Electrical Conductivity Distribution Research Articles

Predicting wetland soil properties using machine learning, geophysics, and soil measurement data

Abstract Purpose Machine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil properties based on geophysical measurements constrained by soil samples. Materials and methods A spatial distribution of soil apparent electrical conductivity (ECa) and laboratory measurements of SOM and SMC from twenty-two core samples were acquired at the St. Michael restored wetland near Defiance, Ohio. The correlations between ECa, SOM, and SMC were explored for predicting the spatial distribution of SOM and SMC. We used a Beta Variational AutoEncoder (β-VAE) approach to synthetically generate over 70,000 training data from the original twenty-two data from soil cores. The training data samples were taken from the latent space. The XGBoost algorithm was then trained on the β-VAE generated data and used to predict the spatial distribution of SOM and SMC at the site. We also validated the accuracy of the XGBoost predictions using an original holdout model validation technique. Results and discussions The generated synthetic data using the β-VAE include both soil attributes and ECa, which are larger and more diverse than the original training set with an absolute mean reconstructed error for SMC and SOM ranging from 0.018 to 0.022 and 0.026 to 0.041, respectively. This indicates that the β-VAE successfully generated a realistic synthetic dataset and overcame the technical barrier of using limited datasets. In addition, using generated data to expand the original training data helps the XGBoost model make more accurate predictions compared to training on the original data. The XGBoost prediction performance yielded average Lin’s concordance correlation coefficient (LCCC) values of 0.82 and 0.85 for SOM and SMC and a ratio of performance to deviation (RPD) values of 1.92 and 2.22 respectively, indicating a good performance. Conclusions This study validated the use of β-VAE to successfully generate synthetic wetland soil datasets with attributes of the original field data that can be effectively used to train the machine learning XGBoost model. The proposed framework offers an efficient solution for mapping the spatial variability of soil property in data-scarce wetland soil environments.

Maxwell–Wagner effect and second harmonic generation in gradient structures

AbstractFor the first time, we have registered, studied, and modeled the Maxwell–Wagner effect in structures with a smooth gradient of charge carriers’ mobility. The Maxwell–Wagner charge, formed after applying a DC voltage to the gradient structure at room temperature, is distributed over the entire region of the gradient. The experiments were performed with slides of a soda‐lime glass subjected to sodium‐to‐potassium ion exchange, where the ions concentration gradient provides gradient of conductivity while dielectric permittivity stays the same. The electric field generated by the Maxwell–Wagner charge is concentrated in potassium‐enriched less conductive regions of the slide and is sufficiently high to induce the second order optical nonlinearity in the initially isotropic glass. The effect causes over 100‐fold increase in a second harmonic signal relative to the signal from the surface of a virgin glass. This phenomenon in graded‐concentration structures is perspective for characterizing interfacial charges, geometry of the gradient regions, and spatial distribution of electrical conductivity in micro‐ and optoelectronic semiconductor structures.

Influence of Magnetic Polarization on Diagrams of Inductive and Electromagnetic Logs

Abstract ––We present the results of the study of the effect of induced magnetic polarization of clay beds under the influence of an external harmonic electromagnetic field (frequencies 70 and 875 kHz). A two-stage numerical modeling procedure is proposed. At the first stage we determine the effective relative magnetic permeability of a synthetic sample with inclusions of clay particles. In this case a 3D heterogeneous mesh sample is generated. Then we numerically model a spatial distribution of an electric field. The electromotive force (EMF) induced in the measuring coil is calculated from this distribution. Relative magnetic permeability is determined by comparison with EMF of homogeneous samples with different values of magnetic permeability. It has been found that during the electric field excitation by an alternating current coil, the effect of induced magnetic polarization appears in the sample with clay particles. Its manifestation is that the effective magnetic permeability becomes complex. At the second stage we calculate the EMF diagram of the three-coil logging probe in the macro-model ‘clay cap – reservoir’. Magnetic permeability of the clay cap is given by a complex value. In the generated logs, extremes appear in the vicinity of the bottom of the clay cap; they do not correspond to the distribution of electrical conductivity and magnetic permeability in the given model. They can be incorrectly interpreted when analyzing real logs into individual formations. Numerical modeling at all stages is performed by the Vector Finite Element Method on a consistent adaptive tetrahedral partition and the first-order Webb vector basis.

Directly depositing highly densified and uniform Cu-Cr pseudobinary alloy

Cu-Cr pseudobinary alloys have excellent electrical properties and are widely used in many industries. Laser additive manufacturing can effectively avoid the shortcomings of traditional methods for preparing Cu-Cr alloys. However, the high reflection of Cu powder to laser energy brings difficulties to industrial practice. The assembled Cu powder is used as raw material to absorb more laser energy through the outer layer of Cr powder, thereby improving the melting efficiency of the powder and the quality of the molten pool. Laser remelting of unmelted Cr powder can inhibit the uneven nucleation and growth of precipitated Cr, and improve the uniformity of electrical conductivity and element distribution. This study provides a new idea for the preparation of high-performance Cu alloys.

Magnetotelluric Observations in the Caspian Sea

Abstract —To study the geological structure in the search for raw hydrocarbons within the Volga delta and the Caspian Sea, two intersecting profiles of magnetotelluric sounding were performed. The analysis of geoelectric sections based on the one-dimensional inversion of the initial and normalized invariant curves of apparent resistance showed that it is necessary to use methods of three-dimensional mathematical modeling to form reliable geoelectric models. Their initial construction necessary for the three-dimensional interpretation of invariant apparent resistance curves was carried out taking into account their one-dimensional inversion. The resulting model, including the lower structural part, is constructed by the method of interactive matching to the apparent resistance curves of model curves calculated according to the program of three-dimensional mathematical modeling. This approach made it possible to take into account the influence of local galvanic distortions on the apparent resistance curves when evaluating the distribution of electrical conductivity in the lower parts of the subsea deposits. As a result of the integrated interpretation of magnetotelluric data, blocks with increased conductivity have been identified in the subsea deposits of the Northern Caspian Sea, which are most likely associated with high fluid saturation of Cretaceous and Neogene sediments. Their position correlates with the regional discontinuous structures of the region.

The spatiotemporal heterogeneity of fertosphere hotspots impacted by biochar addition and the implications for NH3 and N2O emissions

The fertosphere, as the interfaces between fertilizer granular and soil particles, represents a key hotspot for nitrogen transformation processes, particularly for ammonia (NH3) and nitrous oxide (N2O) emissions. Understanding the heterogeneity of the fertosphere, especially when incorporating organic amendments like biochars, is crucial for predicting NH3 and N2O emissions after soil fertilization. In this study, we investigated the effects of three types of biochar (pristine, aged, and acid-washed biochar) on heterogeneity of fertosphere induced by localized urea application. pH-specific planar optodes were employed to visualize pH gradients in fertosphere hotspots with high spatial and temporal resolution. In addition, we conducted thorough measurements of the gradient distribution of electric conductivity (EC), mineral N, aqueous NH3 in soil and enzyme activities relevant to nitrification. Concurrently, NH3 and N2O emissions from the soil were continuously monitored at a high temporal resolution. Initially, urea-induced fertosphere exhibited significant NH3 emissions, primarily attributed to the pH elevation resulting from urea hydrolysis. However, after 6 days, NH3 emissions subsided, and there was a notable sharp increase in N2O emissions. Importantly, compared to urea application alone, the inclusion of pristine biochar led to a delay in soil pH decline with a 19% rise in NH3 emission. Aged biochar, characterized by a higher content of oxygen functional groups, demonstrated increased NH4+/NH3 adsorption capacity and enhanced ammonia monooxygenase (AMO) activity in soil, resulting in an 18% reduction in NH3 emission. While a slight decrease of 5% in NH3 cumulative emission was observed in the acid-washed biochar treatment. Notably, biochar could potentially promote nitrification-derived N2O emissions due to the accumulation of NH3 oxidation products (NH2OH). These findings could contribute to refining N transformation models for fertilized soils, and optimizing N fertilizer application strategies.

A layered multifunctional framework based on polyacrylonitrile and MOF derivatives for stable lithium metal anode

Composite Li metal anodes based on three-dimensional (3D) porous frameworks have been considered as an effective material for achieving stable Li metal batteries with high energy density. However, uneven Li deposition behavior still occurs at the top of 3D frameworks owing to the local accumulation of Li ions. To promote uniform Li deposition without top dendrite growth, herein, a layered multifunctional framework based on oxidation-treated polyacrylonitrile (OPAN) and metal-organic framework (MOF) derivatives was proposed for rationally regulating the distribution of Li ions flux, nucleation sites, and electrical conductivity. Profiting from these merits, the OPAN/carbon nano fiber-MOF (CMOF) composite framework demonstrated a reversible Li plating/stripping behavior for 500 cycles with a stable Coulombic efficiency of around 99.0% at the current density of 2 mA/cm2. Besides, such a Li composite anode exhibited a superior cycle lifespan of over 1300 h under a low polarized voltage of 18 mV in symmetrical cells. When the Li composite anode was paired with LiFePO4 (LFP) cathode, the obtained full cell exhibited a stable cycling over 500 cycles. Moreover, the COMSOL Multiphysics simulation was conducted to reveal the effects on homogeneous Li ions distribution derived from the above-mentioned OPAN/CMOF framework and electrical insulation/conduction design. These electrochemical and simulated results shed light on the difficulties of designing stable and safe Li metal anode via optimizing the 3D frameworks.

Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering

IntroductionThe mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. MethodsMulti-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.Results and discussionThe method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.

Temporal dynamics of water level and water sources analyses of karst tidal springs in Guilin, China

Abstract The hydrological dynamics and sources of karst tidal springs are difficult to capture and quantify due to fluctuations in their flow velocity. In this study, the Laolongshui (LLS) karst tidal spring in the Maocun underground river basin of Guilin City was taken as an example, with a 2-year continuous monitoring of the electrical conductivity, and water level, as well as water chemical analysis. The results showed the following: (1) discovered the variation pattern of LLS water level in different seasons, the water level fluctuates regularly on a daily scale, with a rise and fall time of 43.6 min after heavy rainfall in the rainy season, while in the dry season, it lasts for about 74–80 h. (2) Four peaks were extracted from the frequency distribution of electrical conductivity, representing the response of springwater under different rainfall conditions. (3) The annual average frequencies of the occurrence of P1, P2, P3, and P4 in terms of time are 53.82, 39.29, 6.18, and 0.72%, respectively. The results provide a new method for analyzing groundwater's source and dynamic changes in karst areas.

Time-Lapse Electromagnetic Conductivity Imaging for Soil Salinity Monitoring in Salt-Affected Agricultural Regions

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using electromagnetic induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated soil apparent electrical conductivity (ECa) measurements were taken using an electromagnetic conductivity meter (CMD2) and inverted (using a time-lapse inversion algorithm) to generate electromagnetic conductivity images (EMCIs), representing soil electrical conductivity (σ) distribution. This process involved converting EMCI data into salinity cross-sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated accurate prediction ability of soil salinity with an R2 value of 0.81. The soil salinity cross-sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmers’ income.

Modeling of blood flow in cerebral arterial circulation and its dynamic impact on electrical conductivity in a realistic multi-compartment head model

Background and ObjectiveThis study aims to assess the dynamic impact of non-Newtonian cerebral arterial circulation on electrical conductivity within a realistic multi-compartment head model. Evaluating this research question is crucial and challenging due to its relevance to electrophysiological modalities like transcranial electrical stimulation (tES), electro-/magnetoencephalography (EEG/MEG), and electrical impedance tomography (EIT). In these modalities, accurate forward modeling depends on the electrical conductivity, which is affected by complex tortuous vessel networks, limited data acquisition in Magnetic Resonance Imaging (MRI), and non-linear blood flow phenomena, including shear rate and viscosity in non-Newtonian fluid. MethodsTo obtain an approximation for the blood concentration, we first use Navier–Stokes equations (NSEs) to solve for the pressure and velocity of the blood in the major vessels. Then Fick's law is used to solve for the blood concentration in the tissues. Finally, Archie's law is used to estimate the electrical conductivity distribution based on the blood concentration. ResultsThe results, obtained with an open 7 Tesla MRI dataset, suggest that a dynamic model of cerebral blood flow (CBF) for both arterial and microcirculation can be established; we find blood pressure and electrical conductivity distributions given a numerically simulated pulse sequence and approximate the blood concentration and electrical conductivity inside the brain based on those. ConclusionsOur model provides an approximation of the dynamical blood flow and the corresponding electrical conductivity distribution in the different parts of the brain. The advantage of our approach is that it is applicable with limited a priori information about the blood flow and with an arbitrary head model distinguishing the arteries.

Using Magnetic Field Analysis for Localizing Defects of Fuel Cell & Electrolyzer Components

Production capacities of electrolysis and fuel cell technologies are expected to increase significantly in the upcoming years, reducing costs of stack components through numbering-up. To ensure reliability and durability and reduce costs along the manufacturing process, developing suitable in-line non-destructive diagnostic methods for quality control becomes essential for their industrialization.In both fuel cell and electrolyzer stacks, the main components' electrical resistance and current density distribution greatly influence stack efficiency and longevity. Here, the flowing electric current generates a magnetic field depending on its strength and direction. Potential defects caused by the manufacturing process, including cracks, defective welding spots, coating adhesion issues, delamination, and contamination, influence the components' electric conductivity and current density distribution and hence the magnetic field distribution.This work investigates non-destructive diagnostic methods using magnetic field analysis (MFA) to identify changes in the magnetic field distribution and, thus, local electrical resistance and current density distributions of fuel cell and electrolyzer components. The methods used are magnetic field imaging (MFI) and Eddy Current Testing (ECT). MFI analyzes the magnetic field distribution in all three spatial directions to trace electrical currents and identify electrical defects, creating a magnetic field signature that can potentially distinguish between functional and non-functional components along the manufacturing process. ECT measures variations in local conductivity by inducing eddy currents in the sample through an alternating magnetic field generated by a sender coil. These eddy currents create a detectable magnetic field in a receiver and are affected by defects and changes in electrical conductivity. Therefore, the method can characterize sheet resistance and material homogeneity through electrical anisotropy in electrically conductive components.In initial experiments, ECT measurements on coated and uncoated hollow embossed stainless steel bipolar half plates showed systematic differences depending on quality characteristics resulting in unique patterns and inhomogeneities which require further investigation. Furthermore, experiments with both methods on other stack components will be presented and discussed. In addition, their in-line capability will be assessed and evaluated. However, these first promising results imply that MFA is potentially useful as an in-line, non-destructive diagnostic method for quality control.

In situ XPS confirmed the efficient charge transfer of the CdS/GDY/ZnMoO4 heterojunction based on graphdiyne(CnH2n-2) for photocatalytic hydrogen production

Graphdiyne (GDY) is a new type of carbon material. Because of its excellent electrical conductivity and adjustable charge distribution, it has attracted wide attention in the field of photocatalysis. In this work, multilayer flake GDY was prepared by ball milling method and CdS/GDY/ZnMoO4 ternary composite catalyst was successfully prepared by physical mixing method. In-situ XPS verified the formation of CdS/GDY/ZnMoO4 heterojunction, which effectively inhibited electron-hole recombination, and also reflected that the new carbon material GDY had strong electron-donating ability. The experimental results show that the CdS/GDY/ZnMoO4 composite catalyst has strong redox activity. Under visible light irradiation, the hydrogen production of CdS/GDY/ZnMoO4 is 1238.61 μmol, which is 16.98 times that of pure CdS. This indicates that GDY can be effectively applied to the field of photocatalytic hydrogen production, providing new possibilities for its application.

Combining Electrical Resistivity, Induced Polarization, and Self-Potential for a Better Detection of Ore Bodies

Electrical resistivity (ER), induced polarization (IP), and self-potential (SP) are three geophysical methods that have been broadly used in the realm of mineral exploration. These geophysical methods provide complementary information, each exhibiting a distinct sensitivity to various types of mineral deposits. Considering the relationship among these three methods, we propose an integrated approach that merges their respective information to offer an improved localization technique for ore bodies. First, we invert the electrical conductivity distribution through electrical resistance tomography (ERT). Then, we use the inverted conductivity distribution to invert the IP and SP data in terms of chargeability and source current density distributions. Then, we normalize the inverted chargeability and source current density distributions and we combine them to obtain an ore body index (ORI) χ used to delineate the potential locations of ore deposits. We design this index to be sensitive to the presence of ore bodies, which are reflected by either strong and localized source current density (SP) and/or strong chargeability values (IP). The proposed method is first validated using a synthetic model with two distinct anomalies characterized by different properties. The results show the limitation of individual inversion, as each method exclusively detects one of these anomalies. The combined approach allows a better characterization of the target. Then, the approach is applied to a sandbox experiment in which two metallic bodies are buried in water-saturated sand used as the background. Again, the proposed methodology is successfully applied to the detection of the metallic targets, improving their localization compared with individual methods.

Development of 3D melanoma cultures on a hyaluronic acid-based scaffold with synthetic self-assembling peptides: Electroporation enhancement

Electrochemotherapy (ECT) with bleomycin is an effective antitumor treatment. Still, researchers are investigating new drugs and electroporation conditions to improve its efficacy. To this aim, in vivo assays are accurate but expensive and ethically questionable. Conversely, in vitro assays, although cheaper and straightforward, do not reflect the architecture of the biological tissue because they lack a tridimensional (3D) structure (as in the case of two-dimensional [2D] in vitro assays) or do not include all the extracellular matrix components (as in the case of 3D in vitro scaffolds). To address this issue, 3D in vitro models have been proposed, including spheroids and hydrogel-based cultures, which require a suitable low-conductive medium to allow cell membrane electroporation.In this study, a synthetic scaffold based on hyaluronic acid (HA) and self-assembling peptides (SAPs; EAbuK), condensed with a Laminin-derived adhesive sequence (IKVAV), is proposed as a reliable alternative. We compare SKMEL28 cells cultured in the HA-EAbuK-IKVAV scaffold to the control (HA only scaffold). Three days after seeding, the culture on the HA-EAbuK-IKVAV scaffold showed collagen production. SKMEL28 cells cultured on the HA-EAbuK-IKVAV scaffold started to be electroporated at 400 V/cm, whereas, at the same electric field intensity, those cultured on HA were not. As a reference, 2D experiments showed that electroporation of SKMEL28 cells starts at 600 V/cm using an electroporation buffer and at 800 V/cm in a culture medium, but with very low efficiency (<50 % of cells electroporated). 3D cultures on HA-EAbuK-IKVAV allowed the simulation of a more reliable microenvironment and may represent a valuable tool for studying electroporation conditions. Using Finite Element Analysis (FEA) to compute the transmembrane potential, we detected the influence of inhomogeneity of the extracellular matrix on electroporation effect. Our 3D cell culture electroporation simulations showed that the transmembrane potential increased when collagen surrounded the cells. Of note, in the collagen-enriched HA-EAbuK-IKVAV scaffold, EP was already improved at lower electric field intensities. This study shows the influence of the extracellular matrix on electric conductivity and electric field distribution on cell membrane electroporation and supports the adoption of more reliable 3D scaffolds in experimental electroporation studies.

Exploration of Undercurrent Path for Hot Springs in Tibet by UAV-based Frequency Electromagnetic Induce Method

Hot springs are useful resources for a wide range of fields, including tourist industry and power generation. In this study, an UAV-FDEM instrument is proposed, which use the unmanned aerial vehicle (UAV) to carry a multi-coil Frequency Electromagnetic induce (FDEM) instrument for scanning survey in the field. We exploit the new method to investigate the electrical conductivity (EC) distribution around the hot springs in the Yangbajing, Tibet. The results clearly present the boundary of high and low EC zones, and indicate the undercurrent path of hot spring based on the measured EC values. These discoveries by UAV-FDEM are confirmed by the electric resistivity tomography (ERT) measurements. Our investigations also reveal the causes of hot spring formation i.e., the exposure of shallow groundwater or the eruption of deep conduit hot water.

Recent Prospects of Medical Imaging and Sensing Technologies Based on Electrical Impedance Data Acquisition System

This paper presents a cost-effective data acquisition & medical imaging system using electrical impedance tomography (EIT). The presented method investigates & analyses the output image retrieved from the electrical conductivity distribution of a test object or phantom. The image reconstruction was performed using a modified imaging algorithm. The experimental setup of the proposed system mainly consists of convertible 16/8/4 electrodes (which are the phantom circumference attached), the accurate static Current Source (Milli Ampere Current with KHz Frequency) for providing current which passes into the boundary of an object through a different pair of electrodes. The output of electrodes is acquired as a voltage that is measured from the boundary of the conductive object of other electrodes pair and fed into a computer for the appropriate computing of the acquired data. EIT systems with GUI are used in the data collection procedure to provide a user-friendly interface for effective picture reconstruction, which is used both like offline and online functioning are possible. Performance testing was carried out utilizing a variety of phantom models that represented human test subjects and different physical objects. The data acquisition (DAQ) techniques used in EIT produced findings that were innovative, comparable and showed enhanced sensitivity, functionality for engineering society.

Spatiotemporal evolution of electrical conductivity in current-limiting-fuse arc

To improve the interruption capacity of a current-limiting fuse, a detailed diagnosis of the spatial electrical conductivity distribution inside the fuse arc under the current limiting phase around current zero is required, because this distribution determines the distribution of transient recovery voltage inside the fuse. However, well-established methodologies applicable to fuse arcs are lacking, so the spatial distribution remains unknown. This study presents a borescope-integrated spectroscopic system that simultaneously obtains single-shot recordings of the axial distributions of the electron density and arc temperature in the fuse arc just before extinction. Combining the electron densities and arc temperatures, we can identify the fuse arc composition and hence calculate the axial electrical conductivity distribution under the first Chapman–Enskog approximation. The electrical conductivity provided by this systematic methodology includes no large uncertainties, thus demonstrating its superiority against previous estimation methods of the electrical conductivity.

Microscale Engineering of n-Type Doping in Nanostructured Gallium Antimonide: AC Impedance Spectroscopy Insights on Grain Boundary Characterization and Strategies for Controlled Dopant Distribution.

This paper investigates the microscale engineering aspects of n-type doped GaSb to address the challenges associated with achieving high electrical conductivity and precise dopant distribution in this semiconductor material. AC impedance spectroscopy is employed as a reliable technique to characterize the microstructural and electrical properties of GaSb, providing valuable insights into the impact of grain boundaries on overall electrical performance. The uneven distribution of dopants, caused by diffusion, and the incomplete activation of introduced dopants pose significant obstacles in achieving consistent material properties. To overcome these challenges, a careful selection of alloying elements, such as bismuth, is explored to suppress the formation of native acceptor defects and modulate band structures, thereby influencing the doping and compensator formation processes. Additionally, the paper examines the effect of microwave annealing as a potential solution for enhancing dopant activation, minimizing diffusion, and reducing precipitate formation. Microwave annealing shows promise due to its rapid heating and shorter processing times, making it a viable alternative to traditional annealing methods. The study underscores the need for a stable grain boundary passivation strategy to achieve significant improvements in GaSb material performance. Simple grain size reduction strategies alone do not result in better thermoelectric performance, for example, and increasing the grain boundary area per unit volume exacerbates the issue of free carrier compensation. These findings highlight the complexity of achieving optimal doping in GaSb materials and the importance of innovative analytical techniques and controlled doping processes. The comprehensive exploration of n-type doped GaSb presented in this research provides valuable insights for future advancements in the synthesis and optimization of high-conductivity nanostructured n-type GaSb, with potential applications in thermoelectric devices and other electronic systems.

Comparison of different inversion strategies for electrical impedance tomography (EIT) measurements

SUMMARY Electrical impedance tomography (EIT) is a promising method to image the frequency-dependent complex electrical conductivity distribution of the subsurface in the mHz to kHz frequency range. In contrast to the well-developed electrical resistivity tomography (ERT) method, the inversion approach for EIT data is less established. Different inversion strategies have been proposed, but the implications of the differences between these methods have not been investigated yet. In this study, we aim to compare four different inversion strategies for EIT measurements. The first strategy (CVI) formulates the inverse problem in the complex number domain and is mathematically the most elegant method. The second strategy (RVI) is the established real-valued inversion method, which decouples the inversion of the real and imaginary parts and completely ignores the complex nature. The third strategy (ALT) is very similar to the RVI strategy in case of small phase angles, but it considers the complex coupling in the forward operator and alternately updates the real and imaginary parts of the model in the case of large phase angles. The fourth and final strategy (CVI+) was newly formulated in this study. It fully considers the complex nature of EIT measurements but separates the treatment of the real and imaginary part in terms of the data weighting and regularization. The different inversion strategies were tested with two synthetic models. The first model has a small phase contrast and the second model has a large phase contrast. In the case of a small phase contrast, the CVI strategy was able to resolve the distribution of electrical conductivity amplitude, but the inversion result for the phase angle was less reliable. The other three strategies presented similar results and the models were well resolved within the expected data misfit. In the case of a model with large phase contrast, only the newly formulated CVI + strategy was able to produce reliable results. It was found that the extremely large phase angle can have a significant influence on the modelled amplitude of data. The cross-sensitivity (i.e. the imaginary part of the sensitivity) that describes the influence on the real part of data due to a change in the imaginary part of model, or that on the imaginary part of data due to a change in the real part of model, provided unique information during the inversion. It was concluded that the CVI + strategy is theoretically the most comprehensive and correct approach for EIT inversion, but that in the case of small phase angles the RVI strategy has the practical advantage that no complex calculations are required, which substantially reduces the required computational effort.