Conductivity Distribution Research Articles

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.

Optimization of microwave-produced coal-derived char insulation foam using response surface methodology

A new char-based insulation foam (CIF) is developed using coal-derived pyrolysis char (PC) and lignin, a paper and pulp industry byproduct. In this study, CIF is prepared by adopting the microwave method and Response Surface Methodology (RSM) is utilized to optimize predictions about the properties such as thermal conductivity, foam texture, compressive strength, and pore distribution of CIF. The three independent variables including percent char content, lignin/char ratio, and foaming agent/polyol ratio are considered in the RSM methodology. The face-centered central composite design is selected to obtain 17 test runs. Optimization of the predicted responses is performed to find the recipe considering a composite desirability function. Confirmatory test results show agreement between the predicted and observed values, with the percent error ranging from 0.00 % to 11.35 %, thereby confirming the accuracy of the prediction equations. The optimized CIF exhibits a thermal conductivity of 0.0633 W/m.K and a compressive strength of 695 kPa making it comparable to or better than traditional building insulation products such as fiber insulation, polymer composite, particle board, and ceramic glass foam. Therefore, this new CIF has the potential to be used as an energy-saving insulation material for building envelopes.

Shape-persistent ladder molecules exhibit nanogap-independent conductance in single-molecule junctions.

Molecular electronic devices require precise control over the flow of current in single molecules. However, the electron transport properties of single molecules critically depend on dynamic molecular conformations in nanoscale junctions. Here we report a unique strategy for controlling molecular conductance using shape-persistent molecules. Chemically diverse, charged ladder molecules, synthesized via a one-pot multicomponent ladderization strategy, show a molecular conductance (d[log(G/G0)]/dx ≈ -0.1 nm-1) that is nearly independent of junction displacement, in stark contrast to the nanogap-dependent conductance (d[log(G/G0)]/dx ≈ -7 nm-1) observed for non-ladder analogues. Ladder molecules show an unusually narrow distribution of molecular conductance during dynamic junction displacement, which is attributed to the shape-persistent backbone and restricted rotation of terminal anchor groups. These principles are further extended to a butterfly-like molecule, thereby demonstrating the strategy's generality for achieving gap-independent conductance. Overall, our work provides important avenues for controlling molecular conductance using shape-persistent molecules.

Characterizing preferential infiltration of loess using geostatistical electrical resistivity tomography

Preferential infiltration is prevalent in loess and is pivotal in disasters such as landslides. However, the inherent multi-scale heterogeneity of loess makes traditional in-situ monitoring techniques challenging for characterizing the preferential infiltration process. This study employed the Geostatistical Electrical Resistivity Tomography (GERT) to examine the spatial distribution of mass water content (ωs) in loess strata to understand the preferential infiltration processes in loess. This study improved stimulus-response data quality and used GERT to characterize the spatial-temporal distribution of electrical conductivity (σ) and estimate ωs through the σ-ωs relationship. This study indicates that the surface loess layer has higher ωs than deeper layers, rapidly declining as depth increases. Under preferential infiltration, early-stage water forms a spherical saturated zone, transforming into an ellipsoid as it descends. Using equivalent homogeneous parameters as prior information effectively improved σ estimation. This study found a 15.9% error in the total change in water content based on the GERT survey compared to the known amount of injected water. It explores the possible impacts of the uncertainty of the unknown relationship between σ and ωs, its spatial variability, and the accuracy of the survey on the error. The formation of an electric double-layer structure within the loess as the saturation increases, which reduces the sensitivity of GERT to changes in water content, is likely another cause. Finally, these areas are essential for advancing future studies on applying geophysical tools to accurately estimate water distributions in loess formations.

Nonlinear thermal-mechanical coupled isogeometric analysis for GPLs reinforced functionally graded porous plates

This work presents an isogeometric model for thermal-mechanical coupled analysis of geometrically nonlinear responses of graphene platelets reinforced functionally graded porous (GPLs-FGP) plates. The closed-cell Gaussian random field (GRF) scheme, Halpin-Tsai micromechanics model and rule of mixture are utilized to determine the material properties of GPLs-FGP plates with three types of porosity distribution and GPLs dispersion pattern. Unlike previous studies, the influences of GPLs and porosity on the thermal conductivity of GPLs-FGP plates are considered simultaneously. A geometrically nonlinear isogeometric analysis (IGA) model is developed base on a four-variable refined plate theory (RPT), using the von Kármán nonlinear theory, Hamilton’s principle and Total Lagrangian (TL) incremental scheme. After ensuring the accuracy and reliability of the present model through comparison with several examples, the thermal conductivity, temperature distribution, geometrically nonlinear static bending and transient responses of the plate, taking into account the temperature effects of porosity and graphene under thermal-mechanical loads are investigated. Numerical results show that porosity exerts influences on the thermal conductivity and temperature distribution of GPLs-FGP plates, which subsequently impact their geometrically nonlinear thermal-mechanical coupled responses. The results of this study are helpful for designing and manufacturing GPLs-FGP structures with improved performance.

Graphene/MXene/Cellulose cellulosic paper-based flexible bifunctional sensors utilizing molecular bridge strategy with tunable piezoresistive effect for Temperature-Pressure sensing

Wearable technology is changing the way we interact with the digital world. Wearable systems can enable a smarter lifestyle when connected to other smart devices. This work reports a bifunctional sensor prepared from cellulosic paper as substrate for response to both temperature and pressure. The introduction of CMC-Na improves the dispersion of the binary hybrid conductive fillers in the deionized water. Meanwhile, relying on the intermolecular forces of both hydrogen bonding and cation-π interaction, the interfacial interactions of the binary hybrid conductive fillers are strengthened, which is anticipated to create a stable and homogenous conductive layer. And the concept of “effective conductive fillers distribution density” is introduced to explain and analyze the pressure sensing mechanism. The reported sensor has a pressure sensitivity of −1.6189 %/kPa, a sensing range of 370 kPa, and a response recovery time of 37.5 ms/37.5 ms. In addition to its response to mechanical force, the sensor has a detecting range of 23–58 °C, a response recovery time of 325 ms/1000 ms, and a temperature coefficient of resistance (TCR) of −1.77 %/°C. The potential applications of this sensor demonstrate the excellent gesture tracking characteristics, multi-point tactile sensing capability, and dual stimulus–response of the sensor.

Groundwater flow paths using combined self-potential, electrical resistivity, and induced polarization signals

SUMMARY The dam of Lampy (Black Mountain, Aude, France) is considered as one of the oldest dams in France. A geophysical survey is performed to better understand the pattern of groundwater flow downstream of this dam in the granitic substratum. Induced polarization is first used to image both electrical conductivity and normalized chargeability. Eight core samples of granite from this site are measured and analysed in the laboratory. Their electrical conductivity and normalized chargeability are expressed as a function of the porosity and cation exchange capacity (CEC). The field data and the petrophysical results are used to image the water content, the CEC and the permeability distribution of the substratum. Then, self-potential is used as a complementary passive geophysical technique, which, in absence of metallic bodies, is directly sensitive to groundwater flow through the so-called streaming potential effect. Indeed, the excess of electrical charges in the vicinity of the solid grains, in the so-called double layer, is dragged by the ground water flow generating in turn an electrical (streaming) current and therefore an electrical field. A map of the resulting self-potential signals is done over the area covered by the induced polarization profiles. This map shows a large positive anomaly with an amplitude of ∼80 mV possibly associated with upwelling groundwater in an area where the soil is water-saturated. A groundwater flow simulation is performed to model this anomaly. This is done in two steps. A preliminary groundwater flow model is built using the permeability and water content distributions obtained from the induced polarization data. Then, this groundwater flow model is updated using the information contained in the self-potential data including the electrical conductivity distribution obtained through resistivity tomography. The algorithm for the inversion of the self-potential data is validated through a 2-D numerical test. This analysis yields a groundwater flow model with the flow being focused through a high permeability zone. This study shows how three geoelectrical methods (self-potential, induced polarization and electrical resistivity) can be efficiently combined to image groundwater flow in the vicinity of a dam.

Transient characteristics of ultra-high frequency adjustable multi-pulse gas tungsten welding arc

In response to the shortcomings of traditional Gas Tungsten Arc Welding (GTAW), such as low penetration, slow welding speed, and low production efficiency, an Ultra-High-Frequency Adjustable Multi-Pulse Gas Tungsten Arc Welding (UFMP-GTAW) process is proposed. This process more effectively concentrates the temperature distribution of the welding arc. To characterize the arc morphology and its dynamic changes, high-speed photography captured ten images of the UFMP-GTAW welding arc within one cycle. The arc image processing algorithm proposed in the paper was used to study the arc's variation characteristics over one cycle. This algorithm includes binarization, arc segmentation, and edge detection to obtain arc morphology parameters. The arc symmetry algorithm is used to symmetrize the arc images for Abel inversion, resulting in the emission coefficient distribution. Further calculations provide the temperature distribution, electrical conductivity distribution, and current density distribution of the arc. Analysis reveals that changes in arc temperature, electrical conductivity, and current density lag behind changes in current. The temperature of the arc spreads from the central axis to the surrounding arc space. When high-frequency current variations occur, the heat input generated by the current increase takes time to transfer to the entire arc space. Conversely, if the current suddenly decreases, the heat input rapidly diminishes. However, because heat transfer takes time, the arc space remains relatively stable for a short period, making the ultra-high-frequency arc form more stable.

DEIT-Based Bone Position and Orientation Estimation for Robotic Support in Total Knee Arthroplasty-A Computational Feasibility Study.

Total knee arthroplasty (TKA) is a well-established and successful treatment option for patients with end-stage osteoarthritis of the knee, providing high patient satisfaction. Robotic systems have been widely adopted to perform TKA in orthopaedic centres. The exact spatial positions of the femur and tibia are usually determined through pinned trackers, providing the surgeon with an exact illustration of the axis of the lower limb. The drilling of holes required for mounting the trackers creates weak spots, causing adverse events such as bone fracture. In the presented computational feasibility study, time differential electrical impedance tomography is used to locate the femur positions, thereby the difference in conductivity distribution between two distinct states s0 and s1 of the measured object is reconstructed. The overall approach was tested by simulating five different configurations of thigh shape and considered tissue conductivity distributions. For the cylinder models used for verification and reference, the reconstructed position deviated by about ≈1 mm from the actual bone centre. In case of models mimicking a realistic cross section of the femur position deviated between 7.9 mm 24.8 mm. For all models, the bone axis was off by about φ=1.50° from its actual position.

Measurement and evaluation of the flashover voltage on polluted cap and pin insulator: An experimental and theoretical study

This study investigates the flashover voltage on high voltage insulators in polluted environments, employing a combination of experimental and theoretical approaches. Utilizing Analysis of Variance (ANOVA) and the Finite Element Method (FEM), the research aims to offer a thorough comprehension of the phenomenon. The experimental aspect of the study involves subjecting insulators to polluted environments, replicating real-world scenarios, and measuring the resulting flashover voltages. This experimental study focuses on investigating flashover pollution, where artificial pollution is introduced into an experimental model. An observational approach is employed to assess the impact of conductivity and pollution distribution on the 1512 L cap and pin insulator, as well as its proposed experimental model. Complementing the experimental approach, the theoretical aspect of the study employs the FEM method to simulate and model the insulator's behaviour under pollution. This computational technique enables a detailed analysis of the electrical field distribution, surface potential, and stress repartition across the surface of the insulator. Simulations further explore the impact of pollution on flashover voltage and leakage current. The FEM results are then compared with experimental findings to validate the model's accuracy and reliability. By analyzing the collected data, the ANOVA technique is applied to identify significant differences in flashover voltage under diverse pollution levels. This statistical approach allows for the determination of the most influential factors affecting insulator performance. This research offers valuable insights into the influence of flashover voltage on high-voltage insulators in polluted environments, contributing to the development of more robust and efficient insulation systems. The combination of ANOVA and FEM methods provides a robust framework for understanding and predicting insulator performance, ultimately benefiting the power industry and promoting energy sustainability.

Decomposition and fragmentation of conventional and biobased plastic wastes in simulated and real aquatic systems

AbstractPlastics play a crucial role in our daily lives. The challenge, however, is that they become waste and contribute to a global environmental problem, increasing concerns about pollution and the urgent need to protect the environment. The accumulation and fragmentation of plastic waste, especially micro- and nanoplastics in aquatic systems, poses a significant threat to ecosystems and human health. In this study, the decomposition and fragmentation processes of conventional and biobased plastic waste in simulated water bodies (waters with different pH values) and in real water systems (tap water and seawater) are investigated over a period of one and six months. Three types of plastic were examined: thermoplastic polyethylene terephthalate and thermoset melamine etherified resin in the form of nonwovens and biobased polylactic acid (PLA) in the form of foils. Such a comprehensive study involving these three types of plastics and the methodology for tracking degradation in water bodies has not been conducted before, which underlines the novelty of the present work. After aging of the plastics, both the solid fraction and the leachate in the liquid phase were carefully examined. The parameters studied include mass loss, structural changes and alterations in functional groups observed in the aged plastics. Post-exposure assessment of the fragmented pieces includes quantification of the microplastic, microscopic observations and confirmation of composition by in situ Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy. The leachate analysis includes pH, conductivity, turbidity, total carbon and microplastic size distribution. The results highlight the importance of plastic waste morphology and the minor degradation of biobased PLA and show that microfibers contribute to increased fragmentation in all aquatic systems and leave a significant ecological footprint. This study underlines the crucial importance of post-consumer plastic waste management and provides valuable insights into strategies for environmental protection. It also addresses the pressing issue of plastic pollution and provides evidence-based measures to mitigate its environmental impact. Graphical abstract

Investigating of Carbon Agent Distribution Using Long-Chain Alkyl in Grafted Binder for High-Capacity SiOx/Graphite Anode

A small amount of the conductive carbon agent in lithium-ion battery (LIB) formulation has a significantly impact on the rate capability performance of battery but the distribution problem of the carbon conductive material is issued in the electrode slurry due to its hydrophobic and inert characteristics. To overcome this problem, the use of dispersant agent was proposed in the electrode fabrication process.In this study, the grafting of adhesive acrylamide monomer (AM) and a long-chain alkyl monomer onto carboxymethyl cellulose (CMC) is applied to binder to improve the conductive agent distribution in a high-capacity SiOx/C anode. The long alkyl chain in the monomer of the graft copolymer binder exhibits good dispersibility of the carbon agent and improved conductive matrix compared to the binder with no alkyl monomer. This may be explained by CH-π integration between the alkyl monomer in graft polymer and carbon agent. The high-capacity SiOx/C anode with the long-alkyl chain introduced graft binder is examined in the slurry level with rheology and dispersion stability, in the electrode sheet level with electric resistance, peel-off, and coin-cell level with various electrochemical performance, charge/discharge performance. Keywords: Silicon oxide; Graphite; Carbon agent distribution, long-chain alkyl monomer; Acrylamide; Graft copolymer Figure 1

Microstructural Analysis of Lithium-Ion Battery Cathode Carbon-Binder Domain across Processing Conditions

Improvement of lithium-ion battery (LIB) energy density and rate capability is necessary to increase adoption of electric vehicles (EVs) and curb transportation emissions. The cathode structure must support facile ion and electron transport, enabling rapid charging while maintaining near-theoretical capacity. Much investigation focuses on active material design for fast-charging cathodes, [1] but less is understood about the effect of electrode processing on carbon-binder domain (CBD) structure and its impact on rate capability. As conductive carbon facilitates electron transfer, slow Li+ diffusion through cathode micropores is rate-limiting and prevents full active material utilization at high charge rates. [2,3] However, efforts to improve Li+ diffusion, such as increasing porosity, introduce charge-transfer limitations. [4] Thus, cathode processing conditions and resulting microstructure must be carefully designed to optimize for both electronic and ionic conductivity. For example, an increase in coating shear rate and decrease in slurry solid content improves long-range electronic pathways [5,6] and calendaring strengthens active material-carbon contacts to reduce charge-transfer resistance. [2] However, the impact of processing variations on ionic transport pathways, which can be described by CBD porosity and tortuosity, is not well understood. Further, because the CBD is traditionally characterized as a single phase, the distinct influences of insulating binder and conductive carbon distributions on structure and performance are poorly defined.In this talk, I will discuss how electrode coating conditions impact pathways available to Li+ and electrons within the CBD with the goal of identifying optimal microstructures for efficient transport. We fabricate cathodes with varying solid content, coating shear rate, and degree of calendaring to explore disparate CBD microstructures. We use electrochemical impedance spectroscopy (EIS) with a symmetric cell & blocking electrolyte to determine cathode resistivity and tortuosity and plasma focused ion beam scanning electron microscopy (PFIB-SEM) to examine CBD morphology over a representative cathode volume (~80 x 80 x 50 µm3). We then use small-angle neutron scattering (SANS) with solvent contrast variation to distinguish between binder and carbon distributions and determine the surface area of each component within the CBD. Relationships between processing conditions, structural variations, and rate capability will be discussed, informing the design of the CBD for facile transport and improved fast charging performance. Further, reducing ionic transport limitations through intentional design of LIB cathodes will enable thicker electrodes to increase cell energy density.

Plasma control for the step prototype power plant

In 2019 the UK launched the Spherical Tokamak for Energy Production (STEP) programme to design and build a prototype electricity producing nuclear fusion power plant, aiming to start operation around 2040. The plant should lay the foundation for the development of commercial nuclear fusion power plants. The design is based on the spherical tokamak principle, which opens a route to high pressure, steady state, operation. While facilitating steady state operation, the spherical design introduces some specific plasma control challenges: (i) All plasma current during the burn phase should to be generated through non-inductive means, dominated by bootstrap current. This leads to operation at high normalised plasma pressure βN with high plasma elongation, which in turn imposes effective active stabilisation of the vertical plasma position. (ii) The tight aspect ratio means very limited space for a central solenoid, imposing that even the current ramp up must be non-inductively generated. (iii) The compact design leads to extreme heat loads on plasma facing components. A double null design has been chosen to spread this load, putting strict demands on the control of the unstable vertical plasma position. (iv) The heat pulses associated with unmitigated ELMs are unlikely to be acceptable imposing ELM free operation or active ELM control. (v) To reduce and spread heat loads, core and divertor radiation and momentum loss has to be controlled, aiming to operate with simultaneously detached upper and lower divertors. (vi) High pressure operation is likely to require active resistive wall mode (RWM) stabilisation. (vii) The conductivity distribution in structures near the plasma must be carefully selected to reduce the growth rates for the vertical instability and the RWM without damping the penetration of the of magnetic fields from active control coils too much. This article describes the initial work carried out to develop a STEP plasma control system.

Dynamic behaviour and energy dissipation of offline air pockets in transient pipe flows

ABSTRACT The traditional one-dimensional (1D) model often fails to accurately predict the dynamic pressure response of large offline air pockets during transients, due to a lack of comprehensive understanding of the underlying mechanisms of transient interaction with offline air pockets. This study investigated dynamic behavior and energy dissipation of offline air pockets through experiments and numerical models. Experimental pressure responses were predicted using a 1D-3D coupling model, which presented superior performance compared to traditional 1D model. The 1D-3D coupling model was further utilized to investigate the air-water interface, internal energy and turbulence distribution of offline air pockets. The results reveal that the stability of the air-water interface depends on the decelerating distance of the jet in offline tube, explaining the instability phenomenon with lower water levels observed in experimental snapshots. The internal energy pattern suggests different energy dissipation mechanisms compared to the inline air pockets. The distribution of turbulence intensity and effective thermal conductivity indicates significant additional energy dissipation at the inlet and outlet of offline neck. By incorporating a minor head loss term for offline neck into traditional 1D model, the pressure response aligns well with the results obtained from 1D-3D coupling model under different flow rates and air volumes.

Piezoresistive nanocomposite sensing using electrical impedance tomography and machine learning

Strain measurements have traditionally been accomplished by connecting many gauges to crucial spots on structures. Installing strain gauges can be costly and limited in providing ‎comprehensive data for large surface areas or complex structures, posing significant challenges. This work explores the use of piezoresistive nanocomposite sensors as a cost-effective and informative solution for wide surface area strain detection. Piezoresistive sensors are created using two methodologies: film-coated and pellet-fed 3D printing technique (PF3DP), with variable gauge factors achieved by using 6 and 10 wt% multi-wall carbon nanotubes (MWCNTs), respectively. An electrical impedance tomography (EIT) setup is utilized to analyze electrical changes with respect to strain and predict the conductivity distribution across the sensor’s surface. This study employs two efficient machine learning (ML) techniques, artificial neural networks (ANN) and convolutional neural networks (CNN), to analyze and compare image reconstruction resolution at different loads. The machine learning models are designed to reconstruct images quickly and reliably. The study demonstrates that the hybrid-ML model, which combines ANN and CNN models, achieves high-resolution image construction with minimal errors compared to outcomes obtained through EIT for the experimental measurements. Sensors created using 3D printing provide higher image resolution and data stability than film-coated sensors. This technology has the potential for accurate strain sensing and measuring the structural health of complex surfaces.

Complex-valued multi-frequency electrical impedance tomography based on deep neural networks

The utilization of multi-frequency electrical impedance tomography (mfEIT), a non-invasive imaging technique, allows for the visualization of the conductivity distribution in biological tissues across different frequencies. However, the analysis of phase angle information within complex impedance remains a challenge, as most existing deep learning-based mfEIT algorithms are limited to real number processing. To mitigate this limitation, this study proposes a comlex reconstruction method which is inspired by the idea of combining deep learning with traditional reconstruction algorithm. It uses a spare Bayesian learning algorithm in the preprocessing stage that can perform complex arithmetic operations, and fully learns and makes use of the correlation between the real and imaginary parts to reconstruc the distribution of complex-valued conductivity in the measurement area. After that, an altered UNet network is used to further optimize the pre-reconstruction outcomes. The experimental outcomes validate the efficacy of the proposed algorithm in accurately reconstructing the complex-valued conductivity distributions of diverse biological tissues, such as potato and pig kidney, across different frequencies. Furthermore, the algorithm exhibits exceptional performance in mitigating the presence of image artifacts during the reconstruction process.

Detection of indentation damage in carbon fiber/epoxy composites via EIT during the application of bending loads

Electrical impedance tomography (EIT) is a method of spatially mapping the conductivity distribution of a domain and has been studied as a potential embedded sensing or nondestructive evaluation (NDE) tool. An often touted advantage of EIT is that it can be used in-situ; that is, because the method only requires the application of unobtrusive electrodes, it can conceivably be used while the component or structure is in operation. This material-as-the-sensor philosophy strongly aligns with key components of the NDE 4.0 vision such as the realization of intelligent cyber–physical systems (CPS) and digital twins. To date, however, the claim of in-situ sensing via EIT has not been significantly substantiated. This is problematic because operational loads induce strains that often change the conductivity of the material. Establishing that EIT can detect damage-induced conductivity changes through the presence of unrelated strain-induced conductivity changes is therefore important. To that end, we herein study the application of EIT for detecting indentation damage in a carbon fiber/epoxy composite as the composite is loaded in a four-point bend. It was found that the bending load changes the contact impedance of the electrodes, which resulted in poor EIT images when solving the EIT inverse problem with the ℓ2-norm on the error term. Using the ℓ1-norm on the error term, solved via the primal–dual interior point method (PDIPM), significantly improved image quality. Image quality was even further improved through the use of a mixed prior for regularization, and EIT images were compared to thermography with good agreement. These results show that EIT can indeed detect damage through the presence of an applied load, but care must be taken to account for factors such as outlier data arising from electrode degradation and changing contact impedance. Use of the ℓ1-norm on the error term is therefore highly recommended for in-situ imaging via EIT.

Flexible-Center Hat Complete Electrode Model for EEG Forward Problem.

This study aims to develop a more realistic electrode model by incorporating the non-uniform distribution of electrode contact conductance (ECC) and the shunting effects, to accurately solve EEG forward problem (FP). Firstly, a hat function is introduced to construct a more realistic hat-shaped distribution (HD) for ECC. Secondly, this hat function is modified by applying two parameters - offset ratio and offset direction - to account for the variability in ECC's center and to develop the flexible-center HD (FCHD). Finally, by integrating this FCHD into the complete electrode model (CEM) with the shunting effects, a novel flexible-center hat complete electrode model (FCH-CEM) is proposed and used to solve FP. Simulation experiments using a realistic head model demonstrate the necessity of FCH-CEM and its potential to improve the accuracy of the FP solution compared to current models, i.e., the point electrode model (PEM) and CEM. And compared to PEM, it has better performance under coarse mesh conditions (2 mm). Further experiments indicate the significance of considering shunting effects, as ignoring them results in larger errors than coarse mesh when the average contact conductance is large (101S/m2). The proposed FCH-CEM has better accuracy and performance than PEM and complements CEM in finer meshes, making it necessary for coarse meshes. This study proposes a novel model that enhances electrode modeling and FP accuracy, and provides new ideas and methods for future research.

ERT image reconstruction using marker region segmentation method

Abstract Inspired by image region segmentation method, a marking region segmentation iterative method is proposed to reconstruct sparse binary images of electrical resistance tomography. The grayscale matrix of the iteration process is mapped to another linear space for segmentation processing, adding the watershed thresholding of marking regions. In the iteration process for estimating conductivity distribution, the target regions are separated to avoid excessive segmentation effects. By applying this method in conjunction with the Landweber iterative model to solve the inverse problem of resistivity tomography imaging, more accurate binary images can be obtained, and the method exhibits superior convergence properties in comparison to the Landweber algorithm. To verify the reconstruction effectiveness of LW-TDIS method, numerical simulations and static experiments are conducted for comparison with three other methods. The results demonstrate that the proposed method effectively reduces reconstruction artifacts, improves reconstruction quality, and achieves better reconstruction performance.