Critical Review of Fluorinated Electrolytes for High-Performance Lithium Metal Batteries

Lithium metal batteries (LMBs), due to their ultra-high energy density, are attracting tremendous attentions. However, their commercial application is severely impeded by poor safety and unsatisfactory cycling stability, which are induced by lithium dendrites, side reactions, and inferior anodic stability. Electrolytes, as the indispensable and necessary components in lithium metal batteries, play a crucial role in regulating the electrochemical performance of LMBs. Recently, the fluorinated electrolytes are widely investigated in high-performance LMBs. Thus, the design strategies of fluorinated electrolytes are thoroughly summarized, including fluorinated salts, fluorinated solvents, and fluorinated additives in LMBs, and insights of the fluorinated components in suppressing lithium dendrites, improving anodic stability and cycling stability. Finally, an outlook with several design strategies and challenges will be proposed for novel fluorinated electrolytes.     

GM2NAS: multitask multiview graph neural architecture search

Knowledge and Information Systems - Graph neural network-based multitask learning models on multiview graphs have achieved acceptable results in different real-world applications. However,...     

On measuring network robustness for weighted networks

Knowledge and Information Systems - Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely...     

高性能罗兰-C接收天线设计

为了提高罗兰-C磁天线的接收能力,设计了一种顺接串联多层线圈的磁天线。理论推导了顺接串联多层线圈磁天线的信噪比和灵敏度,最终设计的磁天线大小为150 mm×150 mm。远场测试接收到了较高信噪比的罗兰-C信号,在相同信号调理电路条件下,与单层单组线圈的磁天线相比,顺接串联多层线圈的磁天线接收到3.47 V的电压信号,且信噪比高达30.87 dB,灵敏度和信噪比分别提高了82.42%和63.20%。顺接串联多层线圈磁天线提高了罗兰-C磁天线的灵敏度和信噪比,对罗兰-C系统在定位方面的应用具有重要意义。     

A novel approach of data race detection based on CNN-BiLSTM hybrid neural network

Neural Computing and Applications - Existing data race detection approaches based on deep learning are suffering from the problems of unique feature extraction and low accuracy. To this end, this...     

Epitaxial growth and thermal-conductivity limit of single-crystalline Bi2Se3/In2Se3 superlattices on mica

Nano Research - Thermal transport in superlattices is governed by various phonon-scattering processes. For extracting the phonon-scattering contribution of hetero-interfaces in chalcogenide...     

复合型导电高分子材料研究进展

简述了复合型导电高分子材料的研究及发展,并对其导电机理、基体树脂、导电填料的研究概况进行了述评,介绍了其在各领域中的应用．     

WBMOAIS: A novel artificial immune system for multiobjective optimization

This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.