No abstract

excellent rate performance, and fast reaction kinetics. [1][2][3][4] However, the inadequate cycling performance of Zn metal anode (mainly including Zn foil) originates from the growth of Zn dendrites and other side reactions have impeded its further development and extensive commercialization. [5] To date, many efforts have been devoted to regulate the surface structure and characteristics of Zn foil to induce Zn nucleation and growth on the surface of Zn foil via various strategies such as surface coating of Zn foil and modification of electrolyte, [6][7][8] which have greatly enhanced the electrochemical performances of Zn foil. The inherent defects of planar Zn foil are always affecting its practical application, and there are three major factors involved: i) the 2D plane structure of Zn foil can cause spontaneous dendrite formation along the vertical direction on the surface of the Zn foil, which can easily penetrate the separator and cause short circuit of battery; ii) severe pulverization of Zn foil can occur during the charge/ discharge cycles at high current density, resulting in electric contact failure; and iii) the redundant thickness of Zn foil results in its poor utilization in application. [9,10] Compared with Zn foil, Zn powder (Zn-P) is a valuable substitute for Zn foil owing to its low cost and easily available. More importantly, the content of Zn-P can be accurately controlled, Zn powder (Zn-P)-based anodes are always regarded as ideal anode candidates for zinc ion batteries owing to their low cost and ease of processing. However, the intrinsic negative properties of Zn-P-based anodes such as easy corrosion and uncontrolled dendrite growth have limited their further applications. Herein, a novel 3D cold-trap environment printing (3DCEP) technology is proposed to achieve the MXene and Zn-P (3DCEP-MXene/Zn-P) anode with highly ordered arrangement. Benefitting from the unique inhibition mechanism of high lattice matching and physical confinement effects within the 3DCEP-MXene/Zn-P anode, it can effectively homogenize the Zn 2+ flux and alleviate the Zn deposition rate of the 3DCEP-MXene/Zn-P anode during Zn plating-stripping. Consequently, the 3DCEP-MXene/Zn-P anode exhibits a superior cycling lifespan of 1400 h with high coulombic efficiency of ≈9.2% in symmetric batteries. More encouragingly, paired with MXene and Co doped MnHCF cathode via 3D cold-trap environment printing (3 DCEP-MXene/Co-MnHCF), the 3DCEP-MXene/Zn-P//3DCEP-MXene/Co-MnHCF full battery delivers high cyclic durability with the capacity retention of 95.7% after 1600 cycles. This study brings an inspired universal pathway to rapidly fabricate a reversible Zn anode with highly ordered arrangement in a cold environment for micro-zinc storage systems.

The identification of potential drug-target interaction pairs is very important, which is useful not only for providing greater understanding of protein function, but also for enhancing drug research, especially for drug function repositioning. Recently, numerous machine learning-based algorithms (e.g. kernel-based, matrix factorization-based and network-based inference methods) have been developed for predicting drug-target interactions. All these methods implicitly utilize the assumption that similar drugs tend to target similar proteins and yield better results for predicting interactions between drugs and target proteins. To further improve the accuracy of prediction, a new method of network-based label propagation with mutual interaction information derived from heterogeneous networks, namely LPMIHN, is proposed to infer the potential drug-target interactions. LPMIHN separately performs label propagation on drug and target similarity networks, but the initial label information of the target (or drug) network comes from the drug (or target) label network and the known drug-target interaction bipartite network. The independent label propagation on each similarity network explores the cluster structure in its network, and the label information from the other network is used to capture mutual interactions (bicluster structures) between the nodes in each pair of the similarity networks. As compared to other recent state-of-the-art methods on the four popular benchmark datasets of binary drug-target interactions and two quantitative kinase bioactivity datasets, LPMIHN achieves the best results in terms of AUC and AUPR. In addition, many of the promising drug-target pairs predicted from LPMIHN are also confirmed on the latest publicly available drug-target databases such as ChEMBL, KEGG, SuperTarget and Drugbank. These results demonstrate the effectiveness of our LPMIHN method, indicating that LPMIHN has a great potential for predicting drug-target interactions.