Boosting Piezo-Catalytic Activity of KNN-Based Materials with Phase Boundary and Defect Engineering

Although the piezo-catalysis is promising for the environmental remediation and biomedicine, the piezo-catalytic properties of various piezoelectric materials are limited by low carrier concentrations and mobility, and rapid electron-hole pair recombination, and reported regulating strategies are quite complex and difficult. Herein, a new and simple strategy, integrating phase boundary engineering and defect engineering, to boost the piezo-catalytic activity of potassium sodium niobate ((K, Na)NbO₃, KNN) based materials is innovatively proposed. Tur strategy is validated by exampling 0.96(K_0.48Na_0.52)Nb_0.955Sb_0.045O₃-0.04(Bi_xNa_4-3x)_0.5ZrO₃-0.3%Fe₂O₃ material having phase boundary engineering and conducted the defect engineering via the high-energy sand-grinding. A high reaction rate constant k of 92.49 × 10⁻³ min⁻¹ in the sand-grinding sample is obtained, which is 2.40 times than that of non-sand-grinding one and superior to those of other representative lead-free perovskite piezoelectric materials. Meanwhile, the sand-grinding sample has remarkable bactericidal properties against Escherichia coli and Staphylococcus aureus. Superior piezo-catalytic activities originate from the enhanced electron-hole pair separation and the increased carrier concentration. This study provides a novel method for improving the piezo-catalytic activities of lead-free piezoelectric materials and holds great promise for harnessing natural energy and disease treatment.     

Intercalative Motifs-Induced Space Confinement and Bonding Covalency Enhancement Enable Ultrafast and Large Sodium Storage

Alloying-type metal sulfides with high theoretical capacities are promising anodes for sodium-ion batteries, but suffer from sluggish sodiation kinetics and huge volume expansion. Introducing intercalative motifs into alloying-type metal sulfides is an efficient strategy to solve the above issues. Herein, robust intercalative In S motifs are grafted to high-capacity layered Bi₂S₃ to form a cation-disordered (BiIn)₂S₃, synergistically realizing high-rate and large-capacity sodium storage. The In S motif with strong bonding serves as a space-confinement unit to buffer the volume expansion, maintaining superior structural stability. Moreover, the grafted high-metallicity Indium increases the bonding covalency of Bi S, realizing controllable reconstruction of Bi S bond during cycling to effectively prevent the migration and aggregation of atomic Bi. The novel (BiIn)₂S₃ anode delivers a high capacity of 537 mAh g⁻¹ at 0.4 C and a superior high-rate stability of 247 mAh g⁻¹ at 40 C over 10000 cycles. Further in situ and ex situ characterizations reveal the in-depth reaction mechanism and the breakage and formation of reversible Bi S bonds. The proposed space confinement and bonding covalency enhancement strategy via grafting intercalative motifs can be conducive to developing novel high-rate and large-capacity anodes.     

Functional Materials for Memristor-Based Reservoir Computing: Dynamics and Applications

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain-inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high-dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor-implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor-implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on-demand application scenarios. Furthermore, recent advances in the application of memristor-based physical RC systems are surveyed. In the end, the further prospects of memristor-implemented RC system in a material view are envisaged.     

Blockchain Security Technology Based on the Asynchronous Transmission Mode of IoT Technology in Smart Cities

Wireless Personal Communications - With the rapid development of information technology, issues such as network security and privacy protection have attracted more and more attention. The...     

分布式光伏发电系统的应用研究

随着新能源的快速发展,分布式光伏发电得到普遍推广应用,在碳达峰和碳中和的政策背景下,光伏发电从以集中式地面电站为主逐步过渡到分布式发电遍地开花的应用阶段,既考验电力系统的承载能力,同时促进新型电力系统网络架构的加速建设,本文通过对分布式光伏发电多种应用形式的研究介绍,结合当前存在的一些问题提出改善建议。     

A joint model based on interactive gate mechanism for spoken language understanding

Applied Intelligence - Slot filling and intent detection are two important tasks in a spoken language understanding (SLU) system, it is becoming a tendency that two tasks are jointing learn in SLU....     

我国细菌耐药问题的现状和防控策略

细菌耐药危机日趋严峻,已经成为全球范围内的重大公共安全问题,严重危害人类、动物的健康和生态环境。本文总结了我国各领域细菌耐药问题的现状,分析了加剧细菌耐药性的直接原因,揭示了细菌耐药问题对国民健康、食品安全、生态环境、经济发展等可能造成的危害,整理了我国目前开展的防控行动以及取得的主要成果,最后针对我国细菌耐药防控在耐药监测、抗生素监管、危害风险评估、人才队伍建设等方面的不足,提出了多领域合作、多部门联防联控的细菌耐药防控策略。     

Life cycle assessment and optimization of an iron making system with a combined cycle power plant: a case study from China

In the steel industry, the iron making system deals with large quantities of materials and energy and so it can play a critical role in reducing emissions and production costs. More specifically, excess by-product gases should be used for electricity generation; otherwise, they lead to pollution. A life cycle analysis is performed to compare the environmental impact of an iron making system with a combined cycle power plant (CCPP), to a system producing the same amount of electricity in a coal power plant. The results for a Chinese steel plant show a 33% reduction in the energy conservation and emission reduction potential for the CCPP system, which is thus more environmentally friendly. A mathematical programming formulation is then proposed for optimal scheduling. It incorporates key technological constraints and is sensitive to hourly changing electricity prices. The outcome is a 19% increase in revenue from electricity sales compared to a schedule that does not dynamically adjust to the price profile. The results also show that emissions from by-product gases can be avoided completely. The paper ends with a sensitivity analysis to evaluate the impact of changes in product demand, gas storage and CCPP capacity, and emission cost.     

Bootstrapping Analysis of Lifetime Data with Subsampling

Because of cost and time limitations, reliability experiments frequently contain subsampling, which is a restriction on randomization. A two‐stage approach can analyze right censored Weibull distributed reliability data with subsampling. However, in implementing such a method, we found that it did not address the problems of how to perform confidence intervals of low percentiles and reduce the bias of estimates. In this paper, we present a two‐stage bootstrapping approach and an unbiasing factor approach to solve the aforementioned problems. An example is provided to illustrate the proposed method. In addition, the proposed method is compared with existing methods through simulation. The resulting simulations show that the proposed method performs well in low percentiles. Copyright © 2015 John Wiley & Sons, Ltd.