficiency of 17.29% (16.97%) when measured under reverse (forward) voltage scanning and a steady‐state efficiency of 16.25%. The results suggest that low‐temperature hydrothermal‐synthesized Y‐SnO₂ NSA is a promising ESL for fabricating efficient and hysteresis‐less PSC.