Electrical Performance Research Articles

Understanding the electrical and thermal performance of synthetic ester fluid-impregnated aged pressboard adopting optical and thermography studies

Understanding the electrical and thermal performance of synthetic ester fluid-impregnated aged pressboard adopting optical and thermography studies

Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning.

A solar cell's internal quantum efficiency (IQE) measurement reveals critical information about the device's performance. This information can be obtained using a qualitative analysis of the shape of the curve, identifying and attributing current losses such as at the front and rear interfaces, and extracting key electrical and optical performance parameters. However, conventional methods to extract the performance parameters from IQE measurements are often time-consuming and require manual fitting approaches. While several methodologies exist to extract those parameters from silicon solar cells, there is a lack of accessible approaches for non-silicon cell technologies, like gallium arsenide cells, typically limiting the analysis to only the qualitative level. Therefore, this study proposes using a deep learning method to automatically predict multiple key parameters from IQE measurements of gallium arsenide cells. The proposed method is demonstrated to achieve a very high level of prediction accuracy across the entire range of parameter values and exhibits a high resilience for noisy measurements. By enhancing the quantitative analysis of IQE measurements, the method will unlock the full potential of quantum efficiency measurements as a powerful characterization tool for diverse solar cell technologies.

Investigation of the electrical performance and carrier transport mechanism for heterostructured bilayer In2O3/InGaSnO thin-film transistors

In this study, InGaSnO (IGTO)-based bilayer In2O3/IGTO and IGTO/In2O3 thin-film transistors (TFTs) were designed, and their carrier transport mechanisms and electrical performances were investigated. Herein, the ultrathin In2O3 layer provided a higher carrier concentration (Ne), thus accumulating free carriers and enhancing the carrier mobility. The thick amorphous IGTO layer controlled the device and carrier conductance, yielding a reasonable threshold voltage (Vth). Consequently, the optimized bilayer In2O3/IGTO TFTs exhibited high field-effect mobility (μFE) of 43.6 cm2 V−1s−1 and good control with Vth of 1.2 V compared to the single layer In2O3 and IGTO TFTs. Experimental analysis indicated a decrease in the oxygen vacancy (VO) formation energy owing to the interaction between interstitial Ini and Sn. Consequently, numerous unpaired electrons were generated from VO at the hetero-interfaces. In addition, an analysis of the energy band shift indicated that the heterojunction generated parasitic channels to control the Ne, and the In2O3/IGTO TFT exhibited a smaller Rc (0.34 KΩ μm), which enhanced the μFE of TFTs. Overall, the high-performance bilayer In2O3/IGTO TFTs fabricated herein have significant potential for applications in thin-film electronics.

Experimental study on performance of 100kW low temperature superconducting steady-state magnetoplasmadynamic thruster

Abstract Applied field magnetoplasmadynamic thrusters (AF-MPDTs), with their high specific impulse and considerable thrust, are increasingly favored for large-scale space missions. This paper presents the composition, functionality, and testing methods of a high-power electric propulsion performance testing system, along with the vacuum ignition test results of a 100 kW superconducting MPD thruster. The relationships between thruster effciency, magnetic field strength, current, and mass flow rate are analyzed. For each combination of current and flow rate in an AF-MPDT, there is an optimal magnetic field strength where the thruster effciency reaches its peak. Under conditions of 320 A current and 60 mg/s flow rate, the optimal magnetic field strength is 0.5 T, yielding the highest thruster effciency of 71%.

Titanate nanorod/reduced graphene oxide-filled poly (vinylidene fluoride) fibers as piezoelectric nanogenerators

To investigate the influence of nanorod ceramic powders MTiO3 (M: Mg, Co, Ni) as fillers on the electrical performance, piezoelectric nanogenerators (PENG) device, were constructed using the electrospinning method with polyvinylidene fluoride (PVDF), reduced graphene oxide (RGO), and ceramic powders. The composite nanofibers were characterized using SEM, EDX, ATR-FTIR, EDS-mapping analysis, and XRD. Sensing capacity of the PVDF-RGO-MTiO3 nanofiber-based PENG were compared. Utilizing CoTiO3 (CT) in the PENG resulted in a voltage output of 17.03, which exhibited the best piezoelectric response. Observations revealed that mechanical stimulation could charge different capacitors, suggesting a viable platform for removing the requirement of an external power source for operating portable devices. With varying resistances in the circuit, the resistance of 104 Ω produced the maximum power density of 1.40 mW/cm2. When comparing the P-RGO-CT composite to pure PVDF, the differential scanning calorimetric analysis revealed that the thermal stability and crystallinity percentage increased. Tensile testing was used to evaluate the mechanical characteristics of P-RGO-CT PENG. The maximum stress and breaking strain that the nanofiber film can tolerate are 50 kPa and 33.9%, respectively. EIS investigations obtained that the real intrinsic internal resistance of the P-RGO-CT PENG electrode is lower than PVDF, demonstrating the good conductivity of the synthesized PENG electrode. The impedance spectrum of the PENG electrode is almost parallel to the Zim axis, demonstrating good capacitance performance.

A first-principles calculations investigate the superlubricity and adhesion performance of structural superlubricity micro-components on polydimethylsiloxane surface

Adhesion and friction are crucial considerations in the design of microelectromechanical systems because they can directly influence the energy, dissipation and wear of the system. Current related studies are primarily conducted between graphite and hard materials, such as graphite, mica and gold, but lack the theoretical support of superlubricity on the surfaces of related flexible substrates, such as Polydimethylsiloxane (PDMS). Consequently, friction and wear at the flexible interface represent a significant challenge in the failure of microelectromechanical systems devices. In this paper, we use density functional theory to simulate the adhesion characteristics and friction behaviour of structural superlubricity micro-components on the surface of a flexible substrate in an atmospheric environment. The lower graphene layer of the structural superlubricity micro-components exhibits superior adhesion performance with the PDMS substrate. The increase in normal load is simulated by changing the spacing of layers. The interlayer bonding energy increases with increasing load. The transverse friction increases gradually with the increase in load, while the average friction coefficient decreases. Graphene can reach the superlubricity state on the PDMS substrate. The reliability of this phenomenon is verified by electrical performance. We also investigate the effect of tensile strain on the friction behaviour and electrical properties. These calculations should be combined with analysis of the mechanical properties to verify the reliability of the friction performance.

An Automatic Optimization Method for Electrical and Thermal Performance of Stacked DBC Power Module

An Automatic Optimization Method for Electrical and Thermal Performance of Stacked DBC Power Module

Enhanced efficiency of the electrode by doping of neodymium on SmFeO3 for the application of supercapacitor

In response to growing demand for the renewable energy generating and storage systems, scientists are trying to synthesized electrode material with exceptional performance. Their primary objective was to develop portable intelligent technology gadgets that could guarantee global energy security. In the reported article, the SmFeO3 with Nd-doped material was manufactured by cost-effective hydrothermal method for the application of a supercapacitor. The results demonstrate that the existence of larger surface area improves electrical conduction and improves the electrons and ions pathway, resulting in a rapid charge storage device and greatly enhancing the electrical performance. The pristine SmFeO3 electrode achieved capacitance of 746.3 F g−1, and the Nd-doped SmFeO3 electrode achieved a heightened capacitance of 1455.9 F g−1 at 1 A g−1. Further, the pristine SmFeO3 electrode demonstrated low cyclic retention after the 5000th charging/discharging cycles, while the Nd-doped SmFeO3 electrode demonstrated significant cyclic retention. In addition, the material fabricated attained a significant capacitance of 923.9 F g−1 at 10 mV s−1 during cyclic voltammetry measurements. The exceptional performance of dopant materials suggests their promising conductivity and quick electron/ion transport factors in its exceptional performance made it an excellent electrode for energy storage devices as well as suitability for future-generation energy conversion by surpassing the reliance on perovskite-type structural materials.

Solution-based surface modification method for high-performance ZnO transistors

A solution-based surface modification method is first proposed for improving electrical performance of the ZnO transistors. The ZnO transistors are infiltrated for treatment in the 0.01 mol/L NH4Cl solution, and the effects of treatment time on electrical properties are examined. We find that turn-on voltage (VON) positively correlates with the treatment time, consequently sub-threshold swing (SS) is optimized significantly. After treatment for 30 s, the device showed a shift of VON from −1.96 V to −1.24 V. Additionally, the SS decreased from 154.5 mV/dec to 104.4 mV/dec, and the μFE increased from 20.69 cm2/V·s to 25.55 cm2/V·s. The XPS results imply that the surface modification method effectively reduces oxygen vacancies of the ZnO active layer, which achieves the target of adjusting VON and SS. Besides, characterization results show that the surface is smoother and the wettability is enhanced after treatment. This solution-modified strategy is expected to be put into practical application due to the advantages of easy operation, low cost, low-temperature environmental protection, and capable of large-scale treatment.

Yarn‐to‐Yarn Surface Area and Roughness as Structural Engineering Tools for Optimizing the Electrical Output of Triboelectric Nanogenerators: Geometrical and Experimental Verification

AbstractThis paper investigates the performance of woven triboelectric nanogenerators (W‐TENGs) fabricated with different fabric patterns, specifically plain, twill, and hopsack weaves, using wool and polyester yarns. It is intended to enhance the electrical output and efficiency of W‐TENGs for potential applications in wearable electronics by optimizing the weaving pattern. Results indicate that W‐TENGs with twill 2/2 and hopsack 2/2 patterns exhibit superior electrical outputs, attributed to their higher contact surface area and roughness. The twill 2/2 pattern demonstrates the highest electrical output, generating an open‐circuit voltage of 4.7 V, a short‐circuit current of 4 µA, and a transferred charge of 0.35 µC. This study also highlights the critical role of surface area and dielectric material roughness in determining the output performance of triboelectric structures. A theoretical model is developed to calculate the real contact surface area of the woven fabrics with different patterns. Statistical analysis reveals significant relationships between surface roughness parameters and electrical performance. Dynamic testing under varying contact forces and frequencies demonstrates the practical applicability and robustness of the W‐TENGs. Furthermore, the devices exhibit excellent durability, maintaining stable performance over extended periods. This research provides new insights into fabric design strategies for optimizing energy harvesting in wearable devices.

Classification of Defect Photovoltaic Panel Images Using Matrox Imaging Library for Machine Vision Application

The maintenance of large-scale photovoltaic (PV) power plants has long been a challenging task. Currently, monitoring is carried out using electrical performance measurements or image processing, which have limited ability to detect faults, are time-consuming and costly, and cannot pinpoint the defect's precise location quickly. To address these challenges, this research focused on using deep learning techniques to classify defect and non-defect PV panels. The application provided deep learning algorithms capable of image classification in various classifiers. The image dataset was carefully curated and split into training and development datasets during the training model to ensure the highest accuracy for the prediction of the presence or absence of defects on the PV panel. Statistical measures, which are the average accuracy for the training model and average prediction, were employed to evaluate the classification performance of the defect PV panel model. The results demonstrated a remarkable total accuracy of model 99.9% for each class, and prediction results showed that almost 70% of defect PV panels were detected from the testing dataset. Furthermore, a comparative analysis was conducted to benchmark the findings against other algorithms. The practical implications of this research are significant, showcasing the effectiveness of deep learning algorithms and their compatibility with machine vision applications for the classification of defect PV panel images. By leveraging these techniques, solar farm operators can significantly improve maintenance management, thereby enhancing the efficiency and reliability of solar power generation and potentially saving significant costs.

Production Quality Evaluation of Electronic Control Modules Based on Deep Belief Network

The electronic control module is an important part of a digital electronic detonator, which undergoes a complex production process that includes three electrical performance tests and three visual inspection procedures. In each inspection procedure, several different types of data are generated daily, including numerical and categorical data. To evaluate the production quality of electronic control modules, an algorithm based on a Deep Belief Network with Multi-mutation Differential Evolution (MDE-DBN) is designed in this study. First, key indicators are extracted to construct a production quality evaluation index system. A Multi-mutation Differential Evolution algorithm is designed to optimize the initial network weights of the Deep Belief Network (DBN) and integrate the production quality information into the pre-training phase. Subsequently, the preprocessed experimental data are input into the MDE-DBN algorithm to obtain the distributions of excellent, general, and unqualified production statuses, verifying the effectiveness of the algorithm. The experimental results show that the MDE-DBN algorithm has significant advantages in evaluation accuracy when compared with DBNs improved by other intelligent optimization algorithms and machine learning methods.

Synthesis and Characterization of Polyimide with High Blackness and Low Thermal Expansion by Introducing 3,6-bis(thiophen-2-yl)diketopyrrolopyrrole-Based Chromophores

The market demand for black polyimide (BPI) has grown hugely in the field of flexible copper-clad laminates (FCCLs) as a replacement for transparent yellow polyimide. The 3,6-bis(thiophen-2-yl)diketopyrrolopyrroles (TDPP) derivative is recognized for its high molar extinction coefficient. In this research, we have synthesized a diamine monomer named 3,6-bis[5-(4-amino-3-fluorophenyl)thiophen-2-yl]-2,5-bis(2-ethylhexyl)pyrrolo[4,3-c]pyrrole-1,4-dione (DPPTENFPDA), featuring a TDPP unit attached by fluorinated benzene rings. The subsequent reaction of DPPTENFPDA with pyromellitic dianhydride (PMDA) yielded an inherent BPI (DPPTENFPPI). By introducing chromophores derived from TDPP, the light absorption spectrum of DPPTENFPPI was broadened and red-shifted, thereby achieving full absorption within the visible spectrum and producing a highly black color that has a cut-off wavelength (λcut) of 717 nm and a CIE-Lab coordinate L* of 0.86. Additionally, DPPTENFPPI exhibited a low coefficient of thermal expansion (CTE) and remarkable thermal and electrical performance. Density functional theory calculations were conducted to explore the electronic nature of DPPTENFPPI. The outcomes revealed that the excellent light absorption of DPPTENFPPI predominantly originates from the transition from HOMO to LUMO + 1 within the chromophore moiety. The FCCL made from DPPTENFPPI films has high solder heat resistance and peel strength. This research contributes valuable insights into the structure and design of high-performance intrinsically black PIs for microelectronics applications.

Scalable production of flexible and multifunctional graphene-based polymer composite film for high-performance electromagnetic interference shielding

Graphene have gained significant interest for potential in lightweight, ultrathin, and flexible electromagnetic interference (EMI) shielding materials due to their high electrical conductivity, low density, and ability to dissipate electromagnetic waves, but its poor dispersibility and processability hinders its applications. Besides, there is an urgent need for EMI shielding materials with multifunctional features. Herein, we proposed a flexible graphene/carbon nanotubes/polyurethane (GC/PU) composite film with a dense three-dimensional multilayer structure. This resultant GC/PU film shows high tensile strength of 42.9 MPa, ultra-high conductivity of 2087.2 S/m, and X-band EMI shielding efficiency of 37.7 dB at a thickness of 130 μm and even higher value of 72.1 dB at 520 μm. Moreover, this GC/PU film demonstrates an excellent electric heating performance (surface temperature of 127.5 °C at 5.0 V, thermal response <20 s) and flame-retardant capability, which endows it with the capability of anti-icing/de-icing. Therefore, this work may provide a promising approach for developing high-performance EMI shielding composites that may be used in the areas of wearables, defense, and aerospace.

The design, experimental and numerical study on a novel double-skin glass ventilation wall with PV blind integrated with thermal catalytic materials for synergistic energy generation and air purification

The application of photovoltaic (PV) technology on double-skin glass ventilation wall can increase its functionality and solar utilization efficiency. However, PV modules don't make multi-effect use of heat while generating electricity. Considering that the combination of thermal catalytic (TC) and PV blinds can solve this problem and achieve synergy effect, a novel double-skin glass ventilation wall with PV blind integrated with thermal catalytic materials (PV&TC-blind DSF) that realized power generation, heating and air purification was proposed. In this paper, the PV&TC blinds were designed. Then electrical, thermal and purification performances were comprehensively investigated by experimental and numerical methods. The main results are: (1) The optimal average electrical efficiency, thermal efficiency and formaldehyde single-through conversion were 9.80 %, 56.98 % and 30.89 %, respectively. (2) The established model well fitted experimental data with the maximum RSMD of 7.2 %. (3) The blind angle affected significantly on the thermal and purification performance of the wall, but had relatively small impact on the electrical performance. (4) When the system operated at optimal turning angles, the total reduced heating/cooling loads by the system were 440.6 MJ/m2 and 395.1 MJ/m2, respectively. The annual electricity generation and generated clean air volume were 264542.8 kWh/m2 and 23947.1 m3/m2, respectively.

Theoretical study on electrochemical and thermal behavior of GNP incorporated anode materials for lithium-ion batteries

In this paper, a theoretical investigation of the electrical and thermal performance of lithium-ion batteries employing an unexplored graphene nanoplatelets (GNP) incorporated lithium titanate (LTO-GNP) anode and lithium manganese oxide cathode is demonstrated by using the COMSOL Multiphysics modeling and simulation. The GNP proportions in the LTO active material matrix are varied from 0 to 3 wt%, to assess the improvements in cell potential, state of charge (SOC), variation in internal temperature, discharging capacity, and battery power density across four selected electrodes. The simulation results have indicated the positive effects of increasing the anode’s GNP concentration on the battery performance, alongside temperature-dependent open-circuit voltage (OCV) and SOC outputs of the battery. The analysis of the effects of thermal conductivity on heat accumulation and dissipation within the battery has indicated that the anode with 3 wt% of GNP consistently performed superior to the other electrodes by providing uniform temperature distribution and enhanced electrical performance by promoting better discharge capacity and power density. The LTO anode with 3 wt% GNP has shown 0.2V more cell potential and also has 12.5% improved thermal conductivity when compared to LTO anode without GNP, contributing to better heat distribution inside the battery. The simulation also emphasized the importance of optimal GNP loading in the Li-ion battery anode to attain more uniform temperature distribution with improved battery health and efficiency.

Line-tunneling based GaP/Si heterostructure vertical gate-all-around tunnel FET for enhanced electrical performance

This study explores strategies to enhance Tunnel FET performance through innovative device geometry and heterostructure integration. The use of a GaP/Si heterostructure, combined with a heavily doped n + source pocket, significantly boosts BTBT (band-to-band tunneling). The gate, which completely surrounds the source, induces line-tunneling and improves gate control over the tunnel junction, further contributing to enhanced device performance. The incorporation of a gate-drain underlap architecture and a vertically oriented design effectively suppresses ambipolar and leakage currents, resulting in an increased current ratio, reduced subthreshold slope (SS), and a minimized device footprint. Comparative analysis with existing TFET architectures shows that the optimized device achieves promising results, including an average SS of 13 mV/decade, an off-current (Ioff) of 2.36 × 10-17 A, an on-current (Ion) of 2.1 × 10-5 A, and an ambipolar current (Iamb) of 3.28 × 10-16 A.

Analysis of temperature sensitive electrical performance of sputter grown Ni and Ni–Cr Schottky contacts on 4 H-SiC

Analysis of temperature sensitive electrical performance of sputter grown Ni and Ni–Cr Schottky contacts on 4 H-SiC

Numerical analysis and optimization of photovoltaic performance of Sb2Se3 based photocathode

Antimony selenide (Sb2Se3) based heterojunction photocathodes have recently received an increased attention, largely due to their outstanding performances for hydrogen production through photoelectrochemistry (PEC) water splitting. The PEC water splitting process encompasses both physical and electrochemical processes. The physical process is capable of generating a photo-voltage, which can drive the photo-generated electrons transport to the electrode/electrolyte interface through the p-n junction. However, unlike traditional photovoltaic device, the protective layer and co-catalyst will also affect the electrical performance of device, resulting in a decrease in PEC performances and stability. How to optimize the electrical properties of the photoelectrode is a concern. In this work, devoted to Sb2Se3/TiO2 photocathode structures, the photovoltaic performances of a photocathode were modeled and analyzed from three aspects: p-n junction, back contact, and transition layer between TiO2 and co-catalyst, using the SCAPS-1D software and a realistic set of material parameters. Based on reported optimization strategy, tthe interface electrical characteristics of photocathode were studied by adjusting energy band, donor/acceptor density, defect density, electron affinity, and other parameters. A low-cost and easy to implement optimization strategy was proposed, which used Cd1-xZnxS as the buffer layer between p-n junctions, W-doped TiO2 as the transition between TiO2/co-catalyst, and Sn-doped Sb2Se3 as the back surface layer to suppress the carrier recombination. The optimized photocathode can theoretically obtain photoelectric conversion efficiency of 17.01%–17.14% and a maximum Jsc of 38.79 mA/cm2, exhibiting the potential to obtain a large photocurrent in the photoelectrochemical water splitting process.

Self-powered asphalt-based sensors for smart roads

Monitoring traffic conditions and pavement structures is essential for intelligent transportation systems. However, conventional sensors have limitations, including poor compatibility with pavement and high maintenance costs. Here, we present the concept of transforming asphalt from a pavement structural component to a sensing component and demonstrate its application in smart road systems. The functional asphalt was customized by adding piezoelectric materials into the asphalt matrix. We optimized its piezoelectric properties by improving the fabrication process and measured the electrical performance of the asphalt-based sensors. In the traffic monitoring experiment, we developed a system incorporating data acquisition, signal processing, and wireless transmission functions to capture tire-ground contact information. The details, such as speed and wheelbase, are decoded by a feature extraction algorithm and input into a support vector machine (SVM) classification model for training and testing. The model reaches a test accuracy of 97 % in training a small-sample dataset. In addition, the self-powered asphalt-based sensor showed its feasibility and potential in the verification experiments of acoustic source localization (error = 2.4 %) and energy harvesting. Our work proposes a low-cost, environmentally friendly, multifunctional material suitable for road sensors, potentially facilitating the large-scale implementation of smart road networks.