Abstract Due to issues of hydrogen evolution, corrosion, and uncontrolled deposition behaviors at the Zn anode, the practical implementation of Zn‐ion batteries has faced significant obstacles. Very limited attention is directed toward various alloy crystalline states for the Zn anode protection primarily due to the challenge of synthesizing high‐quality alloy coatings with diverse crystalline states. In this study, the crystalline state of NiCr alloy coating is precisely manipulated using magnetron sputtering, re…

Thermogalvanic cells (TGCs) draw great attention in the field of heat to electricity conversion, but TGCs were only in the form of liquid or organic gel. Here, we report an all-inorganic hydrogel TGC via simply mixing and stirring two inorganic salt solutions. Benefiting from the hydrogen bonds resultant framework and endogenous Fe 2+/3+ redox couple, the TGC can recurrently pulverize-gel with completely holding its initial thermogalvanic performances after even 60 cycles. As the temperature and pH coregulating Fe…

Abstract Zn anode protection in Zn‐ion batteries (ZIBs) face great challenges of high Zn utilization rate (i.e., depth of discharge, DOD) and high current density due to the large difficulty in obtaining an extreme overall RTC (relative texture coefficient) of Zn (002) plane. Through the potent interaction of Mn(III) aq and H + with distinct Zn crystal planes under an electric field, large‐size Zn foils with a breakthrough (002) plane RTC of 99 % (i.e., close to Zn single crystal) are electrodeposited on texture‐l…

According to the characteristics and teaching objectives of the Experimental Course of Big Data Architecture and Mode, a P2G mode teaching mode is proposed and practiced after analyzing and summarizing the problem in the teaching process.P2G teaching mode means point-to-group teaching mode, in which we can divide all students who need to learn the Experimental Course of Big Data Architecture and Mode into different groups.In this paper, we use clustering algorithm to complete the grouping of several forums.We try …

Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications. The graph classification problem is one of the central problems in graph neural networks, and aims to predict the label of a graph with the help of training graph neural networks over graph-structural datasets. The graph pooling scheme is an important part of graph neural networks for the graph classification objective. Previous wor…