Conductivity Distribution Research Articles

Microplastic pollution unveiled: the consequences of small unregulated dumping in villages, spanning from soil to water.

Microplastic contamination in soil ecosystems is a major environmental concern in the world. The current study aims to explore the extent of microplastic pollution in unregulated village dumpsites in India, focusing on the movement of these pollutants from soil to aquatic environments. Soil samples from eight distinct sites (A to H) in six villages were analyzed for various properties, including pH, bulk density, porosity, water retention capacity, hydraulic conductivity, and particle size distribution. The attenuated total reflection-Fourier-transform infrared spectroscopy (ATR-FTIR) method was used to identify prevalent plastic types. The research classifies microplastics by their shape and color, identifying a wide range of particles such as sheets, fibers, foams, fragments, and films. The study also examines the presence and concentration of microplastics in both soil and sediment samples. It was found that PE and PP microplastics are significantly present across different size fractions. Sample A contains a variety of items in the 1-5mm size range, mainly PE, while the 0.3-1mm fraction is largely PP. Samples B to H are mostly composed of PE microplastics in different forms. Sample F is unique with a mix of PE, EPS, and a higher amount of red and blue foam particles in the 0.3-1mm fraction. Microplastics were quantified using stereomicroscopy, revealing concentrations between 80 and 840 numbers per kilogram in soil and 20 to 60 numbers per kilogram in sediments. The findings emphasize the widespread nature of microplastic pollution across ecosystems and the importance of developing effective strategies for monitoring and mitigating their impact on environmental health and human well-being.

The proposal and calculation method of the annular domain weight function for the electromagnetic flowmeter under annular conductivity distribution

Researching on the characteristic of weight function distribution under non-uniform conductivity distribution is of significant importance for expanding the engineering applications of electromagnetic flowmeter. In this paper, the characteristic of weight function distribution is firstly explored for annular conductivity distribution where the center and outer ring are both conductive. The research results indicate an important finding that the average value of the weight function within any small annular domain of the center or outer ring remains constant as the radial position changes. Therefore, the concept of ‘annular domain weight function’ is then innovatively proposed. Furthermore, the relationship is studied between the ratio of the annular domain weight function of the center and outer ring and the annular feature parameters. Ultimately, the correlations are obtained for calculating the annular domain weight functions of the center and outer ring.

Quantifying aquifer heterogeneity using superparamagnetic DNA particles

Identifying and determining hydraulic parameters of physically heterogeneous aquifers is pivotal for flow field analysis, contaminant migration and risk assessment. In this research, we applied a novel uniquely sequenced DNA tagged superparamagnetic silica microparticles (SiDNAmag) to quantify hydraulic parameters and associated uncertainties of a heterogeneous sand tank. In the sand tank with lens shaped heterogeneity, we conducted three sets of multi – point injection experiments in unconsolidated (1) homogeneous (zone 0), (2) heterogeneous with a no-conductivity-zone (zone 1), and (3) heterogeneous with a high-conductive-zone (zone 2). From the breakthrough curves (BTC), we estimated the parameters distributions of hydraulic conductivity (k), effective porosity (ne), longitudinal dispersivity (αL), transverse vertical (αTV), and transverse horizontal dispersivities (αTH) applying Monte Carlo simulation approach for BTC fitting. The estimated parameters and associated uncertainties for each of the heterogeneous sections were further statistically compared (distribution non-specific Mann Whitney U test) these parameter distributions with parameter distributions estimated from the conservative salt tracer. While the time of arrival and time to peak concentration of SiDNAmag and salt in effluent were comparable, peak concentration of SiDNAmag was 1–3 log reduced as compared to the salt tracer due to first order kinetic attachment. Nonetheless, the parameters and associated uncertainty distributions (5 %–95 %) of K, ne, αL, αTV, and αTH, determined from SiDNAmag BTCs were statistically equivalent to the salt tracer in all three experiment systems. Through our experimental and modelling approach, our work demonstrated that in a coarse to very coarse grain sand medium, with lens shaped heterogeneity, the uniquely sequenced SiDNAmag were a promising tool to identify heterogeneity and determine hydraulic parameters and associated uncertainty distributions.

Identifying Subsurface Connectivity From Observations: Experimentation With Equifinality Defines Both Challenges and Pathways to Progress

ABSTRACTLinkages between landscapes and streams are increasingly described in terms of hydrological connectivity. The ability to effectively distinguish different patterns of water movement through catchments makes connectivity particularly interesting to both scientists and practical water managers. Hydrometric data (groundwater levels, soil moisture and streamflow) are often employed to infer the connection between the landscape and its drainage network. Such observational data, however, are insufficient to infer subsurface connectivity in humid settings with perennial stream flow, due to the risk of equifinality. To quantify how much subsurface flow patterns can differ and still be consistent (equifinal) with comprehensive observations of hillslope groundwater levels and stream runoff (the hydrometric data), this study used a modelling experiment based on a well‐characterised field site. Particle‐tracking simulations at different flow rates defined the water flow paths and transit times of two virtual hillslopes that differed profoundly in the vertical distribution of the saturated hydraulic conductivity. Even though the simulated weekly stream flows and groundwater levels were similar (i.e., the hillslopes were hydrometrically equifinal) particle velocities and water ages at specific locations along these hillslopes differed by orders of magnitude. Flow path lengths and catchment transit times varied up to several 100%. The hillslope‐ and stream‐based metrics used to describe connectivity also varied with stream flow rates. These results underline the need to recognise the risks for equifinality when inferring subsurface connectivity from hydrometric observations alone, even when those observations are comprehensive. The results also highlight the value of model simulations for quantifying the uncertainty in the inferred connectivity, targeting the best sampling locations/times to reduce this uncertainty with tracer data and better understanding the way connectivity influences stream chemistry.

Dynamic modeling of air–metal plasma mixture of single-turn coil with erosion at megaGauss magnetic field

Single-turn coil (STC) is a destructive pulsed magnet aiming at 100–300 T magnetic field. Due to the high discharge current, the conductor of STC is heated rapidly and undergoes melting and vaporization, leading to the generation of supersonic air–metal vapor mixed plasma jet and the magneto-fluid effect. In this study, the mixed plasma mass-transfer and fluid dynamic characteristics are modeled at megaGauss magnetic field, high temperature, high pressure, and supersonic conductor shock deformation. The collision integral method is employed to calculate the fluid transport properties. In addition, a boundary constraint model of fluid–structure interaction (FSI) compatible with both fluid wall boundary condition and plasma jet entrance condition and a model to simultaneously solve the thermal ionization and high electric field ionization of the mixed vapor are proposed. As the result, the distributions of plasma electrical conductivity, current density, electron, heavy particles, temperature, air body load, and velocity are derived. Especially, the region of highest electrical conductivity is not the air domain near the inner surface of the conductor with the highest electron density and the highest magnetic field, but the air domain near the outer surface of the conductor with the relatively higher electron density and lower magnetic field.

The Heat Transfer in Skin Tissues Under The General Two-Temperature Three-Phase-Lag Model of Heat Conduction with a Comparative Study

In this paper, a new and more general model of heat conduction that depends on the drift velocity due to thermomass motion assumption will be established and will be applied to the skin tissue. Four different heat conduction models will be incorporated into a unified equation of heat conduction: the Pennes, Vernotte-Cattaneo, dual-phase-lag of Tzou, and the general two-temperature three-phase-lag of Youssef. The governing partial differential equations of the general two-temperature three-phase-lag model of bioheat conduction will be implemented and solved directly in the domain of the Laplace transformation. The numerical solutions of the Laplace transform will be calculated by executing the Tzou iteration formula. The ramp-type heat on the surface of the skin tissue will be considered as thermal loading. The conductive and dynamic temperature increment reactions have been studied and discussed with different values of ramp-time heat, characteristic length, and drift velocity parameters. The novelty of this work is to introduce some comparisons of the four under-studied bioheat conduction models and show the differences between them in the figures. The numerical results show that the ramp-time heat, drift velocity, and characteristic length parameters have major impacts on the increment of both dynamical and conductive temperature distributions.

Imaging of permeability defect distribution by electromagnetic tomography with hybrid L1 normand nuclear normpenalty terms.

Electromagnetic tomography (EMT), with the advantages of being non-contact, non-invasiveness, low cost, simple structure, and fast imaging speed, is a multi-functional tomography technique based on boundary measurement voltages to image the conductivity distribution within the sensing field. EMT is widely used in industrial and biomedical fields. Currently, there are few studies on the application of EMT in magnetic permeability materials, which makes it difficult to obtain high-quality reconstructed images due to its own properties that lead to obvious attenuation of electromagnetic waves during propagation, as well as the ill-posed and ill-conditioned characteristics of EMT. In this paper, a multi-feature objective function integrating L2 normregularization, L1 normregularization, and low-rank normregularization is proposed to solve the challenge of magnetic permeability material imaging. This approach emphasizes the smoothness and sparsity. The split Bregman algorithm is introduced to efficiently solve the proposed objective function by decomposing the complex optimization problem into several simple sub-task iterative schemes. In addition, a nine-coil planar array electromagnetic sensor was developed and a flexible modular EMT system was constructed. We use correlation coefficient and error coefficient as indicators to evaluate the performance of the proposed image reconstruction algorithm. The effectiveness of the proposed method in improving the reconstruction accuracy and robustness is verified through numerical simulations and experiments.

Reconstruction of intra- and extra-neurite conductivity tensors via conductivity at Larmor frequency and DWI data patterns

The developed magnetic resonance electrical properties tomography (MREPT) techniques visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data. In biological tissues, electrical conductivity is influenced by ion concentrations and mobility. To visualize the anisotropic conductivity tensor of biological tissues, we use the Einstein–Smoluchowski equation, which links the diffusion coefficient to particle mobility. By assuming a correlation between ion mobility and water diffusivity, we aim to decompose the internal isotropic conductivity at Larmor frequency into anisotropic conductivity tensors within the intra- and extra-neurite compartments. The multi-compartment spherical mean technique (MC-SMT), utilizing both high and low b-value diffusion-weighted imaging (DWI) data, characterizes the diffusion of water molecules within and across the intra- and extra-neurite compartments by analyzing the microstructural intricacies and the foundational architectural arrangement of the brain’s tissues. By analyzing the relationships between the measured DWI data, the microscopic diffusion signal, and the fiber orientation distribution function, we predict the DWI data for the intra- and extra-neurite compartments using spherical harmonic decomposition. Using the predicted DWI data for the intra- and extra-neurite compartments, we develop a conductivity tensor imaging method that operates specifically within the separated compartments. Human brain experiments, involving four healthy volunteers and a brain tumor patient, were performed to assess and confirm the reliability of the proposed method.

On the Scaling of Transport Phenomena at a Monotonously Changing Hydraulic Conductivity Field

Monotonously stratified porous medium, where the layered medium changes its hydraulic conductivity with depth, is present in various systems like tilled soil and peat formation. In this study, the flow pattern within a monotonously stratified porous medium is explored by deriving a non-dimensional number, Fhp, from the macroscopic Darcian-based flow equation. The derived Fhp theoretically classifies the flow equation to be hyperbolic or parabolic, according to the hydraulic head gradient length scale, and the hydraulic conductivity slope and mean. This flow classification is explored numerically, while its effect on the transport is explored by Lagrangian particle tracking (LPT). The numerical simulations show the transition from hyperbolic to parabolic flow, which manifests in the LPT transition from advective to dispersive transport. This classification is also applied to an interpolation of tilled soil from the literature, showing that, indeed, there is a transition in the transport. These results indicate that in a monotonously stratified porous medium, very low conducting (impervious) formations may still allow unexpected contamination leakage, specifically for the parabolic case. This classification of the Fhp to the flow and transport pattern provides additional insight without solving the flow or transport equation only by knowing the hydraulic conductivity distribution.

Magnetic Induction Tomography for Brain Tissue Imaging Based on Conductivity Distribution for Parkinson’s Disease Diagnosis

Parkinson's disease is a prevalent neurodegenerative complication defined by the accumulation of alpha synuclein lewy bodies in the brain. Misdiagnosis results widespread of Parkinson’s disease because clinical diagnosis is challenging, underlining a need of a better detection technique, such as non-invasive magnetic induction tomography (MIT) technique. Non-invasive techniques for biological tissues imaging are becoming popular in biomedical engineering field. Therefore, MIT technology as a non-invasive technique has been encouraged in a medical field due to its advancement of technology in diagnosing diseases. The measurement parameters in MIT are passive electromagnetic properties (conductivity, permittivity, permeability) for biological tissue and the most dominant parameter in MIT is conductivity properties. It is uses a phase shift between a primary magnetic field and an induced field caused by a target object's conductivity. As a function of conductivity, the phase shift between the applied and secondary fields is expressed. Thus, the phase shift can be used to characterize the conductivity of a target object. The phase shift between the excitation and induced magnetic fields (EMF and IMF) reflects the change in conductivity in biological tissues. This paper focuses on the virtual simulation by using COMSOL Multi-physics for the design and development of MIT system that emphasizes on single channel magnetic induction tomography for biological tissue (bran tissue) imaging based on conductivity distribution for Parkinson’s disease diagnosis. The develop system employs the use of excitation coils to induce an electromagnetic field (e.m.f) in the brain tissue, which is then measured at the receiving side by sensors. The proposed system is capable of indicating Parkinson’s disease based on conductivity distribution. This method provides the valuable information of the brain abnormality based on differences of conductivities of normal brain and Parkinson’s disease brain tissues.

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.

Efficient ecological function analysis and multi-objective optimizations for an endoreversible simple air refrigerator cycle

Abstract Combining finite time thermodynamics and exergetic analysis, analogous to the definition of ecological efficient power for heat engines, this paper proposes a unified performance indicator for various cycles, exergy-based efficient ecological function (E ɛ ) which is defined as product of exergy-based ecological function and coefficient of performance, and introduces it into performance optimization of endoreversible simple air refrigerator cycle coupled to constant-temperature heat reservoirs. Relations among E ɛ , pressure ratio (π) and heat conductance distribution ratio (u) are derived by using numerical method. The cycle performance indicators which include cooling load (R), coefficient of performance (ɛ), and exergetic loss rate (E out/T 0) under the different maximum objective criteria are compared. Taking π as optimal variable, and taking R, ɛ, cooling load density (r), E ɛ and their combinations as optimization objectives, multi-objective optimizations, totally 15 optimization combinations, are performed by using NASG-II algorithm. The results demonstrate that, the maximum E ɛ criteria can better reflect the compromise among R, ɛ and E out/T 0. The Pareto solution sets are majorly distributed in 2.5–20 when quadru-objective optimizations are performed. The option selected by LINMAP decision-making method is closer to ideal solution when bi-objective optimization of ɛ and r is carried out.

Non-Invasive Bio-Impedance Imaging and Sensing for Medical Diagnostics and Industrial Applications

Bio-impedance-based imaging technique stands at the intersection of medical/industrial imaging techniques and is also known as electrical impedance tomography (EIT), imparting a non-invasive, value-powerful approach for imaging biological tissues and procedures. We begin by elucidating the essential standards underlying EIT, focusing on the size of impedance variations inside an object (plastic pipe) to reconstruct its inner conductivity distribution. Subsequently, we delve into the numerous programs of EIT across numerous fields and discuss recent experimental strategies and advancements aimed at mitigating these boundaries, such as novel electrode configurations, advanced reconstruction algorithms, and incorporation of complementary imaging modalities. Finally, we underscore the importance of experimental research in advancing the capabilities and packages of EIT as a valuable device in industrial monitoring, specifically in bedside tracking, process pipelines, tank monitoring and also used for other medical or industrial detection, even as additionally exploring its rising roles in commercial and environmental applications.

Impact of Multidirectional Pacing on Activation and Repolarization Parameters to Localize Ischemic Ventricular Tachycardia Circuits.

In ventricular tachycardia (VT), optimal substrate mapping strategies identifying arrhythmogenic sites are not established. This study sought to evaluate multidirectional pacing on the distribution of specific conduction and repolarization metrics to localize re-entrant VT sites in a porcine infarct model. Substrate maps were created in 13 pigs with chronic myocardial infarction using the Advisor HD Grid (Abbott) during right ventricular (RV), left ventricular, biventricular pacing (BIV), and sinus rhythm (SR). Critical VT sites of early-, mid-, and late-diastolic signals were delineated. Vulnerable sites to re-entry were defined as sites of latest activation timing within and post-QRS complex, largest activation and activation-recovery interval gradients. Distances between the 20 most vulnerable sites and diastolic VT points were measured, and identification of VT points was assessed using the area under the receiver-operating characteristic curve. A total of 34 VTs were mapped, and 48 sinus and pacing maps were obtained (10 BIV, 13 left ventricular, 13 RV, 12 SR). Late potential mapping in SR was taken as the established clinical standard for comparison. Latest activation time with BIV pacing provided the closest localization for VT isthmus (median 5.5mm; IQR: 7.15mm; P< 0.005). The gradient of activation-recovery interval using RV pacing had closest localization for VT exit and entrance (median 10.6mm; IQR: 5.0mm; P< 0.005 and 9.4mm; IQR: 8.0mm; P< 0.05). Global sensitivity and specificity analysis showed that gradient of activation-recovery interval in SR achieved the highest area under the receiver-operating characteristic curve, with similar results from the gradient of activation timing. Multidirectional pacing in combination with conduction and repolarization parameters enables better localization of VT diastolic critical sites vs SR late potentials.

Mapping and Assessment of Within-field Spatial Variability of Soil pH, Electrical Conductivity, and Particle Size Distribution to Delineate Management Zones

Mapping and Assessment of Within-field Spatial Variability of Soil pH, Electrical Conductivity, and Particle Size Distribution to Delineate Management Zones

Hybrid numerical 3D model of electromagnetic levitation of molten metal

This paper introduces a new and efficient methodology for the numerical modeling of electromagnetic levitation. The model requires transferring the time-varying geometry of the molten metal from the hydrodynamic submodel to the electromagnetic submodel. The developed methodology combines a geometry transfer of the conductivity distribution of the metal/air system, and the accompanying conditional reconstruction of the electromagnetic submodel. The proposed solution strategy enables a computationally efficient simulation of the coupled process accounting for any evolution of the molten metal geometry. The developed methodology can be generalized to any magnetohydrodynamic process with the presence of a free surface of liquid metal. The method was applied to the 3D modeling of a classic example of low-scale levitation melting of aluminum.

Coupling stress and transmissivity to define equivalent directional hydraulic conductivity of fractured rocks

A DFN (Discrete Fracture Network) modelling approach is developed to couple stresses with fracture transmissivities and to evaluate large scale rock mass hydraulic conductivity. The transmissivity-stress coupling relies on a negative exponential correlation between normal stress acting on a fracture and fracture transmissivity, bounded by residual and maximal apertures. The remote stresses and the local stress fluctuations induced by the fractures themselves are combined in a semi-analytical approach to compute the normal stress acting on each fracture of a DFN. Directional equivalent hydraulic conductivities are numerically computed in all directions from a spherical permeameter setup. The resulting properties are first a cloud of points, where each point defines a direction and an equivalent hydraulic conductivity. The distribution of equivalent hydraulic conductivities is analyzed to define mean values, preferential directions and anisotropy ratio. The entire workflow is developed in the numerical platform DFN.lab. The capacity of the method to investigate the impact of the in-situ stresses on the rock mass hydraulic conductivity is illustrated for fracturing and in-situ stress conditions similar to the conditions at the Forsmark site in Sweden. We find that the stress fluctuations induced by the fractures have a significant impact on the resulting hydraulic conductivity field. They limit the anisotropy ratio to values close to a factor of 3 while the transmissivity distribution is correlated to orientations and spans several orders of magnitude. Sensitivity analyses, performed by changing the parameters of the transmissivity-stress law, show quantitatively how the directional hydraulic conductivities are rather controlled by the orientations of the in-situ stresses or by the underlying connectivity structure of the DFN.

Operando Decoding Ion-Conductive Switch in Stimuli-Responsive Hydrogel by Nanodiamond-Based Quantum Sensing.

Thermal-responsive hydrogels are developed as ion-conductive switchs for energy storage devices, however, the molecule mechanism of switch on/off remains unclear. Here, poly(N-isopropylacrylamide-co-acrylamide) hydrogel is synthesized as a model material and nanodiamond (ND) based quantum sensing for phase change study is developed. First, micro-scale phase separation with cross-linked mesh structure after sol-gel transition is visualized in situ and water molecules are trapped by polymer chains and on a chemically "frozen" state. Then, the nano-scale inhomogeneous distributions of viscosity, thermal conductivity and ionic mobility in hydrogel at high temperature are observed by measuring the rotation, translation and zero-field splitting of NDs. Besides, the ionic mobility of hydrogel is found to be dependent not only on temperature but also on polymer concentration. These observations suggested that the physical "wall" induced by inhomogeneous phase separation at microscopic scale blocked the ion conduction pathways, providing a potential intrinsic explanation for ion migration shut-down of ionic hydrogels at high temperature.

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.

Sensitivity Matrix Update Method for Electrical Resistance Tomography Based on Error-Constrained Cross Fusion Residual Attention Network

Abstract Electrical resistance tomography (ERT) is an imaging technique of conductivity distribution. The image reconstruction algorithm based on the empty field sensitivity matrix is widely used because it provides an approximate solution to the complex ERT inverse problems. However, due to the soft field effect, the sensitivity matrix changes with the media distribution, leading to significant errors in image reconstruction. To address this issue, an error-constrained deep learning scheme for bubble flow, namely cross fusion residual attention network (CFRA-Net) is designed to update the sensitivity matrix during gas-liquid-solid fluidized bed operation. CFRA-Net employs a multi-head self-attention mechanism to enhance bubble features in boundary measurements, a multilevel cross fusion module to fuse empty field strength and boundary measurement information, and a novel serial-channel residual spatial attention block to boost the feature extraction capability of the model. A weighted loss function is designed to give more weight to the gas phase distribution region. Additionally, this paper establishes a dataset for gas-liquid-solid three-phase flow, considering background field conductivity variations. Validation and reliability of the proposed method are assessed through ablation, comparison, and noise experiments. The experimental results demonstrate that CFRA-Net achieves rapid updates of the sensitivity matrix, high imaging accuracy, and robust noise immunity.