Electrical Conductivity Model Research Articles

A FRACTAL MODEL FOR ELECTRICAL CONDUCTIVITY OF POROUS MEDIA EMBEDDED WITH A DAMAGED TREE-LIKE NETWORK

Predicting the electrical conductivity of porous media is important for oil reservoirs, rock physics, and fuel cells. In this work, a cuboid embedded with a damaged tree-like network is employed to denote a portion of porous media. Analytical expressions for the electrical conductivity are then derived with the fractal theory. Various structural parameters have been examined in detail for the influence of electrical conductivity. It is found that the increased number of damaged channels means more difficult ion migration and lower electrical conductivity of porous media. In addition, a decreasing length ratio or an increasing diameter ratio will increase electrical conductivity. Moreover, both the channel distribution fractal dimension and tortuosity fractal dimension result in a decrease in electrical conductivity. A comparison of the results predicted by other models yields a good agreement, validating our proposed model. These results may further interpret the transport properties of porous media.

Multi-scale imaging of 3-D electrical conductivity structure under the contiguous US constrains lateral variations in the upper mantle water content

The magnetotelluric (MT) component of the USArray survey is a unique data set that enables imaging the electrical conductivity distribution from the surface down to astenospheric depths. Here, we present a new 3-D electrical conductivity model (MECMUS-2022) derived by inverting data from 1291 USArray MT stations covering ∼80% of the contiguous United States on a quasi-regular 70-km grid. The inversion was performed using a novel multi-scale imaging approach that can consistently incorporate a large range of spatial scales and perform 3-D modeling directly in the spherical frame, completely avoiding the conventional flat-Earth assumption. Furthermore, the use of locally refined meshes allows us to take into account the complex coastline and mimic the natural resolution footprint of broadband MT transfer functions. We find conductivity variations that correlate with known continental structures such as due to the active tectonic processes within the western United States (e.g., Yellowstone hotspot, Basin and Range extension, and subduction of the Juan de Fuca slab) as well as the presence of deep roots beneath cratons. We further interpreted conductivity variations in terms of the upper mantle water content by coupling electrical conductivity with constrains on mantle thermo-chemical structure derived from the analysis of seismic data (in the form of P-to-s and S-to-p receiver functions). Our results suggest the existence of a relatively dry upper mantle beneath the United States, with a weak trend in the upper mantle water content from North to South.

Data-driven electrical conductivities of dense plasmas

A wide range of theoretical and computational models have been developed to predict the electrical transport properties of dense plasmas, in part because dense plasma experiments explore order-of-magnitude excursions in temperature and density; in experiments with mixing, there may also be excursions in stoichiometry. In contrast, because high pressures create transient and heterogeneous plasmas, data from experiments that isolate transport are relatively rare. However, the aggregate of our datasets continues to increase in size and plays a key role in the validation of transport models. This trend suggests the possibility of using the data directly to make predictions, either alone or in combination with models, thereby creating a predictive capability with a controllable level of agreement with the data. Here, such a data-driven model is constructed by combining a theoretical model with extant data, using electrical conductivity as an example. Discrepancy learning is employed with a theoretical model appropriate for dense plasmas over wide ranges of conditions and a dataset of electrical conductivities in the solid to expanded warm dense matter regimes. The resulting discrepancy is learned via a radial basis function neural network. Regularization of the network is included through centers chosen with silhouette scores from k-means clustering. The covariance properties of each cluster are used with a scaled Mahalanobis distance metric to construct anisotropic basis functions for the network. The scale is used as a hyperparameter that is used to optimize prediction quality. The resulting predictions agree with the data and smoothly transition to the theoretical model away from the data. Detailed appendices describe the electrical conductivity model and compare various machine-learning methods. The electrical conductivity data and a library that yields the model are available at GitHub.

Electrical Conductivity Modeling and Fabrication of Carbon Nanotubes/Silicone Rubber Composites for High Voltage Insulators in Regions With a Polluted Atmosphere

The condensation mechanism of the insulators’ surface, increase the surface electrical conductivity. The electrical conductivity of the insulator creates a high level of leakage current and causes the failure. Difference between the dew point and the surface temperature of the insulator which occur due to radiative cooling mechanism, is the major reason of the moisture condensation on the polluted insulator surface. To compensate for the temperature difference, carbon nanotubes (CNTs) are being added to the silicone rubber housing material. The main idea of this research is based on generating joule heating by reducing the surface resistance of high voltage insulators. For this purpose, a developed model based on Halpin–Tsai formulation for tensile modulus of nanocomposites is joined with the conventional power-law model for the effective electrical conductivity of silicone rubber matrix carbon nanotube composite (SMCNT). SMCNT samples are prepared with the addition of various CNT volume fractions to high temperature vulcanizing (HTV) silicone rubber by melt-blending method. The developed model reveals that the high fraction of thinner and longer CNT, thicker interphase, higher concentration of percolated CNT, lower waviness of CNT, and higher conductivity of CNT can make a higher effective conductivity for SMCNT.

Modeling of Electrical Conductivity for Polymer-Carbon Nanofiber Systems.

There is not a simple model for predicting the electrical conductivity of carbon nanofiber (CNF)–polymer composites. In this manuscript, a model is proposed to predict the conductivity of CNF-filled composites. The developed model assumes the roles of CNF volume fraction, CNF dimensions, percolation onset, interphase thickness, CNF waviness, tunneling length among nanoparticles, and the fraction of the networked CNF. The outputs of the developed model correctly agree with the experimentally measured conductivity of several samples. Additionally, parametric analyses confirm the acceptable impacts of main factors on the conductivity of composites. A higher conductivity is achieved by smaller waviness and lower radius of CNFs, lower percolation onset, less tunnel distance, and higher levels of interphase depth and fraction of percolated CNFs in the nanocomposite. The maximum conductivity is obtained at 2.37 S/m by the highest volume fraction and length of CNFs.

Modeling of electrical conductivity of graphene-based polymer nanocomposites: calculation from the first principles

The use of nanocomposite materials has led to progress in the creation of new electronic devices (minitransistors, sensors, micro-drives, which are used to build artificial muscles, and supercapacitors. Nanocomposites occupy a special place with magnetosensitive fillers, particularly successfully used in medicine. Nanocomposites are also used for a protective coating. Depending on the operational functions, achieving a specific conductivity value and its change with temperature is necessary for such a coating. In the work, a conductivity model of polymer nanocomposites based on graphene (Gr/PS) was obtained using experimental data. The largest relative deviation between the conductivity surface and experimental data does not exceed 9.5%. The expression was obtained for the graphene concentration 1 < C(Gr) < 30 wt % and the temperature range 20 < T < 100 °C. The dependence of the specific electrical conductivity on the filler concentration and temperature obtained in the work will allow the researchers to select a nanocomposite with the required conductivity and evaluate the temperature effects on it for the conditions to which the material will be exposed.

Electrical conductivity of sand–clay mixtures

This paper presents the results of the electrical conductivity measurements of sand–clay mixtures that were formed of three types of sands and five pure clays and prepared with water of low salinity. The results show that an increase in clay content results in an increase in the overall electrical conductivity. Moreover, a direct or inverse relationship between the overall soil electrical conductivity (σ T) and porosity (n) is observed depending on whether the water conductivity (σw) is higher or lower than the clay particle surface conductivity (σ s). Common predictive models for electrical conductivity were developed for sands and clayey sands with a clay content of less than 20%. This research extended these studies to include clays, sandy clays and clayey sands to show that it is possible to predict the electrical conductivity of a soil from its composition and fabric.

Modeling Laminated Al2O3/HfO2 RRAM Based on Oxygen Vacancy Conduction

A Al2O3/HfO2 RRAM electro-thermal coupled model was constructed by CMOSOL, which considered the effect of oxygen vacancy. The model includes ion migration, electrical conduction and Joule heating models, and the parameters involved are the same as those of the fabricated devices. The model has include the effect of temperature and oxygen vacancy distribution. In addition, this paper simulates the resistance characteristics of the Reset/Set process, and compares and analyzes the difference between the fracture position and the internal mechanism of the conductive filaments in the laminated structure. The work can help further understand the mechanism of laminated Al2O3/HfO2 RRAM.

Hybrid nanofluid flow comprising spherical shaped particles with Hall current and irreversibility analysis: an application of solar radiation

This exploration intends to appraise the traits of hybrid nanofluid flow in a rotating channel influenced by the Hall current. The hybrid nanofluid is comprised of spherical nanoparticles of Silicon dioxide (SiO2 ) and Titanium dioxide (TiO 2) submerged in water. The hybrid nanofluid is performed as a solar energy collector. The heat analysis is conducted considering the non-uniform heat sink, Joule heating, and non-linear thermal radiation. To analyze the performance of spherical particles, the Maxwell model of thermal and electrical conductivity is used. Tiwari-Das hybrid nanofluid flow model is adopted here. The irreversibility analysis of the whole system is also an added feature of this study. The conversion of PDEs is by employing appropriate transformations into ODEs. A MATLAB software tool bvp4c is used to solve the translated mathematical formulations. Significant parameters’ physical behavior and graphical representations are thoroughly investigated. It is comprehended that the velocity profiles are influenced by the rotation parameter. The findings also revealed that the flow of hybrid nanofluid outperforms the nanofluid in solar thermal systems. Further, for escalating values of the rotational parameter, the surface friction increases, and the Nusselt number drops. For validation, a tabular comparison is also attempted. An excellent correlation is achieved.

Optimization of multi-frequency electromagnetic surveying for investigating waste characteristics in an open dumpsite

ABSTRACT Multi-frequency electromagnetic (EM) surveying is a type of frequency-domain electromagnetic (FDEM) survey, a new geophysical exploration approach used as a scanning tool to investigate waste characteristics within a final disposal site before electrical resistivity tomography (ERT) surveying. EM surveys provide information about shallow subsurface conditions; however, to date, no studies on waste bodies or waste characteristics in open dumpsites have assessed the optimal frequency for use in multi-frequency EM surveys. This research aims to determine the optimal frequency set for multi-frequency EM surveying for initial waste quality assessment in open dumpsites. These initial waste quality data are critical to assess the feasibility of open dump mining projects. First, a multi-frequency EM survey was performed in this study, which was measured at the exact electrode positions of the three ERT profiles. This FDEM survey used 15 frequencies ranging from 1,000–⁠15,000 Hz, which were grouped into frequency sets for inversion modeling of the electrical conductivity. Second, an ERT survey was carried out along all three profiles, each of which was around 144 m long. Subsequently, synthetic electrical resistivity data were generated by processing the electrical conductivity model. Data from each frequency set were then generated at the same depth, with interpolation techniques used to analyze the linear correlation between synthetic electrical resistivity and real electrical resistivity data and compare the Pearson’s correlation coefficient (R) values to determine the optimum frequency set for FDEM surveys in the open dumpsite. The results showed that the E4 frequency set, consisting of 5,000, 11,000, and 15,000 Hz values, typically had the highest positive correlations (R = 0.74–0.91) across all profiles and provided waste characteristics information close to the true conditions. Thus, these frequencies represent the optimal set for multi-frequency EM surveys for the primary assessment of waste characteristics in open dumpsites. Implications: Open dump mining (ODM) is now applied as a sustainable approach to combat improper waste disposal and reduce municipal solid waste (MSW) in the open dumpsite. To implement ODM for producing RDF, business developers must know the amount and composition of waste that can be converted into RDF before mining. This study used multi-frequency EM surveys with frequencies of 5,000, 11,000, and 15,000 Hz. This multi-frequency method effectively determined the waste composition and identified potential excavation points in the open dumpsite prior to ODM. This method can mitigate the limitations of traditional surveying, due to its improved mobility, lower time consumption, and reduced labor needs.

Modeling of Electrical Conductivity for Graphene-Filled Products Assuming Interphase, Tunneling Effect, and Filler Agglomeration Optimizing Breast Cancer Biosensors.

In this study, the percolation inception, actual filler amount, and concentration of nets are expressed using the filler size and agglomeration, interphase depth, and tunneling size. A modified form of the power-law model is recommended for the conductivity of graphene–polymer products using the mentioned characteristics. The modified model is used to plot and evaluate the conductivity at dissimilar ranges of factors. In addition, the prediction results of the model are compared with the experimented values of several samples. A low percolation inception and high-volume portion of nets that improve the conductivity of nanoparticles are achieved at a low agglomeration extent, thick interphase, large aspect ratio of the nanosheets, and large tunnels. The developed equation for percolation inception accurately predicts the results assuming tunneling and interphase parts. The innovative model predicts the conductivity for the samples, demonstrating good agreement with the experimented values. This model is appropriate to improve breast cancer biosensors, because conductivity plays a key role in sensing.

Proximal Soil Sensing of Low Salinity in Southern Xinjiang, China

Measuring the soil salinity using visible and near-infrared (vis–NIR) reflectance spectra is considered a fast and cost-effective method. For monitoring purposes, estimating soils with low salinity measured as electrical conductivity (EC) using vis–NIR spectra is still understudied. In this research, 399 legacy soil samples from six regions of Southern Xinjiang, China with low EC values were used. Reflectance spectra were measured in the laboratory on dried and ground soil samples using a portable vis–NIR spectrometer. By using 10-fold cross-validation, three algorithms–partial least-squares regression (PLSR), random forest (RF), and Cubist–were employed to develop statistical models of EC. The model performance evaluation was obtained by the relative importance of variants. In terms of accuracy assessment of soil EC prediction, the results demonstrated that the Cubist model performed better (R2 = 0.67, RMSE = 0.16 mS/cm, RPIQ = 2.28) than both PLSR and RF. Despite similar variants for modelling, the RF model performed somewhat better than that of the PLSR. Additionally, the 610 nm and 790 nm wavelengths only demonstrated significant promise for predicting low soil EC values when used in the Cubist mode. The current research recommends the use of Cubist to estimate the low soil salinity using the vis–NIR reflectance spectra.

Characterization of Spatial Variability of Micro Nutrients in Soils: Classical Vs. Geo-Statistical Approach

ABSTRACT The spatial variability characterization of micronutrients is needed for agricultural quality produce and environmental safety. However, study on this aspect is weakly implicit. In present study, Global Positioning System (GPS)-based 283 surface soil samples were collected and analyzed for pH, electrical conductivity (EC), organic carbon (OC), and micronutrients. Classical results revealed that soils are slightly acidic pH, safe in EC, low to medium in OC, and non-calcareous. OC was in order of Typic Haplustalfs-Chromic Haplusterts (S8)>Vertic Ustochrepts-Chromic Haplusterts (S3)>Typic Haplustalfs-Typic Haplustalfs (S9)>Typic Haplustalfs-Typic Ustipsammarts (S7)>Typic Haplusterts-Typic Ustorthents (S6)>Vertic Ustochrepts -Chromic Haplusterts (S5)> Lithic Ustochrepts -Typic Haplusterts (S4)>Typic Haplusterts -Vertic Ustochrepts(S2)>Typic Haplusterts-Typic Haplusterts (S1). In soils, zinc (Zn) and hot water-soluble boron (HWS-B) in order of S4>S5>S7>S9>S3>S2>S8>S1>S6 and S1>S2>S3>S4>S5>S9>S6>S7>S8, respectively. Overall, about 54.06 and 9.89% soil samples to be deficient in Zn and HWS-B in study area, respectively. Geo-statistical results showed that the exponential model was best-fitted for pH, calcium carbonate (CaCO3), OC, Zn and HWS-B and spherical model for EC and iron (Fe). However, the circular model was fitted for copper (Cu) and Manganese (Mn). The pH, EC, OC Zn, Cu, and Fe fall between 26 and 71%, which exhibit moderate spatial dependency. However, CaCO3 and Mn show 81 and 76% Nugget/sill ratios, respectively. Variability maps also showed the prediction of micronutrients using geo-statistical method better than classical methods.

Electrical conductivity of semiconducting nanoparticles

A simple and comprehensive theoretical model has been presented for the computation of the electrical conductivity for nanosolids, considering the size, the shape, the relaxation factor and the packing fraction dependent cohesive energy. The computational results for the electrical conductivity obtained through the present model, for CdSe, Bi2S3, Si and Ge semiconducting nanosolids in different shapes demonstrate that the electrical conductivity lowers down as the size of particle decreases. It is also observed that the electrical conductivity of spherical nanoparticle shifts towards tetrahedral shape which indicates that the structure of the nanomaterials also changes at its lower range and hence their electrical property changes accordingly. Our computed results show good agreement with the available experimental findings which authenticates the present comprehensive picture. The present model for electrical conductivity of nano-solids can be applied for the materials in the nano-range which have not been investigated experimentally.

Full-waveform inversion of ground-penetrating radar data in frequency-dependent media involving permittivity attenuation

SUMMARYFull-waveform inversion (FWI) of ground-penetrating radar (GPR) data has received particular attention in the past decade because it can provide high-resolution subsurface models of dielectric permittivity and electrical conductivity. In most GPR FWIs, these two parameters are regarded as frequency independent, which may lead to false estimates if they strongly depend on frequency, such as in shallow weathered zones. In this study, we develop frequency-dependent GPR FWI to solve this problem. Using the τ-method introduced in the research of viscoelastic waves, we define the permittivity attenuation parameter to quantify the attenuation resulting from the complex permittivity and to modify time-domain Maxwell’s equations. The new equations are self-adjoint so that we can use the same forward engine to back-propagate the adjoint sources and easily derive model gradients in GPR FWI. Frequency dependence analysis shows that permittivity attenuation acts as a low-pass filter, distorting the waveform and decaying the amplitude of the electromagnetic waves. The 2-D synthetic examples illustrate that permittivity attenuation has low sensitivity to the surface multioffset GPR data but is necessary for a good reconstruction of permittivity and conductivity models in frequency-dependent GPR FWI. As a comparison, frequency-independent GPR FWI produces more model artefacts and hardly reconstructs conductivity models dominated by permittivity attenuation. The 2-D field example shows that both FWIs reveal a triangle permittivity anomaly which proves to be a refilled trench. However, frequency-dependent GPR FWI provides a better fit to the observed data and a more robust conductivity reconstruction in a high permittivity attenuation environment. Our GPR FWI results are consistent with previous GPR and shallow-seismic measurements. This research greatly expands the application of GPR FWI in more complicated media.

Deep Geological Controls on Formation of the Highest‐Grade Uranium Deposits in the World: Magnetotelluric Imaging of Unconformity‐Related Systems From the Athabasca Basin, Canada

AbstractUnconformity‐related uranium (URU) deposits in the Athabasca Basin, Canada, include the highest grade, large tonnage deposits in the world. Recent studies of the Patterson Lake corridor uranium deposits suggest a deep heat source enhances hydrothermal fluid flow and incursion of basinal brines along brittlely reactivated ductile shear zones. However, the spatial extent of the ore systems and lower crustal features capable of driving the hydrothermal cells remained largely unresolved. Three‐dimensional electrical conductivity models derived from inversion of magnetotelluric data and coincident gravity modeling identified a voluminous conductivity anomaly and corresponding gravity low related to a belt of high‐heat producing intrusions that dips below the deposit. Shallow conductive bodies are identified below the deposits and similarly prospective corridors. The volume and location of the intrusive bodies may have provided a source of radiogenic heat that helped drive remobilization of fluids along reactivated structures, redefining the spatial extent of URU systems.

On the kinetics of chemical reactions at the detonation of organic high explosives

The model of electrical conductivity developed earlier allows one to use electrical properties as a tool for the diagnostics of the reaction zone at the detonation of organic high explosives. The comparison of experimental data on electrical conductivity with both the results of experimental research and numerical modeling using Arrhenius kinetics is carried out. The contradiction of the thermal concept of the mechanism of the development of a chemical reaction during detonation is clearly demonstrated. An alternative based on the ideas of A. N. Dremin, J. J. Dick, C. S. Coffey, and F. E. Walker is discussed.

Influence of layers orientation of graphene stacks in shungite disordered carbon to its integral electrical conductivity

The map of carbon distribution in the sample of graphene-containing shungite is investigated by the electron microscopy. It is found the large quantity of stacks which are formatted by parallel disposed layers of graphene. The model of electrical conductivity of graphene stack is proposed. This model takes into consideration the arbitrary orientation of layers relatively to the direction of current. It is made the numerical calculation of dependence of conductivity from the orientation of graphene layers. It is found the sharp abatement of conductivity by the orientation of graphene layers relatively to the current direction along the corner near the 45 degree. The obtained peculiarities are explained by the influence of geometrical factor.

Crustal and Upper Mantle Imaging of Botswana Using Magnetotelluric Method

We used magnetotelluric data from 352 sites in Botswana to derive a country-wide electrical conductivity model of the crust and upper mantle structure. A robust methodological scheme and 3D inversion were used to derive a 3D electrical conductivity model with unprecedented spatial coverage. The model results show interesting features, including the major cratonic blocks and the mobile belts in Botswana. A distinctive resistive structure was imaged in southwest Botswana, which suggests the existence of the Maltahohe microcraton as a separate cratonic unit as proposed by other studies. Furthermore, the model gives new insight into the extension of the East African Rift System to Botswana and the incipient rifting in the Okavango Rift Zone. In northern Botswana, the electrical conductivity model shows a highly conductive structure beneath the Okavango Rift Zone, which connects with a deeper conductive structure that we attribute to the East African Rift System due to its vicinity to Lake Kariba, the last surface expression of the rift system. We suggest that ascending fluids or melt from the East African Rift System causes the weakening of the lithosphere and plays a significant role in the incipient continental rifting in the Okavango Rift Zone.

Integration of Electromagnetic Geophysics Forward Simulation in Coupled Flow and Geomechanics for Monitoring a Gas Hydrate Deposit Located in the Ulleung Basin, East Sea, Korea

We investigate the feasibility of electromagnetic (EM) geophysics methods to detect the dissociation of gas hydrate specifically from a gas hydrate deposit located in the Ulleung Basin, East Sea, Korea via an integrated flow-geomechanics-EM geophysics simulation. To this end, coupled flow and geomechanics simulation is first performed with the multiple porosity model employed, where a mixed formulation with the finite volume (FV) and finite element (FE) methods are taken for the flow and geomechanics, respectively. From the saturation and porosity fields obtained from the coupled flow and geomechanics, the electrical conductivity model is established for the EM simulation. Solving the partial differential equation of electrical diffusion which is linearized using the 3D finite element method (FEM), the EM fields are then computed. For numerical experiments, particularly two approaches in the configuration for the EM methods are compared in this contribution: the surface-to-surface and the surface-to-borehole methods. When the surface-to-surface EM method is employed, the EM is found to be less sensitive, implying low detectability. Especially for the short term of production, the low detectability is attributed to the similarity of electrical resistivity between the dissociated gas (CH4) and hydrate as well as the specific dissociation pattern within the intercalated composites of the field. On the other hand, when the surface-to-borehole EM method is employed, its sensitivity to capture the produced gas flow is improved, confirming its detectability in monitoring gas flow. Hence, the EM geophysics simulation integrated with coupled flow and geomechanics can be a potential tool for monitoring gas hydrate deposits.