Unraveling the Fundamental Mechanism of Interface Conductive Network Influence on the Fast‑Charging Performance of SiO‑Based Anode for Lithium‑Ion Batteries

Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effectively addresses the aforementioned problems;however,the impact of its quality on lithium-ion transfer and structure durability is yet to be explored.Herein,the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time.2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier.Furthermore,atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network,which is critical to the linear reduction of electrode residual stress.This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.

Flexible Polydimethylsiloxane Composite with Multi-Scale Conductive Network for Ultra-Strong Electromagnetic Interference Protection

Highly conductive polymer composites(CPCs) with excellent mechanical flexibility are ideal materials for designing excellent electromagnetic interference(EMI) shielding materials,which can be used for the electromagnetic interference protection of flexible electronic devices.It is extremely urgent to fabricate ultra-strong EMI shielding CPCs with efficient conductive networks.In this paper,a novel silver-plated polylactide short fiber(Ag@PL ASF,AAF) was fabricated and was integrated with carbon nanotubes(CNT) to construct a multi-scale conductive network in polydimethylsiloxane(PDMS) matrix.The multi-scale conductive network endowed the flexible PDMS/AAF/CNT composite with excellent electrical conductivity of 440 S m-1and ultra-strong EMI shielding effectiveness(EMI SE) of up to 113 dB,containing only 5.0 vol% of AAF and 3.0 vol% of CNT(11.1wt% conductive filler content).Due to its excellent flexibility,the composite still showed 94% and 90% retention rates of EMI SE even after subjected to a simulated aging strategy(60℃ for 7 days) and 10,000 bending-releasing cycles.This strategy provides an important guidance for designing excellent EMI shielding materials to protect the workspace,environment and sensitive circuits against radiation for flexible electronic devices.

Black Phosphorus/Nanocarbons Constructing a Dual-Carbon Conductive Network for High-Performance Sodium-Ion Batteries

Black phosphorus has been recognized as a prospective candidate anode material for sodium-ion batteries(SIBs)due to its ultrahigh theoretical capacity of 2596 mA·h/g and high electric conductivity of≈300 S/m.However,its large volume expansion and contraction during sodiation/desodiation lead to poor cycling stability.In this work,a BP/graphite nanoparticle/nitrogen-doped multiwalled carbon nanotubes(BP/G/CNTs)composite with a dual-carbon conductive network is successfully fabricated as a promising anode material for SIBs through a simple two-step mechanical milling process.The unique structure can mitigate the eff ect of volume changes and provide additional electron conduction pathways during cycles.Furthermore,the formation of P–O–C bonds helps maintain the intimate connection between phosphorus and carbon,thereby improving the cycling and rate performance.As a result,the BP/G/CNTs composite delivers a high initial Coulombic efficiency(89.6%)and a high specific capacity for SIBs(1791.3 mA·h/g after 100 cycles at 519.2 mA/g and 1665.2 mA·h/g after 100 cycles at 1298 mA/g).Based on these results,the integrated strategy of one-and two-dimensional carbon materials can guide other anode materials for SIBs.

Mapping Soil Electrical Conductivity Using Ordinary Kriging Combined with Back-propagation Network

Accurate mapping of soil salinity and recognition of its influencing factors are essential for sustainable crop production and soil health. Although the influencing factors have been used to improve the mapping accuracy of soil salinity, few studies have considered both aspects of spatial variation caused by the influencing factors and spatial autocorrelations for mapping. The objective of this study was to demonstrate that the ordinary kriging combined with back-propagation network(OK_BP), considering the two aspects of spatial variation, which can benefit the improvement of the mapping accuracy of soil salinity. To test the effectiveness of this approach, 70 sites were sampled at two depths(0–30 and 30–50 cm) in Ningxia Hui Autonomous Region, China. Ordinary kriging(OK), back-propagation network(BP) and regression kriging(RK) were used in comparison analysis; the root mean square error(RMSE), relative improvement(RI) and the decrease in estimation imprecision(DIP) were used to judge the mapping quality. Results showed that OK_BP avoided the both underestimation and overestimation of the higher and lower values of interpolation surfaces. OK_BP revealed more details of the spatial variation responding to influencing factors, and provided more flexibility for incorporating various correlated factors in the mapping. Moreover, OK_BP obtained better results with respect to the reference methods(i.e., OK, BP, and RK) in terms of the lowest RMSE, the highest RI and DIP. Thus, it is concluded that OK_BP is an effective method for mapping soil salinity with a high accuracy.

Correlating thermal conductivity of pure hydrocarbons and aromatics via perceptron artificial neural network (PANN) method

Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.

Fuzzy neural network analysis on gray cast iron with high tensile strength and thermal conductivity

To develop a high performance gray cast iron with high tensile strength and thermal conductivity, multivariable analysis of microstructural effects on properties of gray cast iron was performed. The concerned parameters consisted of graphite content, maximum graphite length, primary dendrite percentage and microhardness of the matrix. Under the superposed influence of various parameters, the relationships between thermal conductivity and structural characteristics become irregular, as well as the effects of graphite length on the strength. An adaptive neuro-fuzzy inference system was built to link the parameters and properties. A sensitivity test was then performed to rank the relative impact of parameters. It was found that the dominant parameter for tensile strength is graphite content, while the most relative parameter for thermal conductivity is maximum graphite length. The most effective method to simultaneously improve the tensile and thermal conductivity of gray cast iron is to reduce the carbon equivalent and increase the length of graphite flakes.

Temperature dependence of heat conduction coefficient in nanotube/nanowire networks

Studies on heat conduction are so far mainly focused on regular systems such as the one-dimensional（1D） and twodimensional（2D） lattices where atoms are regularly connected and temperatures of atoms are homogeneously distributed.However, realistic systems such as the nanotube/nanowire networks are not regular but heterogeneously structured, and their heat conduction remains largely unknown. We present a model of quasi-physical networks to study heat conduction in such physical networks and focus on how the network structure influences the heat conduction coefficient κ. In this model,we for the first time consider each link as a 1D chain of atoms instead of a spring in the previous studies. We find that κ is different from link to link in the network, in contrast to the same constant in a regular 1D or 2D lattice. Moreover, for each specific link, we present a formula to show how κ depends on both its link length and the temperatures on its two ends.These findings show that the heat conduction in physical networks is not a straightforward extension of 1D and 2D lattices but seriously influenced by the network structure.

IONIC CONDUCTIVITY IN CROSSLINKED POLY (METHYLSILOXANE-g-ETHYLENE OXIDE) NETWORK FILMS CONTAINING LITHIUM PERCHLORATE

Polymer electrolytes based on poly (methylsiloxane-g-ethylene oxide) and LiClO_4 have been prepared. The network films crosslinked by a crosslinking agent are found to exhibit a considerably high ionic conductivity of about 10^(-4) Scm^(-1) at room temperature and have good flexibility.

VISCOELASTICITY AND IONIC CONDUCTIVITY OF TWO-COMPONENT EPOXY NETWORK CONTAINING LITHIUM PERCHLORATE

Polymeric solid electrolyte system composed of triglycidyl ether of glycerol (TGEG), diglycidyl ether of polyethylene glycol (DGEPEG)and LiClO_4 salt were synthesized. In this' system the electrolyte has a pecularity that not merely can the LiClO_4 provide ionic carriers, but also catalyze the crosslinking reaction without adding an usual curing agent. The effect of salt content and degree of crosslinking on the viscoelasticity and ionic conductivity were studied. Both WLF and VTF equations were used to treat the experimental data in order to elucidate the mechanism of ionic conduction. It was found that the ionic conductivity of the system is carded out through the segmental motion mechanism. However, the data must be treated with care. For example, in evaluating WLF parameters, the contribution concerned with ionic carrier generation with temperature to the conductivity must be differentiated from that concerned with segmental motion. Besides, the temperature range suitable to WLF equation must also be considered. For VTF equation, it might be inapplicable ff the temperature is too low and close to the glass transition temperature of the specimen. Further study is needed in order to have a quantitative information on the limitation of these equations.

基于GA-BP神经网络模型预测水基炭黑-胶原蛋白纳米流体热导率和黏度

纳米流体由于其独特的强化传热性能,已广泛应用于各个领域。而热导率和黏度直接影响纳米流体在实际工程中的适用性,因此在考察纳米流体的强化传热特性前首先要分析研究其热导率和黏度。本研究利用炭黑和胶原蛋白,采用两步法制备了水基炭黑胶原蛋白纳米流体。实验分析了炭黑和胶原蛋白质量分数、温度对纳米流体热导率和黏度的影响。采用灰色关联方法对这些参数的权重进行了数学计算,基于实验数据建立了三输入两输出的BP神经网络预测模型,并利用遗传算法(GA)对BP模型进行优化。结果表明,遗传算法优化后的BP神经网络模型对预测输出具有更高的准确性和更好的稳定性,回归系数和最大偏差分别为0.99918和0.002。本研究不仅对于理解和控制水基炭黑-胶原蛋白纳米流体的热物理性能有重要意义,而且为工程设计和材料科学等方面的应用提供了新思路。

Analysis of thermal conductivity in tree-like branched networks

Asymmetric tree-like branched networks are explored by geometric algorithms. Based on the network, an analysis of the thermal conductivity is presented. The relationship between effective thermal conductivity and geometric structures is obtained by using the thermal-electrical analogy technique. In all studied cases, a clear behaviour is observed, where angle （δ,θ） among parent branching extended lines, branches and parameter of the geometric structures have stronger effects on the effective thermal conductivity. When the angle δ is fixed, the optical diameter ratio β＋ is dependent on angle θ. Moreover, γand m are not related to β＊. The longer the branch is, the smaller the effective thermal conductivity will be. It is also found that when the angle θ〈δ2, the higher the iteration m is, the lower the thermal conductivity will be and it tends to zero, otherwise, it is bigger than zero. When the diameter ratio β1 〈 0.707 and angle δ is bigger, the optimal k of the perfect ratio increases with the increase of the angle δ; when β1 〉 0.707, the optimal k decreases. In addition, the effective thermal conductivity is always less than that of single channel material. The present results also show that the effective thermal conductivity of the asymmetric tree-like branched networks does not obey Murray＇s law.

导热绝缘高密度聚乙烯复合材料的制备与性能

文中采用片层状绝缘导热六方氮化硼(h-BN)和高导热导电铜粉(Cu)复配填充来提升高密度聚乙烯(HDPE)的导热性能。控制导热填料总体积分数为30%,通过改变h-BN和Cu在HDPE中的复配比例,实现复合材料中导热网络通路的构建。扫描电镜结果表明,h-BN进入Cu粉无法占据的HDPE基体,起到桥梁作用。h-BN通过连接其周围的Cu粒子,促进了HDPE内部导热网络的形成与构筑。Hot Disk测试结果表明,随着h-BN/Cu比值增加,HDPE复合材料导热系数增大。当V(h-BN):V(Cu)≤1时,大片径h-BN_(30)/Cu体系的热导率高于小片径h-BN_(05)/Cu改性体系;与之相反,当V(h-BN):V(Cu)>1时,相对于大片径h-BN_(30),小片径h-BN_(05)对复合体系热导率的贡献更明显。通过调控h-BN与Cu的含量,实现了在抑制导电网络形成的前提下大幅改善复合材料的导热性能。其研究工作为导热绝缘复合材料的设计制备提供参考。

IONIC CONDUCTIVITY OF EPOXY NETWORK/POLYETHYLENE GLYCOL- LITHIUM PERCHLORATE COMPLEX IPN SYSTEM

In an attempt to prepare a polymeric solid electrolyte with both high ionic conductivity at ambient temperature and adequate mechanical strength, an ionic conducting IPN composed of bisphenol A epoxy resin/polyethylene glycol containing LiClO_4 was synthesized. The dependence of conductivity was investigated as a function of salt content, composition and temperature. It has been revealed that a maximum of conductivity appeared when EO/Li=25, where EO denotes the—(CH_2CH_2O)-unit in polyethylene glycol, and that the temperature dependence of conductivity followed VTF equation, suggesting that the motion of ionic carriers resulted from the segmental motion of the polymer. When glycerol epoxy resin was used instead of bisphenol A epoxy, the ambient temperature (25℃) conductivity could somewhat further be raised up to 3×10^(-5) S/cm.

ITO/AgNWs/ITO薄膜的制备及其性能研究

随着显示面板向超大尺寸、超高清、可触控的方向发展,单一的氧化铟锡(ITO)薄膜难以满足显示器件越来越高的光电性能要求,因此复合导电薄膜得以发展。本文制备了以二维银纳米线(AgNWs)导电网络嵌入ITO薄膜形成的ITO(222)/AgNWs/ITO(400)复合薄膜结构,系统研究了AgNWs添加量和上层ITO薄膜溅射温度对复合薄膜结构与光电性能的影响,AgNWs金属导电网络不仅提升了薄膜的电学性能,还保持了优良的光学性能。结果表明,在旋涂600μL的AgNWs分散液、上层ITO薄膜的溅射温度为175℃时,制备的复合ITO薄膜方阻为7.13Ω/□,在550 nm处透过率为91.52%,且品质因数为57.82×10^(-3)Ω^(-1),实现了超低电阻率和高可见光透过率复合ITO薄膜的制备。

基于银纳米线/聚苯胺纳米复合材料的电致变色薄膜及器件的制备与性能

以质量比分别为1:5,1:15,1:25的银纳米线(AgNWs)和苯胺为原料,采用化学氧化聚合法制备了银纳米线/聚苯胺(AgNWs/PANI)纳米复合材料,喷涂在氧化铟锡(ITO)导电玻璃上得到AgNWs/PANI电致变色薄膜,将喷涂AgNWs/PANI(质量比为1:15)和聚(3,4-乙烯二氧噻吩)(PEDOT)电致变色薄膜的ITO基电极组装得到分层式电致变色器件,研究了AgNWs/PANI电致变色薄膜和电致变色器件的微观形貌和电致变色性能。结果表明:AgNWs/PANI电致变色薄膜中PANI均匀包覆在AgNWs上形成线性核壳结构,当AgNWs和苯胺质量比为1:5时薄膜中有很多AgNWs暴露在外,当质量比为1:25时AgNWs暴露缺陷较少但形成的导电通路连续性差,当质量比为1:15时AgNWs被PANI完美包覆且导电通路连续均匀;与纯PANI电致变色薄膜相比,AgNWs/PANI电致变色薄膜的峰值电流密度、离子扩散速率和光学对比度增大,电荷传递阻抗减小,变色效果明显,电致变色性能提升,AgNWs和苯胺质量比为1:15时电致变色性能最佳;电致变色器件的最大光学对比度为65.2%,着色和褪色过程响应时间分别仅为6.5,4.9 s,经历1 000次恒电位跃迁后光学对比度保留率高达97.7%,着色效率高达124.6 cm^(2)·C^(-1)。

聚苯乙烯/碳纳米管/纳米黏土导电复合材料的制备及性能

为了考察纳米黏土(NC)的引入对碳纳米管(CNTs)在聚苯乙烯(PS)树脂基体中分散的影响,使用NC作为非导电填料,通过熔融共混工艺制备了PS/CNTs/NC导电复合材料,探讨了NC与CNTs的含量及密炼时间对PS/CNTs/NC复合材料导电性能和力学性能的影响规律。扫描电子显微镜分析表明NC的加入减少了CNTs在PS基体中的团聚现象,并促使CNTs在较低添加量下形成了导电网状结构。复合材料的电阻率表征结果表明,在相同的CNTs含量下随着复合材料中NC含量的增加,体系电阻率呈现出逐渐减小趋势,并且在低CNTs添加量下,NC的引入促使体系电阻率降低的现象更明显,随着密炼时间的增加,体系电阻率呈现进一步减小的现象;力学性能表征结果表明,PS/CNTs/NC复合材料的拉伸性能总体上随着CNTs和NC含量的增加而增大,并且随着密炼时间的增加,复合材料拉伸强度最高可增加50.2%。NC与CNTs的质量比为1∶1时,可获得具有良好导电性能的复合材料。

多尺度卷积神经网络的电阻层析成像算法

针对电阻层析成像(ERT)经典算法(LBP、Landweber等)在复杂流型情况下成像精度低的问题,提出一种基于多尺度卷积神经网络(Multi-scale Convolutional Neural Network, MS-CNN)的电阻层析成像图像重建算法。根据气液两相流流型特点,构建有限元模型得到20 000组包含“边界电压向量-电导率分布”的数据集。在典型卷积神经网络Resnet50和Vgg16的基础上,构建针对ERT图像重建问题的MS-CNN。仿真实验结果表明,与Landweber迭代算法和单尺度卷积神经网络算法相比,MS-CNN算法的ICC分别提升了0.715和0.023,RIE分别降低了0.812和0.057。抗噪性测试与静态测试结果表明,MS-CNN算法具有良好的图像重建结果和鲁棒性。

基于圆卷积神经网络的粘连导电粒子检测

为了提高粘连导电粒子检测的精度和稳定性,提高评价指标的客观性和与实际生产需求的适配度,提出了基于圆卷积神经网络的粘连粒子检测。首先提出了更适合粒子检测的圆卷积,修改可变形卷积的采样策略,限制采样点偏移量x,y坐标的自由度,增加尺寸控制参数作为补偿。然后,基于U-MultiNet网络架构将圆卷积替代原有卷积形式,并增加注意力机制,通过标签图计算自注意力,以此作为权重修改损失函数和标签图。最后,提出可重复性和可再现性指标综合评价算法的精度和稳定性。实验结果表明,本文方法的可重复性和可再现性分别为0.8092和0.7051,相比现有主流方法提高了4.52%和1.74%;精确度和召回率分别为0.7128和0.6974,准确度为0.8341,比现有主流方法高1.68%。相比于现有主流方法,该方法对于粘连干扰的粒子检测效果有明显提升,可以满足工业上对粒子检测精度、稳定性和实时性的要求。

Highly Thermally Conductive Polydimethylsiloxane Composites with Controllable 3D GO@f-CNTs Networks via Self-sacrificing Template Method

Constructing controllable thermal conduction networks is the key to efficiently improve thermal conductivities of polymer composites.In this work,graphite oxide(GO)and functionalized carbon nanotubes(f-CNTs)are combined to prepare“Line-Plane”-like hetero-structured thermally conductive GO@f-CNTs fillers,which are then performed to construct controllable 3D GO@f-CNTs thermal conduction networks via selfsacrificing template method based on oxalic acid.Subsequently,thermally conductive GO@f-CNTs/polydimethylsiloxane(PDMS)composites are fabricated via casting method.When the size of oxalic acid is 0.24 mm and the volume fraction of GO@f-CNTs is 60 vol%,GO@f-CNTs/PDMS composites present the optimal thermal conductivity coefficient(λ,4.00 W·m^(-1)·K^(-1)),about 20 times that of theλof neat PDMS(0.20 W·m^(-1)·K^(-1)),also much higher than theλ(2.44 W·m^(-1)·K^(-1))of GO/f-CNTs/PDMS composites with the same amount of randomly dispersed fillers.Meanwhile,the obtained GO@f-CNTs/PDMS composites have excellent thermal stability,whoseλdeviation is only about 3%after 500 thermal cycles(20-200℃).

基于Wenner和Schlumberger双组态融合的土壤电导率测量装置

土壤电导率的准确、实时和原位获取可为农业生产精准管理提供有效的数据支撑,为提高传统电流-电压四端法测量精度,该研究基于电流-电压四端法3种测量组态,开展土壤电导率主要影响因素(土壤含水率、电极入土深度、土壤坚实度和土壤处理方式)对电流-电压四端法的3种测量组态测量精度影响的试验。结果表明,Wenner和Schlumberger两种测量组态可较好地适用于不同土壤环境条件。进一步以Wenner和Schlumberger两种测量组态所测土壤电导率值为输入量,基于BP神经网络构建了双组态融合的土壤电导率回归模型,并在此基础上设计了一种土壤电导率测量装置,该装置主要包括JESTON nano、STM32单片机数据采集模块、传感器、激励源及差分放大模块等组件。工作稳定性试验结果显示,该装置在不同土壤电导率梯度条件下测量数据的标准偏差均小于0.43μS/mm;田间性能对比试验结果显示,该装置测量数据的均方根误差值为0.18μS/mm,测量精度优于传统单独测量组态和市面常用土壤电导率测量仪,以上结果表明所研制的土壤电导率测量装置具有较好的工作稳定性和测量精度。该研究可为田间土壤信息的实时原位采集提供一种高精度的检测工具和技术手段。