Realizing dendrite-free lithium deposition with three-dimensional soft-rigid nanofiber interlayers.

Lithium (Li) metal is regarded as the most desirable anode candidates for high-energy-density batteries by virtue of its lowest redox potential and ultrahigh theoretical specific capacity. However, uncontrollable Li dendritic growth, infinite volume variation and unstable solid electrolyte interface (SEI) ineluctably plague its commercialization process. Herein, the three-dimensional (3D) nanofiber functional layers with synergistic soft-rigid feature, consisting of tin oxide (SnO2)-anchored polyvinylidene fluoride (PVDF) nanofibers, are directly electrospun on copper current collector. This strategy can effectively regulate uniform Li deposition and strengthen SEI stability through the dual effect of physical accommodation and chemical ionic intervention. On the one hand, the nanofiber interlayers with excellent electrolyte affinity and well-distributed Li+ transport pathways can promote uniform Li+ flux distribution and large-size Li deposition. On the other hand, the rigid SnO2 contributes to reducing Li nucleation overpotential and stabilizing SEI layer assisted by its spontaneous reaction with Li. As a result, the smooth and dense Li deposition is achieved by such soft-rigid nanofiber interlayers, thereby extending the cycling life and improving the safety application of Li metal batteries. This work offers a new route for efficient protection of Li metal anodes and brings a new inspiration for developing high-energy-density Li metal batteries.

In Situ Forming Gel Polymer Electrolyte for High Energy-Density Lithium Metal Batteries.

In situ forming gel polymer electrolyte (GPE) is one of the most feasible ways to improve the safety and cycle performances of lithium metal batteries with high energy density. However, most of the in situ formed GPEs are not compatible with high-voltage cathode materials. Here, this work provides a novel strategy to in situ form GPE based on the mechanism of Ritter reaction. The Ritter reaction in liquid electrolyte has the advantage of appropriate reaction temperature and no additional additives. The polymer chains are cross-linked by amide groups with the formation of GPE with superior electrochemical properties. The GPE has high ionic conductivity (1.84 mS cm-1 ), wide electrochemical stability window (>5.25 V) and high lithium ion transference number (≈0.78), compatible with high-voltage cathode materials. The Li|LiNi0.6 Co0.2 Mn0.2 O2 batteries with in situ formed GPE show excellent long-term cycle stability (93.4%, 300 cycles). The density functional theory calculation and X-ray photoelectron spectroscopy results verify that the amide and nitrile groups are beneficial for stabilizing cathode structure and promoting uniform Li deposition on Li anode. Furthermore, the in situ formed GPE exhibits excellent electrochemical performance in Graphite|LiMn2 O4 and Graphite|LiNi0.5 Co0.2 Mn0.3 O2 pouch batteries. This approach is adaptable to current battery technologies, which will be sure to promote the development of high energy-density lithium-ion batteries.

Construction of 3D lithium metal anode using bi-functional composite separator: a new approach for lithium battery.

With the increasing use of Li batteries for storage, their safety issues and energy densities are attracting considerable attention. The Li metal battery (LMB) with limited capacity in the Li metal anode is one of ideal high energy-density systems due to eliminating the use of traditional anode, elevating the energy density of battery and reducing production costs. However, the side reactions between the electrolyte and metallic Li and the irreversible loss of lithium resources caused by the generation of "dead Li" will directly lead to the loss of battery capacity during the cycling process. Therefore, the cycle life of the LMB with limited capacity in the Li metal anode faces significant challenges. Herein, a bi-functional manganese oxide (MnO)/polypropylene/Li1+xAlxTi2-x(PO4)3 (LATP) composite separator is designed to construct a stable three dimensional (3D) Li metal in the surface of Cu foil for LMB. The MnO can dissolve in electrolytes with low concentration, which can be reduced to produce Mn and Li2O, functioning as nucleating seeds to induce sheet-like Li deposition. The sustainably released MnO also involves in the formation of solid electrolyte interphase (SEI) layer, which can be repaired promptly once damaged by the volume expansion of Li. The LATP coating layer is in situ transferred onto the sheet-like Li, acting as an artificial SEI layer for further protection. The constructed 3D Li metal anode with limited capacity shows improved cycle stability in LiFePO4 cell, which shows a capacity retention of 94.5% after 150 cycles. Our strategy, constructing stable 3D Li metal anode with bi-functional composite separator, will bring a new inspiration for developing high energy density LMB.

A Reinforcement Learning Handover Parameter Adaptation Method Based on LSTM-Aided Digital Twin for UDN.

Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to wireless signal fading conditions. This approach seeks good backward compatibility. In order to enhance the efficiency and performance of the DQN method, Long Short Term Memory (LSTM) is used to build a digital twin and assist the DQN algorithm to achieve a more efficient search. Simulation experiments prove that the enhanced method has a faster convergence speed than the ordinary DQN method, and at the same time, achieves an average effective handover ratio increase of 2.7%. Moreover, in different wireless signal fading intervals, the method proposed in this paper has achieved better performance.

Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring.

In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction.