Effective Electrical Conductivity Research Articles

Assessment of effective electrical conductivity in spherocylindrical carbon nanotube composites incorporating intrinsic properties of CNTs and interphase layer

This study introduces an analytical model using mean-field theory to estimate the effective electrical conductivity in composites of spherocylindrical carbon nanotubes (CNTs). The model adjusts the interphase layer's thickness and conductivity based on CNT concentration, affecting the electrical tunneling effect, and incorporates the properties of both CNTs and the interphase layer. Validated through detailed comparisons with experimental data, the model accurately predicts the electrical conductivities of polymer-CNT composites (PCNTs). Mean Squared Error analysis highlights its enhanced precision and superiority over traditional cylindrical CNT models. The model also explores how CNT dimensions, intrinsic conductivity, tunneling distance, potential barrier height, and variations in interphase thickness and conductivity influence electrical conductivity and percolation thresholds. This analytical model improves accuracy in predicting PCNT electrical conductivity and provides critical insights for designing and optimizing conductive polymer nanocomposites.

Inhibiting the Jahn-Teller Effect of Manganese Hexacyanoferrate via Ni and Cu Codoping for Advanced Sodium-Ion Batteries.

Manganese (Mn)-based Prussian blue analogs (PBAs) are of great interest as a prospective cathode material for sodium-ion batteries (SIBs) due to their high redox potential, easy synthesis, and low cost. However, the Jahn-Teller effect and low electrical conductivity of Mn-based PBA cause poor structure stability and unsatisfactory performance during the cycling. Herein, a novel nickel- and copper-codoped K2Mn[Fe(CN)6] cathode is developed via a simple coprecipitation strategy. The doping elements improve the electrical conductivity of Mn-based PBA by reducing the bandgap, as well as suppress the Jahn-Teller effect by stabilizing the framework, as verified by the density functional theory calculations. Simultaneously, the substitution of sodium with potassium in the lattice is beneficial for filling vacancies in the PBA framework, leading to higher average operating voltages and superior structural stability. As a result, the as-prepared Mn-based cathode exhibits excellent reversible capacity (116.0 mAh g-1 at 0.01 A g-1) and superior cycling stability (81.8% capacity retention over 500 cycles at 0.1 A g-1). This work provides a profitable doping strategy to inhibit the Jahn-Teller structural deformation for designing stable cathode material of SIBs.

Comprehensive correlation analysis of electromechanical behavior in high-stretchable carbon nanotube/polymer composites

In this study, a comprehensive correlation analysis of highly stretchable carbon nanotube (CNT)/polymer composites was conducted to predict the change in electrical conductivities in response to uniaxial deformation. To this end, the representative volume elements (RVEs) were generated by randomly distributing CNTs in a polymer matrix using a Monte Carlo simulation algorithm. The effective electrical conductivity was then calculated through a network model. Under uniaxial tensile strain, where the length of CNTs was maintained constant and their configuration kept straight, CNT translation and rotation were considered along with the effects of tensile strain and shrinkage, incorporating Poisson’s ratio. The RVE configuration was updated to account for changes in the network under these conditions. To achieve a strong correlation between the simulation and test results from the previously published works, numerous trade-off studies have been conducted on the RVE size, geometric periodicity, the length of CNT fibers, the mixing ratio of CNT fibers of CNT/polymer composites, and tensile strain. From the results it can be seen that excellent correlations can be only achieved with careful control of the aforementioned parameters.

Discrete element model for effective electrical conductivity of spark plasma sintered porous materials

AbstractThis paper aims to analyse electrical conduction in partially sintered porous materials using an original resistor network model within discrete element framework. The model is based on sintering geometry, where two particles are connected via neck. Particle-to-particle conductance depends on neck size in sintered materials. Therefore, accurate evaluation of neck size is essential to determine conductance. The neck size was determined using volume preservation criterion. Additionally, grain boundary correction factor was introduced to compensate for any non-physical overlaps between particles, particularly at higher densification. Furthermore, grain boundary resistance was added to account for the porosity within necks. For numerical analysis, the DEM sample was generated using real particle size distribution, ensuring a heterogeneous and realistic microstructure characterized by a maximum-to-minimum particle diameter ratio of 15. The DEM sample was subjected to hot press simulation to obtain geometries with different porosity levels. These representative geometries were used to simulate current flow and determine effective electrical conductivity as a function of porosity. The discrete element model (DEM) was validated using experimentally measured electrical conductivities of porous NiAl samples manufactured using spark plasma sintering (SPS). The numerical results were in close agreement with the experimental results, hence proving the accuracy of the model. The model can be used for microscopic analysis and can also be coupled with sintering models to evaluate effective properties during the sintering process.

4D Electrical Resistivity Imaging of Stress Perturbations Induced During High‐Pressure Shear Stimulation Tests

AbstractFluid flow through fractured media is typically governed by the distribution of fracture apertures, which are in turn governed by stress. Consequently, understanding subsurface stress is critical for understanding and predicting subsurface fluid flow. Although laboratory‐scale studies have established a sensitive relationship between effective stress and bulk electrical conductivity in crystalline rock, that relationship has not been extensively leveraged to monitor stress evolution at the field scale using electrical or electromagnetic geophysical monitoring approaches. In this paper we demonstrate the use time‐lapse 3‐dimensional (4D) electrical resistivity tomography to image perturbations in the stress field generated by pressurized borehole packers deployed during shear‐stimulation attempts in a 1.25 km deep metamorphic crystalline rock formation.

Synthesis of nanosheet-like α-Ni(OH)2/g-C3N4 hybrid anode material with enhanced lithium storage performance

Nickel hydroxide (Ni(OH)2) has been recognized as a promising anode material for lithium-ion batteries (LIBs) because of its high theoretical capacity, cost-effectiveness, and simple synthesis method. However, the application of Ni(OH)2 is hampered by its volume effect and poor electrical conductivity. To address these challenges, herein, a α-Ni(OH)2/g-C3N4 composite is prepared by a hydrothermal approach. The composite possesses a hierarchical nanosheet-like morphology with fibrous nanostructures on the surface. Due to the unique hybrid nanostructure and the synergistic effect of α-Ni(OH)2 and g-C3N4 components, the α-Ni(OH)2/g-C3N4 composite exhibits significantly enhanced lithium storage performance in terms of electrochemical activity, cyclability, and kinetics than the bare α-Ni(OH)2 counterpart. Specifically, the α-Ni(OH)2/g-C3N4 achieves a remarkable capacity of 696 mA h g−1 after 400 cycles at 0.5 A g−1, far larger than the corresponding value (96 mA h g−1) of pure α-Ni(OH)2. CV and GITT analysis demonstrate the α-Ni(OH)2/g-C3N4 composite has an obvious pseudocapacitance behavior and a much smaller polarization effect than the bare α-Ni(OH)2 during cycling. This work could provide clues for the structural design and performance improvement of nickel hydroxide-based anode materials for LIBs.

Comparison of the Prediction of the Temperature Profile in an Ohmic Reactor on Different Modeling Scales

AbstractTo predict the temperature profile in an ohmic reactor, it is essential to know the thermal and electrical conductivity of the reactor geometry. Those properties can be considered on different modeling scales: On the detailed (resolved) scale, the reactor consists of a packed bed structure, where the properties of each substance are considered. On the effective (continuum) scale, the packed bed is considered as effective medium with composite properties. In this contribution, we consider both scales and compare the results. Based on the detailed description with a resolved microstructure, the bed's effective thermal and electrical conductivity are computed. These properties are compared with analytical formulas. The electrical conductivity is the basis for the electrical field and the current density. Finally, these properties are used to evaluate the ohmic heating of the packed bed exposed to flow and to compute the temperature distribution within the reactor.

Joint micromechanical model for determination of effective elastic and electromagnetic properties of porous materials

In this paper we propose an approach for calculating the effective physical properties of porous materials (for example, sedimentary rocks) which is based on the unified structure of the pore space. This approach is based on the Generalized Differential Effective Medium (GDEM) method. This method generalizes the classical differential scheme (DEM) for the case of many types of inclusions. The physical properties of a composite calculated using the GDEM depend on how the solution is constructed.A porous medium is represented by the elastic weakly conductive matrix with embedded inclusions of two types (spheroidal and cylindrical), saturated with a conductive liquid. The cylindrical inclusions appear in the system when the porosity value exceeds the void percolation. Parameters, that characterize the inclusions (the aspect ratio of spheroidal inclusions and the relative part of cylindrical inclusions), are determined in the inverse problem solving process for the experimental data approximation of the effective conductivity as a porosity function. These parameters, obtained by solving the inverse problem, were used to calculate the effective elastic moduli, electrical conductivity, and dielectric permittivity of porous media. The results obtained describe well the available experimental data for different effective physical properties.

A differential scheme for the effective conductivity of microinhomogeneous materials with the Hall effect

A differential scheme is proposed for the calculation of the components of the effective electrical conductivity tensor of a microinhomogeneous material taking into account the Hall effect. The presence of the Hall effect results in an appearance of asymmetry of the components of the conductivity tensor and a dependence of these components on the magnitude of the magnetic field applied to the material. In this case, the differential scheme leads to a system of matrix differential equations that were solved numerically in the current work. This solution was obtained for materials containing spherical or cylindrical inclusions. In the case of cylindrical inclusions, the results were obtained for inclusions with the symmetry axes parallel or orthogonal to the magnetic field. A comparison of the results obtained by the proposed methodology to the low concentration approximation was carried out.

Impacts of Solid Rock Components on Resistivity-based Reserves Evaluation in Organic-Rich Mudrocks

The effective electrical conductivity of organic-rich mudrocks can be influenced by the presence of clay, pyrite, and thermally mature organic matter. In this paper, we investigate the impacts of conductivity, volumetric concentrations, and spatial distribution/connectivity of clay, pyrite, and kerogen networks on the effective conductivity of the rock and how this affects the assessment of water/hydrocarbon saturation. This quantification enables an understanding of when such components need to be considered in interpreting resistivity measurements in organic-rich mudrocks. We perform sensitivity analysis on the impacts of (a) thermal-maturity-dependent kerogen conductivity, (b) volumetric concentration/spatial distribution of kerogen, (c) conductivity and volumetric concentration/spatial distribution of pyrite, and (d) cation exchange capacities of various clay minerals and their concentration/spatial distribution on the effective conductivity of the rock. The sensitivity analysis showed that with increasing pyrite concentration, the effective conductivity of the rock increased. This increment became significant when the concentration of pyrite was above 2%. Lastly, we observed that thermally mature kerogen increased the conductivity of the rock, with a significant increment occurring when the kerogen conductivity was above 0.01 S/m. Results confirmed that the incorporation of conductive rock components such as clay, pyrite, and kerogen into the electrical rock physics model improved the estimates of hydrocarbon reserves.

Analytical modeling of the electrical conductivity of CNT-filled polymer nanocomposites

Electrical conductivity of most polymeric insulators can be drastically enhanced by introducing a small volume fraction [Formula: see text] of conductive nanofillers. These nanocomposites find wide-ranging engineering applications from cellular metamaterials to strain sensors. In this work, we present a mathematical model to predict the effective electrical conductivity of carbon nanotubes (CNTs)/polymer nanocomposites accounting for the conductivity, dimensions, volume fraction, and alignment of the CNTs. Eshelby’s classical equivalent inclusion method (EIM) is generalized to account for electron-hopping—a key mechanism of electron transport across CNTs, and is validated with experimental data. Two measurements, namely, the limit angle of filler orientation and the probability distribution function, are used to control the alignment of CNTs within the composites. Our simulations show that decreasing the angle from a uniformly random distribution to a fully aligned state significantly reduces the transverse electrical conductivity, while the longitudinal conductivity shows less sensitivity to angle variation. Moreover, it is observed that distributing CNTs with non-uniform probability distribution functions results in an increase in longitudinal conductivity and a decrease in transverse conductivity, with these differences becoming more pronounced as the volume fraction of CNTs is increased. A reduction in CNT length decreases the effective electrical conductivity due to the reduced number of available conductive pathways while reducing CNT diameter increases the conductivity.

Electrically Programmed Doping Gradients Optimize the Thermoelectric Power Factor of a Conjugated Polymer

AbstractFunctionally graded materials (FGMs) are widely explored in the context of inorganic thermoelectrics, but not yet in organic thermoelectrics. Here, the impact of doping gradients on the thermoelectric properties of a chemically doped conjugated polymer is studied. The in‐plane drift of counterions in moderate electric fields is used to create lateral doping gradients in films composed of a polythiophene with oligoether side chains, doped with 2,3,5,6‐tetrafluoro‐tetracyanoquinodimethane (F4TCNQ). Raman microscopy reveals that a bias voltage of as little as 5 V across a 50 µm wide channel is sufficient to trigger counterion drift, resulting in doping gradients. The effective electrical conductivity of the graded channel decreases with bias voltage, while an overall increase in Seebeck coefficient is observed, yielding an up to eight‐fold enhancement in power factor. Kinetic Monte Carlo simulations of graded films explain the increase in power factor in terms of a roll‐off of the Seebeck coefficient at high electrical conductivities in combination with a mobility decay due to increased Coulomb scattering at high dopant concentrations. Therefore, the FGM concept is found to be a way to improve the thermoelectric performance of not yet optimally doped organic semiconductors, which may ease the screening of new materials as well as the fabrication of devices.

Thermal and mechanical investigation of proton exchange membrane fuel cells under combined loading conditions

Mechanical performance of PEM fuel cells under combined loadings is one of critical issues in improving cell performance and reliability. In this study, thermal and mechanical performance of the PEM fuel cell under clamping force and temperature load are investigated by experiments and three-dimensional dynamic simulations. In experiments, pressure distributions between gas diffusion layer and flow field are measured by pressure sensitive films. The experimental results show that the local pressure increases with clamping load, and pressure distribution is much uneven in the midstream. Meanwhile, a three-dimensional model of the cell combined transient heat transfer and mechanical response of components is developed, which is validated by experimental data. The modeling results show that under combined loadings, the porosity of gas diffusion layer under the rib decreases, attributed to compressive deformation caused by thermal gradient and clamping pressure, leading to higher effective thermal conductivity and electrical conductivity. When at a higher temperature, the overall stresses of gas diffusion layer decrease and the minimum stress is located under the center rib, forming a concave stress distribution pattern. Distribution and variation patterns of local deformations and stresses for the membrane are similar to those of gas diffusion layer, but the maximum stress and deformation are much lower. A dimensional stress coefficient is proposed to determine the stress distribution uniformity under the rib. With the increase of clamping torque, dimensionless stress coefficients under lower ambient temperature change slightly. As temperature increases, dimensionless stress coefficients increase significantly and decrease with the clamping torque, indicating the decreasing role of temperature load on mechanical response of the cell.

Plant functional traits mediate the response magnitude of plant-litter-soil microbial C: N: P stoichiometry to nitrogen addition in a desert steppe

Global nitrogen deposition is significantly altering the carbon (C), nitrogen (N) and phosphorus (P) stoichiometry in terrestrial ecosystems, yet how N deposition simultaneously affects plant-litter-soil-soil microbial stoichiometry in arid grassland is still unclear. In a five-year experimental study conducted in a desert steppe in Northern China, we investigated the effects of N addition on the C:N:P stoichiometry of plants, litter, soil, and soil microbes. We also used structural equation modelling (SEM) exploring the direct or indirect effects of N addition, plant species diversity, functional traits and diversity, soil microbial diversity, soil pH, soil electrical conductivity (EC) and moisture on the stoichiometry in plant-soil system. The results showed that N addition increased the N, P concentrations and N:P in plants, the N concentration and N:P in litter, and the C, N concentrations, C:P and N:P in microbes. Conversely, it decreased the C:N and C:P in plants, and litter C:N. Functional traits, functional dispersion (FDis), soil pH and EC accounted for a substantial proportion of the observed variations in elemental concentrations (from 42 % to 69 %) and stoichiometry (from 9 % to 73 %) across different components. SEM results showed that N addition decreased C:N and C:P in plants and litter by increasing FDis and leaf N content, while increased plant and litter N:P by decreasing leaf C content and increasing specific leaf area, respectively. Furthermore, N addition increased microbial C:P by increasing leaf thickness. We also found the mediating effects of soil pH and EC on C:N, C:P of litter and microbial N:P. Overall, our research suggests that plant functional traits as key predictors of nutrient cycling responses in desert steppes under N addition. This study extends the application of plant functional traits, enhances our understanding of C and nutrient cycling and facilitates predicting the response of desert steppes to N deposition.

Effects of a deficient interface, tunneling size and interphase depth on the percolation inception, percentage of graphene in the nets and conductivity of nanocomposites

In graphene systems, an issue of incomplete interfacial adhesion between the polymer matrix and particles is examined. Two key parameters are introduced: “Dc”, representing the minimum diameter of nanosheets necessary for effective electrical conductivity transfer from the super-conductive filler to the matrix, and “ψ”, denoting the interfacial conduction. Subsequently, the onset of percolation and the volumetric proportion of nanosheets within the networks are determined based on the effective opposite aspect ratio, effective filler share, depth of the interphase, and tunneling dimension. An equation is then proposed to calculate the conductivity of the samples. This study discusses the impacts of various factors, including the proportion of graphene in the networks, percolation inception, and conductivity. Additionally, experimental data are compared with the predictions of the proposed equations. The findings reveal that lower “Dc” values, higher “ψ”, thicker interphase, and lower graphene conductivity positively influence the percolation threshold, network proportions, and overall conductivity.

Metagenomic insights into microbial diversity and carbon cycling related genes along the elevational gradient in an arid mountain ecosystem

Understanding the elevational patterns of soil microbial carbon (C) metabolic potentials is instrumental for predicting changes in soil organic carbon (SOC) stocks in the face of climate change. However, such patterns remain uncertain in arid mountain ecosystems, where climosequences are quite different from other ecosystems. To address this gap, this study investigated the distribution determinants of microbial communities, C-cycling functional genes, and SOC fractions along the elevational gradient (1 707−3 548 m, a mean annual precipitation (MAP) range of 38 to 344 mm) of the north slope of Central Kunlun Mountain, using a metagenomic approach. Results showed that elevation significantly influenced the α-diversity (Shannon index) and composition of microbial communities as well as the C-cycling functional genes. The α-diversity of microbial taxonomy and C-cycling functional genes linearly increased with the increase in MAP along the elevational gradient. The elevational patterns of genes encoding glycoside hydrolases and glycosyltransferases (GTs) were mainly driven by electrical conductivity (EC), mean average temperature (MAT), MAP, and plant diversity. Furthermore, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and their sum generally increased with elevation. However, the MAOC/POC ratio followed a unimodal pattern, suggesting greater stability of the SOC pool in the mid-elevation regions. This unimodal pattern was likely influenced by the abundance of Actinobacteria and genes encoding GTs and carbohydrate esterases (CEs) and threshold effects of EC and MAT. In summary, our findings indicate that the distribution patterns of microbial communities and C-cycling functional genes along the elevational gradient in an arid ecosystem are distinct from those of regions with higher MAP, facilitating the prediction of climate change effects on SOC metabolism under more arid conditions. Soil salinity, plant diversity, precipitation, and temperature are the main regulatory factors of microbial C metabolism processes, and they potentially play a central role in mediating SOC pool stability.

Numerical modeling of the effects of the shape and aspect ratio of 3D curved fiber on the percolation threshold and electrical conductivity of conductive polymer composites.

For designing conductive polymer composites (CPCs), understanding how the fiber curvature affects the percolation behavior of curved conductive fibers is essential for determining the effective electrical conductivity σeff of the CPCs. In this work, CPCs were considered as a polymer matrix filled with the random packing of overlapped curved spherocylinders. The geometries of the curved spherocylinders were defined, and inter-curved spherocylinder contact-detecting and system-spanning fiber cluster searching algorithms were developed. The finite-size-scaling method was used to explore how the aspect ratio α and bending central angle θ of a curved spherocylinder affect the percolation threshold ϕc of an overlapped curved spherocylinder system in 3D space. The findings suggest that ϕc decreases as α increases and increases initially before declining as θ increases. An empirical approximation formula was proposed to quantify the effect of the curved spherocylinder's morphology, characterized by the dimensionless excluded volume Vdex of the curved spherocylinder, on ϕc. The new rigorous bound for ϕc of the soft-curved spherocylinder system was further proposed. A random resistor network model was constructed, and the reliability of this model was validated by comparing the simulations and published data. Finally, a fitting formula was developed to assess the impacts of the normalized reduced density (η - ηc)/ηc and Vdex on the σeff of CPCs. A distinct linear correlation between σeff and (η - ηc)/ηc was constructed, denoted as σeff ∼ [(η - ηc)/ηc]t(α,θ). An empirical approximation model was proposed to establish the relationship between the fiber shape and conductivity exponent t. Our study may provide a theoretical hint for the design of CPCs.

Effective electrical conductivity of random resistor networks generated using a Poisson–Voronoi tessellation

We studied the effective electrical conductivity of dense random resistor networks (RRNs) produced using a Voronoi tessellation when its seeds are generated by means of a homogeneous Poisson point process in the two-dimensional Euclidean space. Such RRNs are isotropic and in average homogeneous; however, local fluctuations of the number of edges per unit area are inevitable. These RRNs may mimic, e.g., crack-template-based transparent conductive films. The RRNs were treated within a mean-field approach. We found an analytical dependency of the effective electrical conductivity on the number of conductive edges (resistors) per unit area, nE. The effective electrical conductivity is proportional to nE when nE≫1.

Structure-property relationships in the electrical conductivity of multilayer graphene sheet/epoxy nanocomposites

The relationship between the physicochemical properties of multilayer graphene sheets (GSs), their resulting state of dispersion, and the effective electrical conductivity of GS/epoxy nanocomposites is investigated. To construct such relationship, four types of GSs with dissimilar properties were physicochemically, structurally, and morphologically characterized, and correlated with the direct current and alternating current (AC) electrical responses of their polymeric nanocomposites. It was found that the most influential parameters that affect the electrical conductivity of the nanocomposites are the size and density of agglomerates formed at the mesoscale. Both parameters, in turn, are governed by the physicochemical properties of the individual GSs. At the nanostructure level, the lateral size of the GS arises as the most influential parameter, with larger GSs yielding higher conductivities and lower percolation thresholds. The carbon/oxygen ratio of the GSs also showed moderate influence. The nanocomposites showed a resistive (R)-capacitive (C) electrical response in AC, which fits to a two-element parallel RC model. Both, the power law and general effective media (GEM) models predict similar percolation thresholds, but the GEM model is more general and adequately reproduces the whole electrical curve for all filler concentrations.

Growth, structural, thermal, and optical characteristics of L-asparagine monohydrate doped magnesium sulphate heptahydrate semiorganic crystals

A novel semi-organic crystal has been grown using slow evaporation technique by doping organic compound L-asparagine monohydrate (C4H8N2O3·H2O) with inorganic material Magnesium sulphate heptahydrate (MgSO4·7H2O). The crystallographic parameters like strain, dislocation density and crystallite size were calculated by powder X-ray diffraction method. Functional groups were identified and bond length, force constants were calculated from FT-IR spectroscopy. Energy dispersive X-ray (EDX) analysis was used to identify the constituent elements of the crystal. Kinetic and thermodynamic parameters, such as, activation energy Ea, change in Gibb's free energy (ΔG) and change in enthalpy (ΔH) have been determined by thermogravimetric analysis (TGA) analysis. Ea, ΔH and ΔG show positive values and change in entropy (ΔS) shows negative ones. The thermal degradation behavior of the crystals has been analyzed by differential scanning calorimetry (DSC) analysis. Various optical constants such as optical band gap, lattice dielectric constant, absorbance, extinction coefficient, the ratio of free charge carrier concentration to the effective mass, Urbach energy, optical and electrical conductivities were estimated from UV–vis transmittance data. High optical conductivity (1010 s−1) justifies the good photo response nature of the semi-organic crystal.