Current-voltage Characteristics Research Articles

Design and simulation of CsPb.625Zn.375IBr2-based perovskite solar cells with different charge transport layers for efficiency enhancement

In this work, CsPb.625Zn.375IBr2-based perovskite solar cells (PSCs) are numerically simulated and optimized under ideal lighting conditions using the SCAPS-1D simulator. We investigate how various hole transport layers (HTL) including Zn3P2, PTAA, MoS2, MoO3, MEH-PPV, GaAs, CuAlO2, Cu2Te, ZnTe, MoTe2, CMTS, CNTS, CZTS, CZTSe and electron transport layers (ETL) such as CdS, SnS2, ZnSe, PC60BM interact with the devices’ functionality. Following HTL material optimization, a maximum power conversion efficiency (PCE) of 16.59% was observed for the FTO/SnS2/CsPb.625Zn.375IBr2/MoS2/Au structure, with MoS2 proving to be a more economical option. The remainder of the investigation is done following the HTL optimization. We study how the performance of the PSC is affected by varying the materials of the ETL and to improve the PCE of the device, we finally optimized the thickness, charge carrier densities, and defect densities of the absorber, ETL, and HTL. In the end, the optimized arrangement produced a VOC of 0.583 V, a JSC of 43.95 mA/cm2, an FF of 82.17%, and a PCE of 21.05% for the FTO/ZnSe/CsPb.625Zn.375IBr2/MoS2/Au structure. We also examine the effects of temperature, shunt resistance, series resistance, generation rate, recombination rate, current-voltage (JV) curve, and quantum efficiency (QE) properties to learn more about the performance of the optimized device. At 300 K, the optimized device provides the highest thermal stability. Our research shows the promise of CsPb.625Zn.375IBr2-based PSCs and offers insightful information for further development and improvement.

Analysis of a fitting model based on polarization characteristics of solid oxide fuel cells

Reliability and long life are the keys to the large-scale commercialization of solid oxide fuel cells (SOFCs), so many studies have focused on how to diagnose SOFC degradation mechanisms. Electrochemical tests have been widely used to study SOFC degradation. The SOFC overpotential can be obtained by measuring the current-voltage (I–V) curves, but these measurements do not show the cause of the SOFC performance attenuation. Electrochemical impedance spectroscopy (EIS) measurements can measure various types of SOFC impedances, but this method is difficult to apply to the high-power stack due to the limited output power of the electrochemical workstation. This paper presents a resistance fitting model based on the I-V measurements for large SOFCs that can determine various electrochemical parameters and resistances by fitting the voltages for different regions of the current densities. The model accuracy is verified by EIS measurements at various current densities with the mean absolute percentage error for each type of resistance given by the fitting model being within 10 % of the EIS measurements which shows that the model can accurately identify the SOFC degradation mechanisms.

Deep traps and persistent photocapacitance in p-SnO2/i-ZrxSn1-xO2/n-SnO2 p-i-n diodes

Current-voltage characteristics in the dark and under illumination, capacitance-voltage characteristics and admittance spectra in the dark and under illumination were measured for two sets of p-SnO2:Mg/i-ZrxSn1-xO2/n-SnO2/n+SnO2:Nb structures. These data strongly point to the structures demonstrating clear-cut persistent photocapacitance behavior existing for temperatures above room temperature. The phenomenon is shown to be due to the centers in the i-ZrxSn1-xO2 layer. These centers are believed to be related to residual Nb donors coming from Nb doped SnO2 layers. The analogy is drawn between the above-mentioned effect and the persistent photocapacitance phenomena in n-type doped AlGaN films for varying Al compositions that have been convincingly explained in the literature as in fact due to some of the standard n-type dopants being DX-like centers with a high barrier for capture of electrons. For the two studied p-SnO2/i-ZrxSn1-xO2/n--SnO2/n+-SnO2:Nb samples the thermal ionization energy, the barrier for capture of electrons, the optical ionization energy of the centers responsible for persistent photocapacitance have been estimated. The results suggest that the photoresponsivity of the samples increases when increasing the Zr mole fraction closer to the value of x = 0.3 and the concentration of centers responsible for persistent behavior decreasing from some 1017 cm-3 to some 1016 cm-3.

A novel approach using iterative g-function and chaotic cooperation search for accurate voltage calculation of double and triple diode solar cell models

The solar cells can be accurately represented via double and triple-diode models (DDM and TDM, respectively). In this paper, a novel iterative procedure based on applying the g-function is proposed for double and triple solar cell voltage calculation for the first time. The proposed iterative procedure is performed without any approximations, while its application overcomes numerical limitations when applying literature-accepted approaches based on the Lambert W function. Also, one notable advantage of the proposed procedure is its effectiveness with arbitrary starting point values. Second, in this paper, an original equation for calculating solar cells’ root mean square error (RMSE) voltage (RMSEU) in the DDM and TDM, which aids in parameter estimation, is proposed. Third, the paper introduces a new metaheuristic algorithm called the Chaotic Cooperation Search Algorithm (ChCSA) for estimating solar cell parameters. The proposed approach (iterative procedure for voltage-current representation as well as a novel algorithm) is tested on two well-known solar cells in the literature and on measured current-voltage characteristics for different weather conditions. Applying the proposed algorithm and the novel iterative procedure for DDM and TDM voltage calculations demonstrated the accuracy and efficiency of the proposed approach for solar cell parameter estimation. In order to achieve the appropriate accuracy, it was shown that only a few iterations of the proposed iterative procedure are needed for the majority of measurement points in both observed cells. Furthermore, the application of the proposed algorithm outperforms existing algorithms in solar cell parameter estimation. Moreover, with the well-known Photowatt-PWP 201 solar module, the application of the proposed algorithm enables the estimation of the parameters of the solar cell so that the RMSEU value is reduced by about 50 % compared to the results obtained for the parameters determined by numerous approaches from the literature. Therefore, the presented research offers a novel and original double and triple solar cell modeling perspective.

Performance evaluation of resin wafer electrodeionization for enhanced nitrogen recovery from ammonium-containing water environment

Ammonium ion (NH4+) is a common pollutant from various sources like human and animal waste, industrial effluents, agricultural runoff, and commercial products, with traditional treatment methods often generating secondary contaminants, such as nitrite and nitrate. In this study, an energy-efficient resin-wafer electrodeionization (RW-EDI) process was employed for nitrogen recovery from ammonium-rich water. The system was operated in a continuous mode, with voltage-current curve measurements identifying a limiting current density of 2.2 A cm−2. With the NH4+ concentration of 1 g/L and a flow rate of 2.4 mL min−1, the system was tested at various cell voltages (1.2–7.2 V). The system could achieve 80 % NH4+ removal efficiency at a cell voltage of 3 V, current density of 2.2 A cm−2, influent NH4+ level of 3 g/L, and flow rate of 6.4 mL min−1. The observed current efficiency and the calculated power consumption were 41 % and 0.78 kWh m−3, respectively. Finally, a mass balance model was developed to predict the number of RW-EDI stacks for complete removal of NH4+ from the feed. Overall, the result from this study demonstrates that the RW-EDI-based system could be a promising technology for recovering nitrogen from wastewater.

The effect of annealing on the structural, optical, electrical and photoelectric properties of ZnO/NiO heterostructures

In this study, n-ZnO/p-NiO heterostructures on glass substrates with an indium tin oxide (ITO) covering were obtained by using the spray pyrolysis methodology. In order to act on the structural and local compositional defects, the samples were annealed under vacuum in the temperature range of Ta = (300 – 500)°C in steps of ΔT = 50°C. X-ray diffraction results indicate the presence of only the hexagonal ZnO and cubic NiO phases in the studied samples, which was confirmed by Raman spectroscopy. Low-temperature photoluminescence (PL), as well as absorption and photoconductivity, were studied to establish the optical properties, the nature of electronic optical transitions, the energy level position of the different defects present in the samples, and the effect of temperature annealing on these properties The band gap of the compounds was determined using the Tauc approximation and the first derivative of the absorption coefficient. Likewise, the current-voltage characteristics of the n-ZnO/p-NiO heterostructures and their diode character, were analyzed.

A data-driven compact drain current model for InGaAs FinFET using TCAD assisted machine learning approach

Abstract This article presents a novel machine learning-based framework for modeling the drain current of InGaAs FinFETs. An artificial neural network, trained on a comprehensive dataset generated by 3D numerical device simulations, forms the core of this model. The simulations covered a wide range of device geometries and bias conditions, resulting in diverse current-voltage characteristics. Using TensorFlow in a Python environment, we built and trained a neural network to accurately predict drain current. The model’s performance was evaluated on independent test datasets, demonstrating its effectiveness on unseen data. The model accurately captures the effects of varying gate length, drain voltage, and gate voltage. Multiple error metrics, including mean squared error, mean absolute error, root mean squared error, and correlation coefficient, were used to assess its performance. At shorter gate lengths, the calculated relative error in drain current was 2.18% and 1.17% at VDS=50mV and 1V, respectively.
After training, the network parameters were extracted to create a Verilog-A model. This model was integrated into the SPICE simulator for circuit simulations. A resistive load inverter circuit was designed to demonstrate the model’s capabilities, and voltage
transfer characteristics and transient analysis were performed within SPICE. These results validate the effectiveness of the developed machine learning-based InGaAs FinFET model.

The dynamics of pinned charge density wave in NbSe3 nanoribbons revealed by noise spectroscopy

Abstract Systematic nonlinear transport and broadband noise (BBN) measurements are performed on single nanoribbon devices of the charge density wave (CDW) conductor NbSe$_{3}$ over a wide range of excitation levels and temperatures. The nonlinear voltage-current characteristics elucidate the depinning process of the two CDWs and the temperature dependence of their threshold electric fields. Within the temperature and electric field range where the CDW is anticipated to be entirely pinned by residual impurities, a non-monotonic behavior in the noise magnitude versus electric field is observed. This phenomenon is attributed to the proliferation of thermally activated phase slip events, enhanced by the size effect in nanodevices. The idea is corroborated by the observation of a smeared activated behavior described by the Dutta-Horn relation. Certain aspects of the temperature dependence of the noise magnitude deviate from a simple activated behavior, suggesting a multifaceted origin of the resistance fluctuations in CDW systems at the nanometer scale. These findings provide valuable insights into the dynamics of CDW in nanodevices.

Effect of Nature and Charge of Counterions and Co-Ions on Electrotransport Properties of Heterogeneous Anion Exchange Membranes

A comprehensive characterization of heterogeneous anion exchange MA-40 and MA-41 membranes, differing in the nature of functional groups and the exchange capacity (3.32 and 1.41 mmol/gdry, respectively), was carried out. The MA-40 membrane contains low basic secondary and tertiary amino groups, while the MA-41 membrane contains predominantly quaternary ammonium bases. Concentration dependences of conductivity and diffusion permeability, current-voltage curves were obtained, and the transport and structural parameters of a microheterogeneous model of membrane in solutions of different natures (salts and acids) containing singly and doubly charged cations and anions (sodium and calcium chlorides, sodium sulfate and sulfuric acid). The influence of counter- and co-ions on the electrical transport properties of the studied membranes was revealed and it was shown that changes in their properties are determined not only by the nature of the electrolyte, but also by the value of the exchange capacity of the samples, as well as the nature of their functional groups.

Divacancy graphene and graphyne as potential biomedical sensors: A first-principles study

Imbalance in the levels of hydrogen peroxide signals the onset of chronic diseases in our body. Thus, it becomes imperative to design simple material systems with single molecule sensitivity. In this study, we investigate graphene and graphyne based systems by including various vacancy structures to assess their potential as H2O2 sensor materials. From the computed adsorption energies, we select two promising candidates, interestingly both are divacancies in graphene and graphyne. We then investigate electron transport in both the systems using the DFT-NEGF approach. Upon adsorption of H2O2 on the 585 vacancy (a special case of divacancy) in graphene and divacancy graphyne monolayers, we observe distinguishing current-voltage and electron transmission characteristics, in particular, in the former. For the 585 divacancy graphene system through analysis of the electronic properties, we identify the underlying mechanisms responsible for the difference in current values between the clean and H2O2 adsorbed systems, and hence its sensing ability. This work opens up a new avenue of intelligently incorporating vacancy defects in cheaper carbon-based 2D materials to effectively serve as potentially highly sensitive and selective biosensors for early disease detection.

Effect of Al2O3 on the operation of SiNX-based MIS RRAMs

The role of a 3 nm Al2O3 layer on top of stoichiometric LPCVD SiNx MIS RRAM cells is investigated by using various electrical characterization techniques. The conductive filament formation is explained, and a compact model is used to fit the current–voltage curves and find its evolution during each operation cycle. The conduction in SiNx is also studied.

Plasticity of parylene memristors: Compact phenomenological model and synaptic properties

Memristive devices have proven themselves as synaptic elements with rich internal dynamics and stochasticity for bio-inspired neuromorphic computing systems. Memristors based on parylene are of special interest due to their promising memristive properties, biocompatibility and ease of production. However, their synaptic behavior has not yet been fully demonstrated. In addition, the previously proposed model of resistive switching (RS) of these memristors was of a qualitative character and therefore important switching nuances turned out to be hidden. In this paper, a phenomenological model of resistive switching in parylene memristors is developed, based on the electromigration of metal cations from the top electrode, taking into account the stochastic nature of the RS process and conductivity of the parylene gap between the filament and the electrode, which determines various resistive states (plasticity) of the structures. The model is confirmed by a good correspondence between the calculated and measured current-voltage characteristics of the memristors. We also demonstrate various forms of bio-inspired plasticity of the structures, such as paired pulse facilitation/depression, long-term potentiation/depression and spike timing/amplitude/width/rate dependent plasticity. In the case of rate-based plasticity it resembles the theoretically and experimentally thoroughly studied BCM plasticity rule. The results obtained show the possibility of using such structures in the development of next-generation neuromorphic computing systems with promising calculating and learning capabilities.

Nanoelectronic Detection of Acetone with MIL-53(Al)-NH2 Metal-Organic Framework on Single-Walled Carbon Nanotubes.

A new composite material has been synthesized by incorporating an amine-functionalized MIL-53 (Al) metal-organic framework (MOF) and single-walled carbon nanotubes (SWCNT) under hydrothermal conditions. This hybrid material combines the porosity of the MOF with the electrical conductivity of SWCNT. The preservation of MIL-53(Al)-NH2 MOF structure and morphology in the composite with SWCNT was verified by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, and Brunauer-Emmett-Teller surface area analysis. A characteristic current-voltage (I-V) curve showed the electrical conductivity of this composite and gave a linear response in the chemiresistive sensor with increasing concentrations of acetone. The MIL-53(Al)-NH2/SWCNT composite also displayed fluorescence quenching tendencies across various acetone concentrations, likely attributable to the impact of guest molecules influencing the framework. An alternative approach utilizing layer-by-layer sensor device fabrication was employed for growing this MOF on carbon nanotubes directly onto a silicon chip, demonstrating its potential for versatile on-chip-sensing applications.

High-fidelity analysis and experiments of a wireless sensor node with a built-in supercapacitor powered by piezoelectric vibration energy harvesting

A supercapacitor or an electric double layer capacitor (EDLC) is an essential component of a high-performance wireless sensor node (WSN) that can transmit the three-axis acceleration waveform data of structural vibrations and is powered by a piezoelectric vibration energy harvester (VEH). However, the intrinsically slow charging rate of the piezoelectric VEH triggers charging of extra capacitors formed inside the complex-shaped electrodes of the supercapacitor, which complicates the charging/discharging characteristics and thus estimation of the power generation and energy storage in the supercapacitor. Therefore, in this work, we establish an analytical methodology that allows accurate prediction of the charging/discharging characteristics of the supercapacitor built into the WSN powered by the piezoelectric VEH, considering (1) the nonlinear piezoelectricity, (2) a three-branch circuit model of the supercapacitor, and (3) the current–voltage relation equation for the diode. We develop a parameter identification technique for both the nonlinear piezoelectric VEH and the supercapacitor, and then establish a coupled analysis technique based on the differential algebraic equation (DAE). Using the DAE, we investigate the charging/discharging characteristics of the supercapacitor by considering the actual working steps of the WSN. We revealed that the second branch in the three-branch model contributes not only to an increase in the effective capacitance of the supercapacitor, but also to faster recovery of the voltage across the supercapacitor during WSN operation. We also revealed that a longer charging time led to more energy being stored in the capacitor in the third branch, from which it is difficult to extract energy within a short time period such as the WSN’s transmission process due to its long time constant. The contribution of the second and third branches must be considered to enable accurate prediction of the charging/discharging characteristics of the supercapacitor when built into the WSN powered by the piezoelectric VEH.

Structure, Optical and Electrical Properties of Nb(Zn) Doped Sol-Gel ITO Films: Effect of Substrates and Dopants.

We present comparative studies of sol-gel ITO multilayered films undoped and doped with Nb or Zn (4 at.%). The films were obtained by successive depositions of five layers using the dip-coating sol-gel method on microscopic glass, SiO2/glass, and Si substrates. The influence of the type of substrates and dopant atoms on the structure and optical properties of the sol-gel ITO thin films is examined and discussed in detail. XRD patterns of these layers showed a polycrystalline structure with an average crystallite size of <11 nm. Raman spectroscopy confirmed the chemical bonding of dopants with oxygen and showed the absence of crystallized Nb(Zn)-oxide particles, indicated by the XRD pattern. Spectroscopic Ellipsometry and AFM imaging revealed a clear dependence of the optical parameters and surface morphology of the ITO and ITO:Nb(Zn) thin films on the type of substrates and dopants. The analysis of the current-voltage and capacitance-voltage characteristics of the Al/ITO/Si structures revealed the presence of charge carrier traps in the ITO bulk and the ITO-Si interface. The densities of these traps are obtained and the character of the current transport mechanism is established.

Electric-field-induced polarization anisotropy of interband photoluminescence in GaAs

Near-infrared photoluminescence spectra of the n-doped gallium arsenide epilayer under heating lateral electric field conditions are studied. The field dependences of hot electron and hole temperatures are determined from photoluminescence spectra and from solving the power balance equation using current–voltage characteristic. Polarization anisotropy of the photoluminescence radiation arising due to the anisotropy of hot electron distribution function and angular dependence of the interband optical matrix element in the momentum space is observed and investigated. The obtained experimental data are in satisfactory agreement with the theoretical model.

Improving Solar PV System Performance with DC-DC Converters with MPPT Technique in Real-Time Simulation

A Photo Voltaic (PV) system turns Solar Energy (SE) into electricity that needs efficient converter topology. One significant obstacle associated with solar systems is the fluctuation in energy production from solar PV modules due to meteorological factors like temperature and irradiance. The advancement of Maximum Power Point Tracking (MPPT) algorithms has enhanced the effectiveness of solar panels. Implementing an effective operational control approach may optimize the performance of PV panels, while utilizing proper semiconductor materials can maximize the efficiency of converters. This work introduces a MPPT method based on Perturb and Observe (P&amp;O) algorithm. The PV system performs with optimal efficiency when it is near the IV curve, where it produces the highest amount of electricity due to its non-linear voltage-current characteristics. The power output is determined by the degree of irradiance and the temperature of the cells, resulting in the maximum achievable power output. The conditions vary according on the season, time of day, and environmental factors. Rapid variations in light can occur due to factors like clouds. Thus, it is imperative to precisely monitor the maximum power point (MPP) in all settings to optimize power generation. This study proposes a method to optimize the power output of a PV system by integrating MPPT with a DC-DC energy converter. This approach ensures that the PV generator operates at its maximum power level, irrespective of external factors such as sunshine intensity and temperature. MPPT algorithms exhibit variations in their implementation and performance, with a range of MPPT control algorithms being accessible. The P&amp;O algorithms are widely used due to their ability to regulate the MPP of PV panels. The method is validated in real-time using Typhoon Hill Simulation.

Modeling solar power plants with daily data using genetic programming and equivalent circuit

AbstractAmong the various methods proposed for modeling solar panels, those based on equivalent circuits have received significant attention. In these approaches, determining unknown parameters varies depending on the modeling objective. To model voltage–current characteristics, circuit analysis methods are employed to extract these unknown parameters. However, this modeling method relies on data provided by the solar panel manufacturer, leading to increased modeling error over time as coefficients change. In this article, a method independent of the manufacturer's data for modeling solar panels is presented. This method enables accurate modeling of pre‐installed solar power plants. By utilizing genetic programming on a single day's worth of data from a solar panel, the proposed method can establish relationships with a high degree of fit for the open‐circuit voltage, maximum power point, and short‐circuit current based on weather conditions. Through these relationships, the voltage–current characteristics can be modeled with greater precision compared to traditional circuit analysis methods, and without the need for data from the solar panel manufacturer. Finally, for further evaluation, a 3 kW solar power plant is modeled, which demonstrates the effectiveness of the proposed method.

Accurate modeling and parameters estimation of photovoltaic models: Analytical and artificial intelligence solutions

The single-diode model is a widely used framework for simulating solar cell behavior. It consists of a diode, a current source, and two resistors to replicate real-world performance. This work introduces three alternative single-diode equivalent circuits with five parameters each. The current-voltage relationships of these equivalent circuits are derived using the Lambert W function, and a closed-form analytical solution is presented using Special Trans Function Theory (STFT). Additionally, the paper introduces a chaotic variant of the Gorilla Troops Optimizer (GTO), referred to as CGTO, for parameter estimation. The effectiveness of the proposed alternative single-diode models and the CGTO algorithm was validated by comparing their accuracy, using root mean square error (RMSE) metrics, against traditional models across various solar cells and conditions (different values of irradiance and temperature). The analysis confirmed that the alternative configurations could achieve comparable precision in modeling solar cell characteristics under different conditions, challenging the exclusivity of the standard single-diode model's configuration. This indicates that various single-diode solar cell models can effectively represent the current-voltage behavior of solar cells. Furthermore, the configuration of elements in the classic SDM equivalent circuit is not definitive or the most precise. This opens the door for the creation of more sophisticated models of solar cells with diverse element configurations.

Predicting Long-Term Stability from Short-Term Measurement: Insights from Modeling Degradation in Perovskite Solar Cells during Voltage Scans and Impedance Spectroscopy.

A drift-diffusion model is used to investigate the effect of device degradation on current-voltage and impedance measurements of perovskite solar cells (PSCs). Modifications are made to the open-source drift-diffusion software IonMonger to model degradation via an increasing recombination rate during the course of characterization experiments. Impedance spectroscopy is shown to be a significantly more sensitive measure of degradation than current-voltage curves, reliably detecting a power conversion efficiency drop of as little as 0.06% over a 4 h measurement. Furthermore, we find that fast degradation occurring during impedance spectroscopy can induce loops lying above the axis in the Nyquist plot, the first time this experimentally observed phenomenon has been replicated in a physics-based model.