Electric Potential Measurements Research Articles

Transport numbers of ion-exchange membranes in ternary mixtures of KCl, H2O and ethanol relevant for electrodialysis and desalination processes

Understanding the transport phenomena in electrolyte mixtures involved in electrochemical processes is of utmost importance for the modelling and improvement of battery technology, fuel cells, ion-exchange membrane processes, and much more. In this work, we derive relations and present methods for the analysis of electric potential measurements of concentration cells and ion-exchange membrane permselectivity with ternary mixtures of KCl, H2O and ethanol (EtOH). Measurements of electric potentials across the Selemion CMVN (cation-exchange) and AMVN (anion-exchange) membranes in this ternary mixture suggest that the presence of EtOH affects the membrane permselectivity, but that the transference of EtOH is zero; i.e., ta=0. Instead, the decrease in membrane permselectivity can be explained by a varying water transference coefficient, tw. In the absence of EtOH we find tw≈4 in both membranes, and that the decrease in tw is proportional to the average thermodynamic activity of EtOH in mixtures adjacent to the membranes. The membranes were also found to be charge selective, as quantified by the ionic transport numbers; i.e., tK+=1 for the Selemion CMVN and tK+=0 for the Selemion AMVN. The presented method can readily be extended and leveraged to determine transference coefficients in other electrochemical systems.

Managing ion concentration in sap to control drying collapse, permeability, and streaming potential in plantation-grown eucalyptus timber

Wood is an important renewable resource for a future sustainable bioeconomy. Ion-mediated response in wood is considered a major factor in sap-flow regulation. This study examined the influence of sap replacement with KCl solution and deionised water on drying collapse, wood permeability, and streaming potential of never-dried wood. Volumetric and tangential collapse in KCl-treated Eucalyptus nitens logs decreased significantly by 42%–62% and by 51%, respectively. Improvement in shrinkage properties was concentration-dependent, with a maximum at a concentration (20 mM) in the range of total ionic strength found in living trees. Logs treated with deionised water showed higher normal shrinkage, without affecting collapse. Consistent with reduced collapse in KCl-treated logs, eucalyptus stem cores showing low collapse contained significantly more inorganic cations than the high-collapsed wood. The decrease in collapse when treated with KCl solution coincided with increased green wood permeability. Sap conductivity affected the streaming potential, with the polarity of the induced electric potential varying between concentrations and matching literature reports of electric potential measurements in living trees and laboratory experiments.This study confirmed that drying collapse was negatively correlated to sap conductivity, and potential technological solutions to drying collapse in E. nitens could include sap replacement as a pre-drying treatment, and/or nutrient management of the plantations.

Simultaneous measurement of electrical potential on both sides of the dielectric surface in a parallel-plate dielectric barrier discharges and analysis of net electric field

Dielectric barrier discharges (DBDs) are widely used for ozone generation and surface treatment owing to their ability to generate reactive species. Surface charges generated during discharges distort the electric field between the dielectrics and affect the generation of reactive species. Therefore, the net electric field variations are of significant interest. Herein, a DBD measurement system for the net electric field based on the Pockels effect is established for the first time. The proposed system can simultaneously measure the surface potentials on both sides of the dielectric, thereby obtaining the net electric field at the discharge gap. The net electric field distribution varies insignificantly with the magnitude of the applied voltage but significantly with gap length. Moreover, the breakdown electric field increases with a decreasing gap length. This study provides a physical explanation for microgap reactors, demonstrating that the electric field in a DBD can be manipulated.

The emerging chemistry of self-electrified water interfaces.

Water is known for dissipating electrostatic charges, but it is also a universal agent of matter electrification, creating charged domains in any material contacting or containing it. This new role of water was discovered during the current century. It is proven in a fast-growing number of publications reporting direct experimental measurements of excess charge and electric potential. It is indirectly verified by its success in explaining surprising phenomena in chemical synthesis, electric power generation, metastability, and phase transition kinetics. Additionally, electrification by water is opening the way for developing green technologies that are fully compatible with the environment and have great potential to contribute to sustainability. Electrification by water shows that polyphasic matter is a charge mosaic, converging with the Maxwell-Wagner-Sillars effect, which was discovered one century ago but is still often ignored. Electrified sites in a real system are niches showing various local electrochemical potentials for the charged species. Thus, the electrified mosaics display variable chemical reactivity and mass transfer patterns. Water contributes to interfacial electrification from its singular structural, electric, mixing, adsorption, and absorption properties. A long list of previously unexpected consequences of interfacial electrification includes: "on-water" reactions of chemicals dispersed in water that defy current chemical wisdom; reactions in electrified water microdroplets that do not occur in bulk water, transforming the droplets in microreactors; and lowered surface tension of water, modifying wetting, spreading, adhesion, cohesion, and other properties of matter. Asymmetric capacitors charged by moisture and water are now promising alternative equipment for simultaneously producing electric power and green hydrogen, requiring only ambient thermal energy. Changing surface tension by interfacial electrification also modifies phase-change kinetics, eliminating metastability that is the root of catastrophic electric discharges and destructive explosions. It also changes crystal habits, producing needles and dendrites that shorten battery life. These recent findings derive from a single factor, water's ability to electrify matter, touching on the most relevant aspects of chemistry. They create tremendous scientific opportunities to understand the matter better, and a new chemistry based on electrified interfaces is now emerging.

An insight into the effect of interaction with protein on antibacterial activity of chitosan derivatives

Antimicrobial activity of chitosan in protein-rich media is of a particular interest for various protein-based drug delivery and other systems. For the first time, bacteriostatic activity of chitosan derivatives in the presence of caseinate sodium (CAS) was studied and discussed. Complexation of chitosan derivatives soluble in acidic (CH and RCH) or alkalescent (RCH) media with CAS was confirmed by fluorescent spectroscopy, turbodimetry, light scattering data and measurement of electrical potentials of CAS/chitosan derivative complexes. An addition of CH and RCH caused a static quenching of CAS. Binding constants Kb determined for CH/CAS and RCH/CAS complexes at pH 6.0 were equal to 29.8 × 106 M−1 and 8.9 × 106 M−1, respectively. Kb value of RCH/CAS complex at pH 7.4 was equal to 1.1 × 105′M−1. The poisoned food method was used for counting the number and the direct measurement of the size of bacterial colonies on the surfaces of turbid agar media containing CAS/chitosan derivative complexex. Complete suppression of E. coli cells growth and restriction of S. aureus cells growth were observed on the surface of acidic media. A high concentration of CAS reduced the activity. The activity of RCH in alkalescent media is low or absent. These results can be promising for preparation of microbiologically stable protein-based drug delivery systems.

Feasibility study of a Heavy Ion Beam Probe for the Thailand Tokamak-1

A Heavy Ion Beam Probe (HIBP) diagnostic system can offer non-disturbing, local measurement of plasma electric potential and various plasma parameters. This paper presents results of a feasibility study for implementing a HIBP system on the Thailand Tokamak-1 (TT-1). With successful plasma discharges achieved in early 2023, TT-1, is planed for upgrades and expansion of diagnostic capabilities to conduct experiments in high confinement modes. Sector K is designated for the installation of the HIBP diagnostic system, where primary beam ions are injected through the top port and secondary beam ions emerge from the horizontal port. The HIBP system consists of two main components: (1) the primary beam injection, and (2) the detection of the secondary beam. In the injection system, the incident direction of the primary beam into the plasma is controlled by two pairs of electrostatic sweepers. For the detection of the secondary beam, the electric field produced by electrostatic sweepers, each with a length of 0.25 m and inclined at 30 degrees, ensures that the secondary beam ions enter the energy analyzer entrance at the center with normal angles. Candidate ions, such as Cs+, Rb+, and K+, with varying energies, are evaluated for their suitability based on beam attenuation and plasma density conditions. Cesium ions are suitable at lower densities (n̄e≤1×1019 m−3), while Rb+ and K+ are preferable at higher plasma densities (n̄e≥1×1019 m−3). The analysis indicates that the ratio of the attenuation current is approximately in the range of 10−3 to 10−2. This study provides valuable insights for the integration of the HIBP diagnostic system into TT-1, enabling investigations of H-mode plasma physics phenomena.

The effects of peeling on finite element method -based EEG source reconstruction

The problem of reconstructing brain activity from electric potential measurements performed on the surface of a human head is not an easy task: not only is the inverse problem fundamentally ill-posed, but the methods utilized in constructing a synthetic forward solution themselves contain many inaccuracies. As an example the usual method of modelling primary currents in the human head via dipoles brings about at least two modelling errors: one from the singularity introduced by the dipole, and one from placing such dipoles near conductivity discontinuities in the active brain layer boundaries.In this article, we observe how the removal of possible source locations from the vicinity of active layer surfaces affects the localisation accuracy of two inverse methods, Standardized Low-Resolution Tomography (sLORETA) and Dipole Scan, at different signal-to-noise ratios (SNR), when the H(div) source model is used. This source location restriction is achieved by setting a peeling depth or a threshold distance dp, that acceptable source positions must have from active gray matter boundaries. Our aim is to understand the full effect and potential of peeling with a multi-compartment and high-resolution head model, in which the cortical activity is limited into a thin and strongly folded layer of gray matter.Our results suggest that peeling can significantly improve the overall regularity of the reconstructed distributions, when a suitable peeling depth and a low enough noise level are selected. The applied inverse algorithm and brain compartment under observation also affect the accuracy.

Transport simulations and electric field in T-10 and TJ-II

A modeling of the transport processes in T-10 tokamak using Astra code for two sets of discharges having ECRH auxiliary heating is presented. Electric potential measurements using HIBP show that radial profiles are negative for ohmic heating but become positive for large heating powers. We first obtain T α and n profiles by adjusting transport coefficients and then try to obtain the radial E r profiles by assuming three models for it. Results show good agreement for Ohmic and small ECRH power phases but not for large power where E r > 0 indicating that other processes have to be included.

Surface Electric Potential Measurement with a Static Probe

Surface electric potential measurements are widely used in non-destructive inspection and testing of precision surfaces, for example, in the production of semiconductor devices and integrated circuits. Features of the construction and application of devices for measuring the surface electric potential using an immovable reference electrode are considered. Despite the need to increase the area of the probe compared to devices with a vibrating probe, measurement techniques with an immovable probe have a number of advantages and could expand the scope of surface electric potential measurements in the inspection of samples with precise surfaces. Models of the formation of a measuring signal in the presence of a spatial inhomogeneity of surface electric potential are presented and discussed.

Self-Potential as a Tool to Monitor Redox Reactions at an Ore Body: A Sandbox Experiment

Ore bodies generate natural electrical fields that are measurable at the ground surface. The ground surface signature of this electrical field is called a self-potential anomaly. We developed a sandbox experiment to monitor the evolution of a self-potential anomaly associated with redox processes mediated by bacterial activity at the surface of a buried metallic object crossing the water table. A Bio-Electrochemical Cell (BEC) is formed by a metal bar connecting the upper, oxygen-rich, part of the tank and an aquifer containing an electron donor in the form of acetate. The self-potential response was observed during a period of 327 days. The tomography of the self-potential signature confirms that self-potential tomography is able to locate the metallic target acting as a BEC. In addition, we performed redox potential, pH, and electrical potential measurements over a vertical cross-section of the tank at several time steps to obtain an idea of where the redox front is located. The distributions of the redox potential and pH further demonstrated the development of the oxidation-reduction chemical processes facilitated by the BEC as bacterial communities developed around the metallic bar. The electrical potential anomaly shows that the bacterial communities followed a short period of exponential growth, then a longer period of a sustained population. These results demonstrate the usefulness of the self-potential method in monitoring redox processes at the surface of a buried ore body. Further works will need to combine such self-potential anomalies with induced polarization anomalies through joint inversion.

Left ventricular electrical potential measured by the NOGA XP electromechanical mapping method as a predictor of response to cardiac resynchronization therapy.

The aim of the study was to determine whether left ventricular electrical potential measured by electromechanical mapping with the NOGA XP system has predictive value for response to CRT. Approximately 30% of patients who undergo cardiac resynchronization therapy do not see the expected effects. The group of 38 patients qualified for CRT implantation were included in the study, of which 33 patients were analyzed. A 15% reduction in ESV after 6 months of pacing was used as a criterion for a positive response to CRT. The mean value and sum of unipolar and bipolar potentials obtained by mapping with the NOGA XP system and their predictive value in relation to the effect of CRT were analyzed using a bulls-eye projection at three levels: 1) the global value of the left ventricular (LV) potentials, 2) the potentials of the individual LV walls and 3) the mean value of the potentials of the individual segments (basal and middle) of the individual LV walls. 24 patients met the criterion of a positive response to CRT vs. 9 non-responders. At the global analysis stage, the independent predictors of favorable response to CRT were the sum of the unipolar potential and bipolar mean potential. In the analysis of individual left ventricular walls, the mean bipolar potential of the anterior and posterior wall and in the unipolar system, mean septal potential was found to be an independent predictor of favorable response to CRT. In the detailed segmental analysis, the independent predictors were the bipolar potential of the mid-posterior wall segment and the basal anterior wall segment. Measurement of bipolar and unipolar electrical potentials with the NOGA XP system is a valuable method for predicting a favorable response to CRT.

Spatial electric potential distributions and damage mapping in 3D carbon fiber/epoxy angle-interlock woven composites with different current directions

Exploring electric potential distributions in carbon fiber reinforced polymer composites (CFRPs) is important in electrical-based structural health monitoring for in situ damage detection. Here we investigate influences of current injection angles, electrode locations, and current injection pairs on the electric potential distributions in 3D carbon fiber/epoxy angle-interlock woven (AIW) composites. We have conducted different types of electric potential measurements and established a linear anisotropic conductivity model to reveal the electric potential distributions. It was found that the curved warp yarns form many contacts and increase the conduction path, leading to lower electric conductivity and flatter potential field than those of the weft yarn with straight alignment. The orientation and value of surface electric potential field change depend on current flow direction caused by current injection mode (CIM) and conduction mechanism due to yarn arrangement. Oblique current directed at an angle of 45° is more sensitive in indicating interior damage than that of current parallel to the yarn direction. It is recommended that the oblique current injection should be used to provide effective damage sensing for CFRPs with complex structures.

Superconductivity in Te-Deficient ZrTe2.

We present structural, electrical, and thermoelectric potential measurements on high-quality single crystals of ZrTe1.8 grown from isothermal chemical vapor transport. These measurements show that the Te-deficient ZrTe1.8, which forms the same structure as the nonsuperconducting ZrTe2, is superconducting below 3.2 K. The temperature dependence of the upper critical field (H c2) deviates from the behavior expected in conventional single-band superconductors, being best described by an electron-phonon two-gap superconducting model with strong intraband coupling. For the ZrTe1.8 single crystals, the Seebeck potential measurements suggest that the charge carriers are predominantly negative, in agreement with the ab initio calculations. Through first-principles calculations within DFT, we show that the slight reduction of Te occupancy in ZrTe2 unexpectedly gives origin to density of states peaks at the Fermi level due to the formation of localized Zr-d bands, possibly promoting electronic instabilities at the Fermi level and an increase at the critical temperature according to the standard BCS theory. These findings highlight that the Te deficiency promotes the electronic conditions for the stability of the superconducting ground state, suggesting that defects can fine-tune the electronic structure to support superconductivity.

Stem electrical potential variations may aid in the early detection of drought stress in fruit-bearing trees

Electrical plant signaling as a variation potential (VP) occurs in response to a wide range of stimuli, and it is associated with the induction of sudden changes in xylem pressure. Additionally, there is evidence that electrical potential (EP) of plants changes with changing soil water content. Therefore, the use of EP as a direct plant measurement of plant water status may have potential as water stress monitoring information. EP measurements within plant stems indirectly correlate with sap flow (SF), which is one possible variable for estimating plant water use. However, whether this relationship is stable under drought conditions is not known. The present work investigated the relationships between SF and EP variations during an 18-day drought period on three avocado trees to test the hypothesis that the relationships between SF and EP may be lost due to drought intensity. The results showed that short-term variations in EP were positively associated with vapor pressure deficit (VPD) and SF variations but negatively associated with soil water content (SWC). An increase in VP emissions was observed as drought advanced, which was negatively associated with stem water potential (SWP). After 18 days of drought, irrigation almost completely suppressed short-term variations in EP. The present work provides preliminary results that strongly suggest a relationship between drought stress and EP variations in plant stems. Further research is needed to confirm whether the EP trends observed during drought are due to cavitation events and emission of VP signals and/or linked to other physiological processes, e.g., pH changes in response to drought or embolism. Meanwhile, the present work indicates that short-term variations in EP (dEP) are strongly associated with the intensity of drought stress, thus stem electrical potential variations may aid in the early detection of drought stress in fruit-bearing trees.

Machine Learning Applied to EIT: Image Reconstruction, Displaced Electrodes and Boundary Shape

Electrical Impedance Tomography (EIT) is a noninvasive, nonradioactive technique used to obtain an internal image of a body or object. This work addresses several EIT-related problems using Artificial Neural Networks (ANNs). The EIT method consists of performing measurements of electrical potentials in a set of electrodes while different patterns of electrical current are applied. This work proposes the use of a modified version of LeNet-5 to determine the conductivity distribution within the domain, resulting in an image of the interior of the boundary in which the electrodes are positioned. There are two sources of errors in the usage of EIT in medical images. The first one is the displacement of the electrodes, which are supposed to be equidistant; and the second one is the modification of the boundary shape. The human body is not rigid, and due to breathing the boundary shape changes, leading to the displacement of electrodes. A modified version of LeNet-5 is also used to determine the changes of the boundary shape, and classifiers, such as Support Vector Machine (SVM), Random Forest and K-nearest Neighbors (KNN), are used to determine the displaced electrodes. In this work, all methods are used apart. Then, the reconstruction algorithm does not yet consider these sources of errors.

System Codesign for the Measurement of Biological Tissues using Surface Potential Data Analysis

In this work we present the development of a monitoring system that is targeted to track the data of electrical potential measurements in biological tissues. The system is conceived to be portable and its design allows for low-energy consumption. To account for different types of biological materials, the measurement of the bioimpedance of the material is generally used. To achieve both speed and accuracy, we deploy a system using hardware-software codesign with reconfigurable hardware. This integral approach is facilitated by the use of a modern system of chip with mixed signal capabilities. In particular we use a field programmable gate array for accelerating signal processing and data acquisition. A double core microcontroller is used to collect, process and deliver the data over a networked environment.

Reservoir characterization of Maracangalha sandstone using measurements on outcrops and well logs

In this research, we performed petrophysical measurements using conventional techniques (Pycnometer and gas permeameter), resistivity and chargeability electrical, besides nuclear magnetic resonance (NMR), on 122 shale sandstone samples collected from outcrops of the Maracangalha Formation in Frades Island, Bahia Brazil. This Formation is an of the main hydrocarbon reservoirs in the Recôncavo Basin in Brazil, but little petrophysical information is available. Electrical measurements were performed using a sample holder build with four-electrode, two for current injection and two for electrical potential measurements, where the rock samples were inserted under condition 100% saturated with multiples salinity solutions. NMR measurements were also obtained with saturated samples and under the condition of partial saturation. The conventional, electrical, and NMR data were analyzed together, resulting in important petrophysical parameters for the lithological characterization of the Maracangalha Formation sandstones. These samples were characterized according to your effective porosity, cementation exponent, volumetric-solid conductivity, clay content, mean grain, and pore-throat sizes. Such laboratory results allowed developing an integrated approach to interpret geophysical well logs of the Maracangalha reservoir. These logs, including gamma-ray, porosity (density, sonic and neutron) and electrical data, were interpreted in terms of shaliness, effective porosity, and partial water saturation, supported by our experimental petrophysical results. In particular, to complete this interpretation was used a combination of electric logs was used to describe the true resistivity at the invaded and in the virgin zone of this reservoir. Taking into account the effects of weathering in the samples used, these results contributed to the formation of a petrophysical database for the Maracangalha Formation, as well as its use as guide values for new interpretations in geophysical profiles in this region.

Detailed magnetoelectric analysis of a nerve impulse propagation along the brachial plexus

ObjectiveTo visualize impulse conduction along the brachial plexus through simultaneous electromagnetic measurements. MethodsNeuromagnetic fields following median nerve stimulation were recorded above the clavicle with a superconducting quantum interference device biomagnetometer system in 7 healthy volunteers. Compound nerve action potentials (CNAPs) were obtained from 12 locations. Pseudocolor maps of equivalent currents reconstructed from magnetic fields and isopotential contour maps were superimposed onto X-ray images. Surface potentials and current waveforms at virtual electrodes along the brachial plexus were compared. ResultsIn magnetic field analysis, the leading axonal current followed by a trailing backward current traveled rostrally along the brachial plexus. The spatial extent of the longitudinal intra-axonal currents corresponded to the extent of the positive–negative-positive potential field reflecting transmembrane volume currents. The peaks and troughs of the intra-axonal biphasic current waveforms coincided with the zero-crossings of triphasic CNAP waveforms. The amplitudes of CNAPs and current moments were linearly correlated. ConclusionsReconstructed neural activity in magnetic field analysis visualizes not only intra-axonal currents, but also transmembrane volume currents, which are in good agreement with the surface potential field. SignificanceMagnetoneurography is a novel non-invasive functional imaging modality for the brachial plexus whose performance can surpass that of electric potential measurement.

Inverse modelling of potential measurements on reinforced concrete to assess localized corrosion: Sensitivity with respect to the corrosion state and concrete properties

We present a novel, fundamentally sound approach for the determination of corrosion rate and anode location in reinforced concrete. In this contribution, we focus on the sensitivity of this non-destructive method with respect to the state of corrosion and concrete properties. Corrosion is often the main cause for the premature deterioration of reinforced concrete structures. Especially the type of pitting (or localized) corrosion poses great danger, as it can result in fast local decrease of the steel diameter. Non-destructive testing (NDT) is essential to detect the corrosion at an early stage, without the need to damage the concrete. The most commonly applied NDT method for reinforced concrete is potential mapping. However, it only predicts the probability of corrosion at a certain location, and does not supply any information about the corrosion rate. Other electrochemical measurements targeting corrosion rate rely on the polarization resistance, based on the mixed-potential theory by Wagner and Traud [1938]. This theory, however, is not applicable to localized corrosion. A novel inverse modelling approach allows us to detect and quantify corrosion, based on electrical potential measurements at the concrete surface in combination with a small perturbation of the corroding system by an external electrode. Using a 3D finite-element model to describe the electrical potential field resulting from external polarization of the corroding steel, we estimate the location and rate of corrosion through the application of inverse methods. This study presents the sensitivity of the approach with respect to the degree of corrosion and the concrete properties themselves. We investigated effects of the width of the corroding spot, the cover thickness and the concrete resistivity. First, we analyzed the direct effect of the potentials measured at the surface, using the finite-element model. Secondly, we determine the range of these factors for which the inverse method is able to accurately estimate the corrosion rate and location. This information allows us to understand the limitations of inverse modelling of electrical potential in detecting corrosion in reinforced concrete.

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R2-score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(104) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(105) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈O(105)−O(107) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.