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.

Design,progress and challenges of 3D carbon-based thermally conductive networks

The advent of the 5G era has stimulated the rapid development of high power electronics with dense integration.Three-dimensional(3D)thermally conductive networks,possessing high thermal and electrical conductivities and many different structures,are regarded as key materials to improve the performance of electronic devices.We provide a critical overview of carbonbased 3D thermally conductive networks,emphasizing their preparation-structure-property relationships and their applications in different scenarios.A detailed discussion of the microscopic principles of thermal conductivity is provided,which is crucial for increasing it.This is followed by an in-depth account of the construction of 3D networks using different carbon materials,such as graphene,carbon foam,and carbon nanotubes.Techniques for the assembly of two-dimensional graphene into 3D networks and their effects on thermal conductivity are emphasized.Finally,the existing challenges and future prospects for 3D carbon-based thermally conductive networks are discussed.

Estimation of the anisotropy of hydraulic conductivity through 3D fracture networks using the directional geological entropy

With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directional entropic scale is used to measure the anisotropy of spatial order in different directions.Compared with the traditional connectivity indexes based on the statistics of fracture geometry,the directional entropic scale is capable to quantify the anisotropy of connectivity and hydraulic conductivity in heterogeneous 3D fracture networks.According to the numerical analysis of directional entrogram and fluid flow in a number of the 3D fracture networks,the hydraulic conductivities and entropic scales in different directions both increase with spatial order(i.e.,trace length decreasing and spacing increasing)and are independent of the dip angle.As a result,the nonlinear correlation between the hydraulic conductivities and entropic scales from different directions can be unified as quadratic polynomial function,which can shed light on the anisotropic effect of spatial order and global entropy on the heterogeneous hydraulic behaviors.

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.

Efficient Preconstruction of Three‑Dimensional Graphene Networks for Thermally Conductive Polymer Composites

Electronic devices generate heat during operation and require efficient thermal management to extend the lifetime and prevent performance degradation.Featured by its exceptional thermal conductivity,graphene is an ideal functional filler for fabricating thermally conductive polymer composites to provide efficient thermal management.Extensive studies have been focusing on constructing graphene networks in polymer composites to achieve high thermal conductivities.Compared with conventional composite fabrications by directly mixing graphene with polymers,preconstruction of three-dimensional graphene networks followed by backfilling polymers represents a promising way to produce composites with higher performances,enabling high manufacturing flexibility and controllability.In this review,we first summarize the factors that affect thermal conductivity of graphene composites and strategies for fabricating highly thermally conductive graphene/polymer composites.Subsequently,we give the reasoning behind using preconstructed three-dimensional graphene networks for fabricating thermally conductive polymer composites and highlight their potential applications.Finally,our insight into the existing bottlenecks and opportunities is provided for developing preconstructed porous architectures of graphene and their thermally conductive composites.

Modeling of thermal conductivity for disordered carbon nanotube networks

Several theoretical models have been developed so far to predict the thermal conductivities of carbon nanotube(CNT)networks.However,these models overestimated the thermal conductivity significantly.In this paper,we claimed that a CNT network can be considered as a contact thermal resistance network.In the contact thermal resistance network,the temperature of an individual CNT is nonuniform and the intrinsic thermal resistance of CNTs can be ignored.Compared with the previous models,the model we proposed agrees well with the experimental results of single-walled CNT networks.

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.

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℃).

Transparent micropatterned conductive films based on highlyordered nanowire network

Transparent conductive films that are based on nanowire networks are essential to construct flexible,wearable,and even stretchable electronics.However,large-scale precise micropatterning,especially with regard to the controllability of the organizing orientation of nanowires,is a critical challenge.Herein,we proposed a liquid film rupture self-assembly approach for manufacturing transparent conductive films with microstructure arrays based on a highly ordered nanowire network.The large-scale microstructure conductive films were fabricated through air-liquid interface self-assembly and liquid film rupture self-assembly.Six typical micropattern morphologies,including square,hexagon,circle,serpentine,etc.,were prepared to reveal the universal applicability of the proposed approach.The homogeneity and controllability of this approach were verified for multiple assemblies.With the assembly cycles increasing,the optical transmittance decreases slightly.In addition,theoretical model analysis is carried out,and the analytical formula of the speed of the film moving with the surface tension and the density of the liquid film is presented.Finally,the feasibility of this approach for piezoresistive strain sensors is verified.This fabrication approach demonstrated a cost-effective and efficient method for precisely arranging nanowires,which is useful in transparent and wearable applications.

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.

Boosting the zinc storage performance of vanadium dioxide by integrated morphology engineering and carbon nanotube conductive networks

Vanadium dioxide(VO_(2)) with the advantages of high theoretical capacity and tunnel structure has attracted considerable promising candidates for aqueous zinc-ion batteries.Nevertheless,the intrinsic low electronic conductivity of VO_(2) results in an unsatisfactory electrochemical performance.Herein,a flower-like VO_(2)/carbon nanotubes(CNTs)composite was obtained by a facile hydrothermal method.The unique flower-like morphology shortens the ion transport length and facilitates electrolyte infiltration.Meanwhile,the CNT conductive networks is in favor of fast electron transfer.A highly reversible zinc storage mechanism was revealed by ex-situ X-ray diffraction and X-ray photoelectron spectroscopy.As a result,the VO_(2)/CNTs cathode exhibits a high reversible capacity(410 mAh·g^(−1)),superior rate performance(305 mAh·g^(−1)at 5 A·g^(−1)),and excellent cycling stability(a reversible capacity of 221 mAh·g^(−1)was maintained even after 2000 cycles).This work provides a guide for the design of high-performance cathode materials for aqueous zinc metal batteries.

An unusual network of α-MnO_(2) nanowires with structure-induced hydrophilicity and conductivity for improved electrocatalysis

Nanowires with anisotropic morphologies have been applied in various scientific and technological areas.It is also widely employed to fabricate nanowires into high-dimensional superstructures(arrays,networks etc.)to overcome the shortcomings of low-dimensional nanowires.However,typical strategies for constructing these superstructures are restricted to complicated and harsh synthetic conditions,not to mention unique 3D structures with advanced properties beyond common superstructures.Herein,we report an unusual network ofα-MnO_(2)nanowires with structure-induced hydrophilicity and conductivity.In the network,the nanowires are interconnected from all directions by nodes,and the 3D network structure is formed from the endless connection of nodes in a node-by-node way.The unique network structure brings about high hydrophilicity and conductivity,both of which are positive factors for an efficient electrocatalyst.Accordingly,the α-MnO_(2) network was tested for electrocatalytic water oxidation and showed significantly enhanced activity compared with isolatedα-MnO_(2)nanowires and 3Dα-MnO_(2)microspheres.This study not only provides a synthetic route toward an advanced network structure but also a new idea for the design of materials for electrochemistry with both efficient mass diffusion and charge transfer.

Multiple conductive network for KTi_(2)(PO_(4))_(3)anode based on MXene as a binder for high-performance potassium storage

KTi_(2)(PO_(4))_(3)is a promising anode material for potassium storage,but suffers from low conductivity and difficult balance between high capacity and good structural stability.Herein,the Ti_(3)C_(2)T_(x)MXene is used as a multifunctional binder to fabricate the KTi_(2)(PO_(4))_(3)electrode by the traditional homogenizing-coating method.The MXene nanosheets,together with the conductive agent super P nanoparticles,construct a multiple conductive network for fast electron/ion transfer and high electrochemical kinet-ics.Moreover,the network ensures the structural stability of the KTi_(2)(PO_(4))_(3)electrode during the de-intercalation/intercalation of 4 K^(+)ions,which is beneficial for simultaneously achieving high capacity and good cycle performance.Therefore,the MXene-bonded KTi_(2)(PO_(4))_(3)electrode delivers a reversible capacity of 255.2 mAh/g at 50 mA/g,outstanding rate capability with 132.3 mAh/g at 2 A/g,and ex-cellent cycle performance with 151.6 mAh/g at 1 A/g after 2000 cycles.This work not only suggests a high-performance anode material for potassium-ion batteries,but also demonstrates that the MXene is a promising binder material for constructing conductive electrodes in rechargeable batteries.

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.

An optimization model for conductivity of hydraulic fracture networks in the Longmaxi shale,Sichuan basin,Southwest China

Shale gas is an important unconventional resource.The economic recovery of shale gas is only possible when a fracture network with sufficient conductivity is created by hydraulic fracturing,that,if effectively propped,connects fracturing fractures and natural fractures.Focusing on the Longmaxi shale in the Sichuan Basin,Southwest China,we built an optimization model for conductivity of multi-grade fractures based on equivalent seepage theory.We then experimentally analyzed the conductivity of self-propped and sand-propped fractures,and optimized the propping patterns of multi-grade hydraulic fractures in shale gas reservoirs.We concluded that the propping effectiveness of fracture networks could be improved by using low concentrations of small-sized sands and by focusing on creating a large number of self-propped fractures.By applying this understanding to the optimization of fracturing designs for the Longmaxi shale,we successfully created networks of well-propped fractures.

Effects of aging parameters on hardness and electrical conductivity of Cu-Cr-Sn-Zn alloy by artificial neural network

In order to predict and control the properties of Cu-Cr-Sn-Zn alloy,a model of aging processes via an artificial neural network(ANN) method to map the non-linear relationship between parameters of aging process and the hardness and electrical conductivity properties of the Cu-Cr-Sn-Zn alloy was set up.The results show that the ANN model is a very useful and accurate tool for the property analysis and prediction of aging Cu-Cr-Sn-Zn alloy.Aged at 470-510 ℃ for 4-1 h,the optimal combinations of hardness 110-117(HV) and electrical conductivity 40.6-37.7 S/m are available respectively.

Ultrafast battery heat dissipation enabled by highly ordered and interconnected hexagonal boron nitride thermal conductive composites

Heat dissipation involved safety issues are crucial for industrial applications of the high-energy density battery and fast charging technology.While traditional air or liquid cooling methods suffering from space limitation and possible leakage of electricity during charge process,emerging phase change materials as solid cooling media are of growing interest.Among them,paraffin wax(PW)with large latent heat capacity and low cost is desirable for heat dissipation and thermal management which mainly hindered by their relatively low thermal conductivity and susceptibility to leakage.Here,highly ordered and interconnected hexagonal boron nitride(h-BN)networks were established via ice template method and introduced into PW to enhance the thermal conductivity.The composite with 20 wt%loading amount of h-BN can guarantee a highly ordered network and exhibited high thermal conductivity(1.86 W m^(-1) K^(-1))which was 4 times larger compared with that of random dispersed h-BN involved PW and nearly 8 times larger compared with that of bare PW.The optimal thermal conductive composites demonstrated ultrafast heat dissipation as well as leakage resistance for lithium-ion batteries(LIBs),heat generated by LIBs can be effectively transferred under the working state and the surface temperature kept 6.9℃ lower at most under 2–5℃ continuous charge-discharge process compared with that of bare one which illustrated great potential for industrial thermal management.

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.

Resistivity-temperature Characteristics of Conductive Asphalt Concrete

The changes of resistivity of conductive asphalt concrete at different temperatures were studied,and positive temperature coefficient（PTC）modelwas established to estimate the influence of temperature on the resistivity quantitatively,which eliminated the interference with conductivity evaluation brought by temperature variation.Finally,the analysis of temperature cycling test results proves that the changes of percolation network structure caused by temperature variation prompt the emergence of PTC of conductive asphalt concrete.

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.