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Water loss of the gel polymer electrolytes (GPEs) and dendrites growth on Zn anode are overriding obstacles to applying flexible zinc‐air batteries (ZABs) for wearable electronic devices. Nearly all previous efforts aim at developing novel GPEs with enhanced water retention and therefore elongate their lifespan. Herein, a facile interface engineering strategy is proposed to retard the water loss of GPE from the half‐open structured air cathode. In detail, the poly(ethylene vinyl acetate)/carbon powder (PEVA‐C) nanofiber composite interface layer with features of hydrophobicity, high conductivity, air permeability, and flexibility are prepared on the carbon cloth and set up between the GPE and electrode. The as‐assembled ZAB with simple alkaline PVA GPE exhibits an impressive cycle life of 230 h, which outperforms ZAB without the PEVA‐C nanofibers interface layer by 14 times. Additionally, the growth of Zn dendrites can be suppressed due to the tardy water loss of GPE.

Wireless channel modeling has always been one of the most fundamental highlights of the wireless communication research. The performance of new advanced models and technologies heavily depends on the accuracy of the wireless CSI (Channel State Information). This study examined the randomness of the wireless channel parameters based on the characteristics of the radio propagation environment. The diversity of the statistical properties of wireless channel parameters inspired us to introduce the concept of the tomographic channel model. With this model, the static part of the CSI can be extracted from the huge amount of existing CSI data of previous measurements, which can be defined as the wireless channel feature. In the proposed scheme for obtaining CSI with the tomographic channel model, the GMM (Gaussian Mixture Model) is applied to acquire the distribution of the wireless channel parameters, and the CNN (Convolutional Neural Network) is applied to automatically distinguish different wireless channels. The wireless channel feature information can be stored offline to guide the design of pilot symbols and save pilot resources. The numerical results based on actual measurements demonstrated the clear diversity of the statistical properties of wireless channel parameters and that the proposed scheme can extract the wireless channel feature automatically with fewer pilot resources. Thus, computing and storage resources can be exchanged for the finite and precious spectrum resource.Keywords: wireless channel modeling, tomographic channel model, Gaussian mixture model, convolutional neural network

Metal single atoms show outstanding electrocatalytic activity owing to the abundant atomic reactive sites and superior stability. However, the preparation of single atoms suffers from inexorable metal aggregation which is harmful to electrocatalytic activity. Here, ionic covalent organic frameworks (iCOFs) are employed as the sacrificial precursor to mitigate the metal aggregation and subsequent formation of bulky particles. Molecular dynamics simulation shows that iCOFs can trap and confine more Co ions as compared to neutral COFs, resulting in the formation of a catalyst composed of Co single atoms and uniformly distributed Co nanoparticles (CoSA&CoNP‐10). However, the neutral COFs derive a catalyst composed of Co atomic clusters and large Co nanoparticles (CoAC&CoNP‐25). The CoSA&CoNP‐10 catalyst exhibits higher oxygen bifunctional electrocatalytic activities than CoAC&CoNP‐25, coinciding with the density functional theory results. Taking the CoSA&CoNP‐10 as the air cathode in Zn–air batteries (ZABs), the aqueous ZAB presents a high power density of 181 mW cm−2, a specific capacity of 811 mAh g−1 as well as a long cycle life of 407 h at a current density of 10 mA cm−2, while the quasi‐solid state ZAB displays a power density of 179 mW cm−2 and the cycle life of 30 h.

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