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.

Porous Indium Nanocrystals on Conductive Carbon Nanotube Networks for High-Performance CO_(2)-to-Formate Electrocatalytic Conversion

Ever-increasing emissions of anthropogenic carbon dioxide(CO_(2))cause global environmental and climate challenges.Inspired by biological photosynthesis,developing effective strategies NeuNlto up-cycle CO_(2)into high-value organics is crucial.Electrochemical CO_(2)reduction reaction(CO_(2)RR)is highly promising to convert CO_(2)into economically viable carbon-based chemicals or fuels under mild process conditions.Herein,mesoporous indium supported on multi-walled carbon nanotubes(mp-In@MWCNTs)is synthesized via a facile wet chemical method.The mp-In@MWCNTs electrocatalysts exhibit high CO_(2)RR performance in reducing CO_(2)into formate.An outstanding activity(current density-78.5 mA cm^(-2)),high conversion efficiency(Faradaic efficiency of formate over 90%),and persistent stability(∼30 h)for selective CO_(2)-to-formate conversion are observed.The outstanding CO_(2)RR process performance is attributed to the unique structures with mesoporous surfaces and a conductive network,which promote the adsorption and desorption of reactants and intermediates while improving electron transfer.These findings provide guiding principles for synthesizing conductive metal-based electrocatalysts for high-performance CO_(2)conversion.

Constructing Anisotropic Conductive Networks inside Hollow Elastic Fiber with High Sensitivity and Wide‑Range Linearity by Cryo‑spun Drying Strategy

Stretchable conductive fibers composed of conductive materials and elastic substrates have advantages such as braiding abil-ity,electrical conductivity,and high resilience,making them ideal materials for fibrous wearable strain sensors.However,the weak interface between the conductive materials and elastic substrates restricts fibers flexibility under strain,leading to challenges in achieving both linearity and sensitivity of the as-prepared fibrous strain sensor.Herein,cryo-spun drying strategy is proposed to fabricate the thermoplastic polyurethane(TPU)fiber with anisotropic conductive networks(ACN@TPU fiber).Benefiting from the excellent mechanical properties of TPU,and the excellent interface among TPU,silver nanoparticles(AgNPs)and polyvinyl alcohol(PVA),the prepared ACN@TPU fiber exhibits an outstanding mechanical performance.The anisotropic conductive networks enable the ACN@TPU fiber to achieve high sensitivity(gauge factor,GF=4.68)and excellent linearity within a wide working range(100%strain).Furthermore,mathematical model based on AgNPs is established and the resistance calculation equation is derived,with a highly matched fitting and experimental results(R2=0.998).As a conceptual demonstration,the ACN@TPU fiber sensor is worn on a mannequin to accurately and instantly detect movements.Therefore,the successful construction of ACN@TPU fiber with anisotropic conductive networks through the cryo-spun drying strategy provides a feasible approach for the design and preparation of fibrous strain sensing materials with high linearity and high sensitivity.

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.

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.

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.

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.

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.

Conductive Polymer Composites Fabricated by Disposable Face Masks and Multi-Walled Carbon Nanotubes: Crystalline Structure and Enhancement Effect

Influenced by recent COVID-19,wearing face masks to block the spread of the epidemic has become the simplest and most effective way.However,after the people wear masks,thousands of tons of medical waste by used dis-posable masks will be generated every day in the world,causing great pressure on the environment.Herein,con-ductive polymer composites are fabricated by simple melt blending of mask fragments(mask polypropylene,short for mPP)and multi-walled carbon nanotubes(MWNTs).MWNTs were used as modifiers for composites because of their high strength and high conductivity.The crystalline structure,mechanical,electrical and thermal enhancement effect of the composites were investigated.MWNTs with high thermal stability acted the role of promoting the crystallisation of mPP by expediting the crystalline nucleation,leading to the improvement of amount for crystalline nucleus.MWNTs fibers interpenetrate with each other in mPP matrix to form conducting network.With 2.0 wt% MWNTs loading,the tensile strength and electrical conductivity of the composites were increased by 809% and 7 orders of magnitude.MWNTs fibers interpenetrate with each other in mPP matrix to form conducting network.Thus,more conducting paths were constructed to transport carriers.The findings may open a way for high value utilization of the disposable masks.

Biomimic Vein-Like Transparent Conducting Electrodes with Low Sheet Resistance and Metal Consumption

In this contribution,inspired by the excellent resource management and material transport function of leaf veins,the electrical transport function of metallized leaf veins is mimicked from the material transport function of the vein networks.By electroless copper plating on real leaf vein networks with copper thickness of only several hundred nanometre up to several micrometre,certain leaf veins can be converted to transparent conductive electrodes with an ultralow sheet resistance 100 times lower than that of state-of-the-art indium tin oxide thin films,combined with a broadband optical transmission of above 80%in the UV–VIS–IR range.Additionally,the resource efficiency of the vein-like electrode is characterized by the small amount of material needed to build up the networks and the low copper consumption during metallization.In particular,the high current density transport capability of the electrode of>6000 A cm^−2 was demonstrated.These superior properties of the vein-like structures inspire the design of high-performance transparent conductive electrodes without using critical materials and may significantly reduce the Ag consumption down to<10%of the current level for mass production of solar cells and will contribute greatly to the electrode for high power density concentrator solar cells,high power density Li-ion batteries,and supercapacitors.

A Molecular Description of Superconductivity of Sulfur Hydride and Related Systems under High-Pressure Conditions

It has been shown that the recently discovered sulfur trihydride (H3S) can be considered as a superconductor with a transition temperature Tc of 203 Kelvin (K) at 155 GigaPascals (GPa). This is the highest Tc value reported for any superconductor. The established superconductivity occurs via the formation of a molecular system with sulfur atoms arranged on a body-centered cubic lattice. It has been generally accepted that the high Tc value is the result of an efficient electron-phonon interaction. The responsible substance formed by H2S under high pressure, may be considered as a compound with H3S stoichiometry creating an impressive network with hydrogens. We will focus on the hydrogen bonding between sulfur and hydrogens demonstrating a symmetrical arrangement. The geometry of the individual radical compound in relation to corresponding systems will be discussed. Ab initio calculations based on a linear three-center two-, three- and four-electron type of bonding clearly visualized in combination with the dynamics of the Van’t Hoff concept, as described by us in various papers, give a good description of this exclusive network. We also discuss the superconductivity of related phosphorus hydrides and focus on the stability and geometrical differences with respect to the H3S system. These differences are significant, demonstrating the diversity in various structures in showing superconductivity.

PREPARATION OF STAR NETWORK PEG-BASED GEL POLYMER ELECTROLYTES FOR ELECTROCHROMIC DEVICES

An amorphous,colorless,and highly transparent star network polymer with a pentaerythritol core linking four PEG-block polymeric arms was synthesized from the poly(ethylene glycol)(PEG),pentaerythritol,and dichloromethane by Williamson reaction.FTIR and ~1H-NMR measurement demonstrated that the polymer repeating units were C[CH_2-OCH_2O-(CH_2CH_2O)_m-CH_2O-(CH_2CH_2O)_n-CH_2O]_4.The polymer host held well mechanical properties for pentaerythritol cross-linking.The gel polymer electrolytes based on Lithium pe...

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。