Electrical Conductivity Model Research Articles

A Multi-Phase Analytical Model for Effective Electrical Conductivity of Polymer Matrix Composites Containing Micro-SiC Whiskers and Nano-Carbon Black Hybrids.

Multifunctional polymer composites containing micro/nano hybrid reinforcements have attracted intensive attention in the field of materials science and engineering. This paper develops a multi-phase analytical model for investigating the effective electrical conductivity of micro-silicon carbide (SiC) whisker/nano-carbon black (CB) polymer composites. First, CB nanoparticles are dispersed within the non-conducting epoxy to achieve a conductive CB-filled nanocomposite and its electrical conductivity is predicted. Some critical microstructures such as volume percentage and size of nanoparticles, and interphase characteristics surrounding the CB are micromechanically captured. Next, the electrical conductivity of randomly oriented SiC-containing composites in which the nanocomposite and whisker are considered as the matrix and reinforcement phases, respectively, is estimated. Influences of whisker aspect ratio and volume fraction on the effective electrical conductivity of the SiC/CB-containing polymer composites are explored. Some comparison studies are performed to validate the accuracy of the model. It is observed before the percolation threshold that the addition of nanoparticles with a uniform dispersion can improve the electrical conductivity of the polymer composites containing SiC/CB hybrids. Moreover, the results show that the electrical conductivity is more enhanced by the decrease in nanoparticle size. Interestingly, the composite percolation threshold is significantly reduced when SiC whiskers with a higher aspect ratio are added. This work will be favorable for the design of electro-conductive polymer composites with high performances.

A predictive model for electrical conductivity of polymer carbon nanofiber composites considering nanofiber/interphase network and tunneling dimensions

A predictive model for electrical conductivity of polymer carbon nanofiber composites considering nanofiber/interphase network and tunneling dimensions

A predictive model for electrical conductivity of polymer carbon black nanocomposites

AbstractAlthough many studies have investigated the percolation threshold and conductivity in the polymer carbon black (CB) nanocomposites (PCBs) by experimental data, the modeling in this field is a topic previously overlooked in literature. This paper suggests a simple equation for percolation threshold in PCBs by the surface properties of polymer and CB a well as interphase depth (t). Also, the electrical conductivity of PCBs is simulated by CB radius (R), tunneling distance (λ) and interphase depth. The offered model is validated by experimented findings of numerous samples and parametric examinations. A thicker interphase and a higher interfacial tension positively reduce the percolation onset. The smaller CBs, lower percolation threshold, and smaller tunnels enhance the electrical conductivity of PCBs. The low percolation threshold of 0.01 is obtained by the low R (15 nm) and the thick interphase (35 nm). Also, R = 15 nm and t = 35 nm maximize the PCB conductivity to 6 S/m. These results signify the important roles of CB radius and interphase depth in the percolation threshold and conductivity of PCBs. In addition, network fraction (f) of 0.7 and λ = 1 nm produce the uppermost PCB conductivity of 1.6 S/m, but the system is insulated at CB network fraction less than 0.35 or tunneling distance more than 2 nm. Therefore, high network fraction and low tunneling distance are needed to enhance the conductivity of PCBs.Highlights An equation for percolation onset of PCBs is proposed by CB radius and interphase depth. The offered model is validated by experimented data and parametric examinations. A thicker interphase and a higher interfacial tension positively reduce the percolation onset. Smaller CBs, lower percolation onset, and smaller tunnels enhance the PCB conductivity. CB radius and interphase depth significantly manage the conductivity of PCBs.

ECO: A new reference model of dry olivine electrical conductivity

Electrically Conductive Olivine, ECO, is presented as a new reference model for the electrical conductivity of dry olivine. The model takes into account the prevailing redox state, temperature and pressure in the upper mantle, as well as their effects on the conductivity, and is based on conductivity analyses of dry single crystal olivine at high pressures. The model demonstrates higher conductivity of olivine than those previously proposed standard olivine models, which rely on the conductivity of olivine measured at room pressure. Accepting that the bulk electrical structure is in general dominated by olivine, the upper mantle is more conductive than the estimates of the previous models. The olivine conductivity is in fact high at the oxidizing conditions of the shallow mantle, but drops at greater depths where the mantle portion is reducing. The model provides a new reference basis for understanding the electrical structure of the upper mantle, and is important for invoking any other conductive candidates to explain enhanced mantle conductivity.

Hysteresis of unfrozen water content of tailing mud with freeze-thaw and its correlation with electrical conductivity

Identifying the correlation between the hysteresis of unfrozen water content and the electrical conductivity (EC) of tailing mud with freeze-thaw is essential for determining the range of frost hazards in tailings ponds by field conductivity measurement and enabling targeted treatment in coastal and seasonally frozen areas. In this study, dynamics of unfrozen water content and temperature of saturated tailing mud samples with 0.0–5.0 % NaCl were evaluated with 5TM sensor while the EC with the frequency domain reflectometry (FDR) sensor. Results show that unfrozen water hysteresis of tailing mud with freeze-thaw occurred below phase-change temperatures, with the cooling section above the warming. The area of hysteresis curve rose upon higher salinity or fewer freeze-thaw cycles. Phase-change temperatures of tailing mud, including freezing and thawing points, depressed with higher salinity but were less affected by freeze-thaw cycles, with the former coinciding with the liquidus line of NaCl solution while the latter located above. The EC curve also exhibits hysteresis with freeze-thaw and the initial salinity determines both the maximum EC value and the slope logEC/T below phase-change temperatures. It was concluded that the unfrozen water content, converted salt concentration and EC of frozen tailing mud show synchronous changes. A modified Michalowski model, with phase-change temperatures and residual unfrozen water content respectively simplified as proportional and exponential functions of initial salinity, was established to characterize the unfrozen water hysteresis of tailing mud with freeze-thaw cycles. A simple EC model with hysteresis was then developed by approximating the EC of frozen tailing mud as a power function of the converted salt concentration, which was applied to the tailing mud with 0.5–5.0 % NaCl in the range of −20 °C up to phase-change temperatures.

Temperature and state-dependent electrical conductivity of soft biological tissue at hyperthermic temperatures

Objective: We present a physics-based, temperature and state-dependent electrical conductivity model for soft biological tissue under thermal therapies with a quantified damage parameter that represents the state of soft biological tissue (degree of denaturation). Most existing models consider electrical conductivity to be only temperature-dependent and evaluate tissue damage during post-processing after temperature calculation. Our model allows tissue damage to be coupled into the thermal model for a more accurate description of both RF ablation and electrosurgery. Methods: We model the denaturation process with an Arrhenius-type differential equation for chemical kinetics and a modified Stogryn equation for electrical conductivity under state transition. We present experimental data from two types of heating procedures at 128 kHz to validate and showcase the capability of our model. Results: Our model is able to capture the change in electrical conductivity during heating, cooling, and reheating procedures, which distinguishes different states and shows the irreversibility of denaturation. The model also accurately captures tissue change during slow cooking at a constant temperature, highlighting a state dependence. Conclusion: By incorporating state dependence into the model for electrical properties, we are able to capture the denaturation process more accurately and distinguish different degrees of damage. Our model allows the modeling of procedures involving repeated heating or cooling, which is impossible for models without a state dependence. While being able to adapt to patient-specific needs, the model can be used to improve planning and control in future robot-assisted surgeries to reduce unnecessary damage.

Analysis of Enceladus’s Time-variable Space Environment to Magnetically Sound its Interior

We provide a comprehensive study of Enceladus’s time-variable magnetic field environment and identify in measurements of the Cassini spacecraft signatures that appear to be consistent with induced fields from the moon’s interior. Therefore, we first analyze the background field Enceladus is exposed to within 21 flybys and 50 crossings of the moon’s orbit by the Cassini spacecraft. Considering magnetic field variability due to Enceladus’s eccentric orbit, Saturn’s planetary period oscillations, and local time effects within the magnetospheric current sheet, we construct predictive, time-variable background fields near Enceladus with a correlation coefficient of 0.75 and larger compared to the measured background fields. Subsequently, we build a geophysically based electrical conductivity model of Enceladus’s ocean from the equation of state for saline water and mixing laws for a porous core permeated by water. Using this conductivity model and the derived time-variable fields, we calculate expected induced fields. For close flybys, we identify within mostly plume-dominated magnetic field perturbations of 10–30 nT much smaller perturbations of 1–3 nT, which could be consistent with induction. The flybys over Enceladus’s north pole are best suited for induction studies, and the associated measurements suggest that a conductivity of the ocean with 1–3 S m–1 is not sufficient to produce an adequate induction response, but they support a highly conductive, porous core of 20–30 S m–1 and/or a more conductive ocean. Our study also provides strategies for future magnetic sounding of Enceladus.

Deep thermal state on the southern margin of the Zhangzhou Basin based on the electrical conductivity model

Exploring the internal spatial and thermal structure of the Zhangzhou Basin is of great scientific significance in understanding the properties of the deep heat sources and the heating mechanism of hot springs in this region. This study estimates the temperature distribution within the upper mantle of the Basin's southern margin using the Arrhenius equation and Hashin-Shtrikman bounds based on a two-dimensional crust-mantle electrical resistivity model. We also employ a layered simulation technique to calculate the crustal temperature distribution using a one-dimensional steady-state heat conduction equation, constrained by the upper mantle's top and ground surface temperatures. This approach displays the characteristics of the longitudinal variations and horizontal inhomogeneities in crust-mantle temperature. Additionally, we estimate the heat flow values within the study area. Our findings reveal that: (i) the upper mantle (at depths of 30 - 50 km) exhibits a temperature range of 700 - 1100 °C, with the presence of local Moho and upper mantle uplifts; (ii) the crustal temperature spans from 21 - 900 °C, with a diminishing influence of the upper mantle uplift area on crustal temperature at shallower depths; (iii) the surface heat flow values derived from our simulations range between 87 and 100 mW/m2, averaging at 93.23 mW/m2; (iv) the exploration of dry heat rock in this region is likely to reach a depth of at least 6 km. These results suggest that the genesis of hot springs in the study area is not solely influenced by the heat energy extracted from large-area granitic surrounding rocks during a long transport process, but is also considerably affected by local deep thermal anomalous bodies and deep-large faults.

Geostatistical Inversion of Frequency-Domain Electromagnetic Data for Near Surface Modelling

The detailed characterization of near-surface deposits is important for both environmental and economic reasons. These shallow subsurface systems can be very complex and heterogenous due to natural dynamics and anthropogenic interferences. Modeling techniques based exclusively on direct sampling generate limited informed three-dimensional models of the near-surface. Geophysical methods provide valuable and additional information to model the spatial distribution of the near-surface for locations where direct observations are not available. From this set of methods, frequency-domain electromagnetic induction (FDEM) has been successfully applied to image complex near-surface deposits. Yet, predicting the spatial distribution of relevant subsurface properties from geophysical data, and the integration of direct observations, is not straightforward. It requires solving a challenging geophysical inversion problem. Geostatistical modelling tools have been effectively applied to couple direct observations with geophysical data such as seismic reflection. We propose an iterative geostatistical FDEM inversion method able to integrate data from direct measurements of the near-surface with surface loop-loop FDEM measurements to simultaneously predict high-resolution models of electrical conductivity and magnetic susceptibility, and their associated uncertainty. The iterative geostatistical inversion method is based on stochastic sequential simulation and co-simulation as model perturbation and update techniques. The iterative optimization is based on the local data misfit between observed and simulated FDEM data, weighted by the sensitivity of the acquisition equipment. The proposed method is first demonstrated for a synthetic landfill data set created based on real data collected at a mine tailing disposal site in Portugal, and on a real data set collected at a site with archaeological features in Knowlton, UK. The results show the ability of the proposed method to accurately predict and characterize the spatial distribution of electrical conductivity and magnetic susceptibility down to the depth of interest while reproducing the recorded FDEM data.

Modeliranje električne vodljivosti grafitom modificiranog asfaltbetona na temelju mikromehanike

This paper proposes a novel micromechanics theory to model the electrical conductivity of graphite-modified asphalt concrete. The constraint range of electrical conductivity was derived according to the Hashin–Shtrikman (H–S) method. Based on the self-consistent (SC) method and percolation theory, A modified self-consistent (MSC) method was used to develop an electrical conductivity model based on the SC method and percolation theory. The results predicted by the MSC model were compared with the test data by adjusting the aspect ratio and percolation exponent. We highlight this new theory and its application to graphite-modified asphalt concrete, and demonstrate that the predicted values are in close agreement with the test data.

Estimation of electrical conductivity models using multi-coil rigid-boom electromagnetic induction measurements

Electromagnetic induction measurements from multi-coil configuration instruments are used to obtain information about the electrical conductivity distribution in the subsurface. The resulting inverse problem might not have a unique and stable solution. In that case, a local inversion method can be trapped in a local minimum and lead to an incorrect solution. In this study, we evaluate the well-posedness of the inverse problem for two and three-layered electrical conductivity models. We show that for a two-layered model, uniqueness is ensured only when both in-phase and quadrature data are available from the measurements. Results from a Gauss–Newton inversion and a lookup table demonstrate that the solution space is convex. Furthermore, we demonstrate that for even a simple three-layered model, the data contained in such measurements are insufficient to reach a correct or stable solution. For models with more than 2 layers, independent prior information is necessary to solve the inverse problem. The insights from the numerical examples are applied in a field case.

Machine learning prediction model for clay electrical conductivity and its application in electroosmosis consolidation

Machine learning prediction model for clay electrical conductivity and its application in electroosmosis consolidation

Electrical conductivity model for reactive porous media under partially saturated conditions with hysteresis effects

The electrical conductivity of a porous medium is strongly controlled by the structure of the medium at the microscale as the pore configuration governs the distribution of the conductive fluid. The pore structure thus plays a key role since different geometries translate in variations of the fluid distribution, causing different behaviors measurable at the macroscale. In this study, we present a new physically-based analytical model derived under the assumption that the pore structure can be represented by a bundle of tortuous capillary tubes with periodic variations of their radius and a fractal distribution of pore sizes. By upscaling the microscale properties of the porous medium, we obtain expressions to estimate the total and relative electrical conductivity. The proposed pore geometry allows us to include the hysteresis phenomenon in the electrical conductivity estimates. The variations on these estimates caused by pore structure changes due to reactive processes are accounted by assuming a uniform dissolution of the pores. Under this hypothesis, we describe the evolution of the electrical conductivity during reactive processes. The expressions of the proposed model have been tested with published data from different soil textures, showing a satisfactory agreement with the experimental data. Hysteretic behavior and mineral dissolution are also successfully addressed. By including hysteresis and mineral dissolution/precipitation in the estimates of the electrical conductivity, this new analytical model presents an improvement as it relates those macroscopic physical phenomena to its origins at the microscale. This opens up exciting possibilities for studies involving electrical conductivity measurements to monitor water movement, and hysteretic and reactive processes in porous media.

Electrical conductivity of mantle minerals beneath East Asia revealed by geomagnetic observatory data

The electrical conductivity of the earth’s mantle can provide important information about geodynamic phenomena. East Asia is associated with complex tectonics and geodynamic processes. Hence, it is necessary to better understand the deep structure beneath East Asia. In this study, geomagnetic data obtained from East Asian observatories are employed to image the conductivity structure of the mantle at depths ranging from 410 to 900 km. First, the data are processed using the modified bounded influence remote reference processing (BIRRP) method and the ratio method is used to correct for the ocean effect. Thereafter, the stable C-response curves at the 27 observatories are estimated, and 1D electrical conductivity models for these observatories are established using the L-BFGS method. The conductivity-depth profiles reveal a heterogeneous distribution of the electrical conductivity beneath East Asia. The mantle transition zone (MTZ) beneath East China and Japan is found to be more conductive, whereas the MTZ beneath central and southern regions of China is more resistive. In East China, the dehydration of the stagnant Pacific slab may lead to an increase in the conductivity of the mantle minerals. There is also the possibility of upwelling of the thermal material from the lower mantle beneath the Japanese Island arc. In Northwest China, there exists a large high-conductive body beneath the Tarim area, which could indicate an upwelling of the Tarim mantle plume. Our results provide insights into the deep structure of the earth at the mantle scale.

Electrical conductivity fluctuations as a tracer to determine time-dependent transport characteristics in hyporheic sediments

Assessing solute transport in riverbed sediments is important for quantifying the effective reactivity of hyporheic sediments and the magnitude of exchange flows between rivers and their river beds. A typical approach of estimating transport in riverbed sediments is by measuring natural tracers such as fluctuations of temperature or electrical conductivity (EC) and fitting models to them that assume time-independent travel time distributions, implying steady-state flow. Here, we use a transport parameterization that is based on the advection–dispersion equation (ADE) with coefficients that continuously vary in time. The ADE is solved numerically and its solution is fitted to measured EC time series using Bayesian parameter inference. A continuous function of model parameters is constructed by smoothly interpolating between point values with different temporal resolution, and Tikhonov regularization is used to avoid spurious parameter fluctuations. The approach is tested using EC time series synchronously measured in river water and hyporheic porewater of two urban rivers in Germany and one urban river in South Australia. For all datasets the goodness of fit was improved by introducing a time-dependent EC offset. Estimated porewater velocities were highly transient in three out of the four datasets with values increasing by up to a factor of six over the course of a day. Flux transients were likely related to both variations of hydraulic gradients along and spatial shifting of flow paths. Comparison to stationary, non-parametric deconvolution indicated that transient flow may induce multimodality in stationary travel time distributions. Given the high temporal dynamics of transport in the streambed sediments of the three investigated urban rivers, we envision that the presented model is also a valuable tool to improve the assessment of reactive transport in riverbed sediments.

An innovative model for electrical conductivity of MXene polymer nanocomposites by interphase and tunneling characteristics

The endeavor to forecast the electrical conductivity in composites constituted of MXene nanosheets and polymers presents a significant challenge due to the absence of a simplistic model. The present investigation introduces a comprehensive model that anticipates the electrical conductivity of specimens filled with MXene. The proposed methodology incorporates a multitude of variables that determine the total conductivity of the specimens. These variables encompass the size parameters of MXene, the percolation onset, the volumetric fraction of MXene, the tunneling distance, the interphase thickness, and the network fraction. The reliability of this methodology is rigorously tested using experimental data derived from several specimens. Moreover, a comprehensive examination of the relationship between the estimated conductivity and the variables is carried out to verify the trustworthiness of the suggested approach. The outcomes derived from the suggested model demonstrate a significant alignment with the results from the experiments. Factors such as slender and larger nanosheets, thicker interphase, smaller tunneling distance, and higher portion of percolated nanosheets in the network can significantly enhance the conductivity of nanocomposite. The maximum electrical conductivity of 14 S/m is attained with the lowest tunneling distance of 1.4 nm and the lowest percolation onset of 0.01. Additionally, with the maximum interphase thickness of 41 nm and the highest MXene conductivity of 3 ×106 S/m, the nanocomposite achieves an optimal conductivity of 2 S/m.

The spectral inversion model for electrical conductivity in mural plaster following phosphate erosion based on fractional order differentiation and novel spectral indices

The Dunhuang murals are a precious treasure of China’s cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive, circumventing issues of high costs, low efficiency. Building on this, the study constructs an inversion model for the Electrical Conductivity (EC) values of mural plaster subjected to phosphate erosion, through the integration of Fractional Order Differentiation (FOD), a novel three-band spectral index, and the Partial Least Squares Regression algorithm. The specific research contents include: (1) Initially, in preparation for the experiments, the materials used to create the samples underwent a rigorous desalting process, and phosphate solutions were prepared using deionized water to ensure uniform experimental conditions and the accuracy of the results. These meticulous preprocessing steps guaranteed that the measured EC values exhibited a clear correlation with the phosphate content. Subsequently, by employing qualitative experimental analysis techniques, this study was able to more accurately simulate the real-world scenarios of mural plaster affected by salt damage, enabling a deeper investigation into the mechanisms by which salts inflict microscopic damage to murals. (2) Explores the absorption mechanisms and characteristic spectral bands of the Electrical Conductivity (EC) values measured after the phosphate erosion of mural plaster. By integrating the optimal spectral indices, a univariate linear regression model is constructed, providing a basis for the rapid quantitative measurement of electrical conductivity in murals. (3) By comparing the accuracy of the Phosphate Simple Ratio (PSR) and Phosphate Normalized Difference Index (PNDI) spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study’s findings include: (1) Surfaces of samples deteriorated by phosphate erosion formed numerous irregularly shaped crystal clusters, exhibiting uneven characteristics. (2) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (Q2) of 0.728. (3) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural electrical conductivity with a determination coefficient (Q2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals.

Electrical conductivity of Ni ferrite nanofluids: An experimental study on the effects of temperature, volume fraction, and base fluid

This study investigates the electrical conductivity of NiFe2O4 nanofluids in water and ethylene glycol (EG) as base fluids, aiming to understand how varying volume fractions (φ= 0 %, 0.1 %, 0.25 %, 0.45 %, 0.7 %, and 1 %) and temperatures (20–70°C) influence electrical conductivity. NiFe2O4 nanoparticles were synthesized using the chemical co-precipitation method and characterized through X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), and Energy Dispersive Spectroscopy (EDS). The results revealed that at 70°C, the electrical conductivity of NiFe2O4-water nanofluid increased by 1100 % within the volume fraction range of 0–1 %. For NiFe2O4-EG nanofluid, the increase in electrical conductivity was even more significant, reaching 1235 % within the same volume fraction range. Similarly, at a 1 % volume fraction within the temperature range of 20–70°C, the electrical conductivity of NiFe2O4-water nanofluid increased by 136 %, while for NiFe2O4-EG nanofluid, it was 370 %. These findings indicate that both temperature and volume fraction significantly enhance the electrical conductivity of the nanofluids, with a more pronounced effect observed in the NiFe2O4-EG nanofluid compared to the NiFe2O4-water nanofluid. The study validated Shen et al.'s model for electrical conductivity in nanofluids, contrasting with Maxwell's model. The novelty of this work lies in the comprehensive analysis of the electrical conductivity behavior of these nanofluids, which has not been extensively reported in the literature. These findings have potential applications in heat transfer enhancement and magnetic-targeted drug delivery.

Temperature and composition dependence modeling of viscosity and electrical conductivity of low-activity waste glass melts

The development of models that accurately relate the properties of a glass melt to its temperature and composition is important for glass formulation, melter control, and modeling the melt flow, refractory corrosion, and production rate. Using a database consisting of more than 4,000 data points measured between 900 °C and 1250 °C for over 600 unique low-activity waste glass compositions, we developed models for the melt viscosity and electrical conductivity. Models based on the Gaussian process regression approach outperformed models based on the Vogel–Fulcher–Tammann equation according to four standard metrics and yielded reliable prediction intervals. The models found primarily linear effects between properties and individual components, except for the effect of the Na2O mass fraction on the electrical conductivity. The effects were found to be consistent with current theories on physical processes involved with those properties.

Study on the EC prediction of cracked soda saline-alkali soil based on texture analysis of high-resolution images from ground-based observation and machine learning methods

The rapid and accurate measurement of soil salinity is essential for assessing the level of soil salinization. In natural conditions, the surface of cohesive soda saline-alkali soil experiences shrinkage and cracking phenomena during water evaporation. The propagation and development of these desiccation cracks can be effectively described using texture features due to their statistical randomness. This study aims to establish a relationship between electrical conductivity (EC) values and texture features derived from both gray-level co-occurrence matrix (GLCM) and wavelet decomposition, and to develop a prediction model for EC accordingly. To achieve these objectives, crack images on the surface of 200 soil samples with varying levels of salinity were obtained in the Songnen Plain field. GLCM was computed and wavelet decomposition was performed to extract texture features in different directions and scales. The results demonstrate a significant correlation between texture features and soil EC values. Among the various texture features, 12 GLCM texture features with a grayscale level of 2 and a step size of 1 pixel, and wavelet texture features derived from a 4th level orthogonal decomposition based on the coiflet-1 function were selected as the optimal parameters for establishing and comparing the predictive effects of two machine learning models on soil EC values. In comparison to the testing results of the BP neural network model (R2=0.83, RPD=1.64, RMSE=0.32 dS/m, MAE=0.18 dS/m), the random forest model exhibited higher accuracy and stability (R2=0.95, RPD=3.48, RMSE=0.21 dS/m, MAE=0.07 dS/m), indicating that although both machine learning models demonstrated rapid and nondestructive capabilities, the random forest method was better suited for EC prediction in soda saline-alkali soil due to its superior accuracy.