Search

Abstract In order to maximize the work efficiency of wireless mobile charger, a payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi‐node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade‐off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.

The movement towards electrification is entailing deep changes in power systems. On the demand side, the adoption of electric heating (EH) has grown rapidly in recent years. To eliminate the voltage fluctuations caused by the random switching‐on/‐off behaviors of EHs in some application scenes with special‐power‐line in low voltage distribution networks (LVDNs), a swarm‐intelligence‐based coordinated control approach of EHs for voltage stabilization with zero communication burden while guaranteeing the users' thermal comforts is researched. A novel bird‐perch‐on‐branch (BPB) ‐based swarm intelligence theory is proposed, which reveals the mapping relation between local observations and global states. On this theoretical basis, a multi‐agent reinforcement learning (MARL) framework is developed for the coordinated dispatch of EHs, where the deep‐Q‐network (DQN) algorithm is adopted to learn and generate the optimal control policy for each EH. The implementation of the proposed MADQN‐BPB approach is described based on the principle of centralized‐training and decentralized‐execution. Comparative control performances of MADQN‐BPB with a commercially available approach, and the global optimal solution are evaluated. Simulation results verify the capability of MADQN‐BPB in voltage stabilization with zero communication burden. Its control performance is close to that of the global optimal solution, and is scalable to the variations of environment. [ABSTRACT FROM AUTHOR]

The development of electric vehicles (EV) is constrained by factors such as the spatial distribution and planning volume of charging infrastructure. In order to determine the planning volume of charging piles in charging stations and the spatial layout of charging infrastructure service area within the service radius of Tianjin, this paper firstly determines the service area demand based on the service radius, combined with the demand prediction function and the EV ownership, and secondly forms the charging infrastructure service area with the central place theory large spatial layout, and finally determine the planning volume of charging infrastructure through queuing service system. The research results can provide a new theoretical basis for the layout planning of charging infrastructure, and finally put forward the relevant factors and development trends for the optimal layout of charging facilities.

The preparation of ultra–small silver nanoparticles (average diameter ≤10 nm) is a technical problem because of their aggregation tendency. Therefore, the preparation conditions are usually harsh and complex. It is widely recognized that covalent organic frameworks (COFs) represent a novel class of porous crystalline compounds with broad applications in various fields. In this work, an environmentally and economically friendly new method has been developed for synthesizing ultra–small silver nanoparticles by reducing silver ions in ethanol solution with a covalent organic framework (TPB–DMTP–COF) as a template and reductant at room temperature. Interestingly, the TPB–DMTP–COF not only serves as a template and reductant but also functions as a stabilizer. Moreover, the TPB–DMTP–COF can be utilized repeatedly as a reductant. The morphology and structure of the silver nanoparticles were examined using transmission electron microscopy and showed that they were spherical with an average particle size of approximately 7.5 nm. Compared with the previously reported silver nanoparticles, the silver nanoparticles synthesized by this new method are not coated with Surfactant, such as Polyvinylpyrrolidone (PVP). As a result, they possess a larger exposed surface area, exhibit increased activity, and demonstrate excellent antimicrobial properties against both Gram–negative Escherichia coli and Gram–positive bacteria Staphylococcus aureus. [ABSTRACT FROM AUTHOR]

Human activities, such as mining and manufacturing, expose society and the natural environment to harmful levels of metal ions. Recently, optical sensor arrays for metal ion detection have become popular owing to their favourable features, such as facile sample preparation and the requirement of less expensive instrumentation compared to traditional, spectrometry‐based analysis techniques. Sensor arrays usually consist of numerous optical probes that are used in combination to generate unique analyte responses. In contrast, here we present an array that comprises a single fluorescent sensor, Coum4‐DPA, that produces unique responses to metal ions in different pH environments. With this simple sensing platform, we were able to classify 10 metal ions in different water sources and quantify Pb2+ in tap water using just one fluorescent sensor, a few pH buffers and two sets of spectral data. This novel approach significantly decreases time and costs associated with probe synthesis and data collection, making it highly transferrable to real‐world metal sensing applications. [ABSTRACT FROM AUTHOR]

Human activities, such as mining and manufacturing, expose society and the natural environment to harmful levels of metal ions. Recently, optical sensor arrays for metal ion detection have become popular owing to their favourable features, such as facile sample preparation and the requirement of less expensive instrumentation compared to traditional, spectrometry-based analysis techniques. Sensor arrays usually consist of numerous optical probes that are used in combination to generate unique analyte responses. In contrast, here we present an array that comprises a single fluorescent sensor, Coum4-DPA, that produces unique responses to metal ions in different pH environments. With this simple sensing platform, we were able to classify 10 metal ions in different water sources and quantify Pb 2+ in tap water using just one fluorescent sensor, a few pH buffers and two sets of spectral data. This novel approach significantly decreases time and costs associated with probe synthesis and data collection, making it highly transferrable to real-world metal sensing applications., (© 2022 The Authors. Chemistry - An Asian Journal published by Wiley-VCH GmbH.)