JOURNAL OF MATERIALS AND ELECTRONIC DEVICES

Transfer Learning-Based Fault Detection in Solar Panels Using Pretrained DenseNet121 DenseNet169 and DenseNet201Architectures

2025-12-09T11:33:42+03:00

Physical and electrical residue, dust, and other foreign contaminants accumulated on the surfaces of solar panels negatively impact the efficiency of solar modules and the amount of energy directly produced. At the same time, solar energy is a natural resource that is becoming increasingly important globally for sustainable energy production. Therefore, early and accurate detection of faults that may occur in solar panels is crucial for the continuity of energy production. Furthermore, timely monitoring and cleaning of solar panel surfaces with the right techniques is also ciritical for increasing the efficiency of these modules. Traditional observational or sensor-based methods for monitoring, cleaning, troubleshooting, and maintaining solar panels exhibit limited performance due to their high cost and vulnerability to human error. Thus, this study proposed an innovative transfer learning-based autonomous deep learning (DL) approach to detect faults from solar panel images. The proposed system utilized a publicly available solar system image dataset consisting of six classes. After completing the data cleaning and preprocessing steps, feature extraction was performed using three different pre-trained DenseNet121, DenseNet169, and DenseNet201 transfer learning architectures. Six different artificial intelligence (AI) based classification algorithms were executed to perform predictions on the resulting feature maps. The performance of the proposed innovative DL-based system was evaluated using quality metrics such as accuracy, precision, recall, AUC, and F₁-score. The experimental results demonstrate that the highest accuracy rate of 88.14% was achieved with the DenseNet201+Logistic Regression (LR) hybrid model. Other results obtained in this proposed study were explained in detail and compared using tables and graphs. The findings demonstrate that AI-assisted DL and transfer learning-based approaches offer effective, fast, and low-cost solutions for solar panel monitoring and maintenance processes.

Investigation of diode parameters of Al/Al2O3/n-Si Schottky diode produced by RF sputtering method according to current-voltage and capacitance-voltage characteristics

2025-11-10T11:25:24+03:00

The primary goal of this work is to ascertain the Al/Al₂O₃/n-Si MIS type structure's current-voltage (I-V) and capacitance-conductance-voltage (C-G-V) performance when fabricated on an n-Si wafer using the sputtering method. The I-V electrical measurements of the fabricated MIS-type Schottky diode structure were taken at room temperature and in the dark. Critical electrical parameters, such as saturation current (), ideality factor (), barrier height (), series resistance (), and rectification rate (RR), are extracted using thermionic emission (TE) theory. At ± 1 V, the structure's rectifying ratio (RR) was discovered to be roughly 7965. The values of ideality factor, barrier height and saturation current are found to be about 1.56, 0.816 eV and 6.37 nA, respectively. Using Norde's approach and the TE method, the series resistance values were found to be 3.32 kΩ and 1.06 kΩ, respectively. It was discovered that the interface density of states was roughly between 10¹¹ and 10¹² eV^-1 cm^-2. 4.66x10¹¹ eV^-1 cm^-2 in the energy range (-0.72 eV) and 2.17x10¹² eV^-1 cm^-2 in the energy range (-0.53 eV) were determined to be the interface density of states. Additionally, frequency dependent capacitance () and conductance () data for the Al/Al₂O₃/n-Si structure produced at room temperature were investigated in the voltage range of -4 - +4 V and the frequency range of 20 kHz - 1 MHz. The doping donor atoms (), barrier height (), and Fermi level () for each frequency were determined by computing the interception and slope of the C^-2-V plot. As the frequency increases, both the and values increase. Additionally, voltage dependence profiles of frequency and were extracted from and data using the Nicollian-Brews method.

Enhanced Photoelectrical Performance of Al/p-Si/ZnO:B4C/Al Photodiodes Fabricated via the Sol-Gel Spin-Coating Technique

2025-11-03T11:18:11+03:00

In this study, Al/p-Si/ZnO:B₄C/Al structured photodiodes were fabricated using the sol-gel spin-coating method, and the effect of B₄C doping (0, 1, 3, 5, and 10 wt.%) on device performance was investigated. The thin films were characterized by FE-SEM and EDX analyses, confirming the formation of crack-free, homogeneous, and nanostructured surfaces on p-Si substrates. Electrical and photoresponse measurements were carried out under illumination intensities ranging from 20 to 100 mW cm^-2. At 100 mW cm^-2, the photocurrent values were determined as 56×10^-4, 16.4×10^-5, 6.97×10^-5, 3.43×10^-5, and 2.67×10^-5 A for 0%, 1%, 3%, 5%, and 10% B₄C contents, respectively. It was observed that the photocurrent decreased with increasing B₄C concentration, although the photoresponse could be tuned by adjusting the doping level. The results demonstrate that ZnO:B₄C photodiodes were successfully fabricated and that their optoelectronic performance can be effectively controlled through the B₄C doping ratio.

Optical properties of MXene-based hybrid nanocomposites

2025-12-10T12:16:45+03:00

Ti₃C₂T_x MXene/PANI (polyaniline), Ti₃C₂T_x MXene0.19/PbO and MXene0.03/PbO0.86/ PANI0.11 nanocomposites were synthesized by in situ polymerisation with lead(II)oxide (PbO) nanoparticles produced via hydrothermal route. The fundamental scientific and future development trends and research directions of MXene-based organic and inorganic nanocomposites in the field of optical functional materials are anticipated. The structural and optical properties of Ti₃C₂Tₓ MXene0.19/PbO and MXene0.03/PbO0.86/PANI0.11 nanocomposites were examined, including absorbance (A), reflectance (R), optical band gap (Eop), Urbach energy (Eᵤ), refractive index (n), the real part of the complex dielectric permittivity (ε′) and optical conductivity (σ′). The absorbance spectra revealed maximum absorption peaks at 636 nm for Ti₃C₂Tₓ MXene0.19/PbO and 332 nm for MXene0.03/PbO0.86/ PANI0.11.

PID Control of Proximal and Distal Interphalangeal Jointed Robotic Hand

2025-12-19T23:23:39+03:00

This study presents the design, modeling, and control of a biomimetic robotic hand with independently actuated proximal (PIP) and distal (DIP) interphalangeal joints. A detailed anatomical reconstruction of the human hand was created in a CAD environment, where all finger joints were modeled as single-degree-of-freedom rotary joints. A total of 14 MG90S micro servo motors—three for each of the four fingers and two for the thumb—were integrated into the design to achieve a fully actuated, multi-input–multi-output (MIMO) structure capable of independent joint control.

Following the mechanical design, the model was transferred to a Python-based simulation environment, and PID controllers were implemented for MCP, PIP, and DIP joints. PID parameters were tuned and compared using Ziegler–Nichols and Cohen–Coon methods. Closed-loop angular responses of each joint were analyzed with respect to rise time, overshoot, damping ratio, and settling performance. The results show that while Ziegler–Nichols tuning provides rapid response, it introduces significant overshoot and oscillatory behavior, particularly in low-inertia joints such as the DIP. Conversely, the Cohen–Coon method yields more balanced, stable, and well-damped responses across all joints, making it a more suitable choice for robotic finger control where precise manipulation and stability are required.

This study demonstrates that a fully actuated robotic hand that adheres to anthropometric joint structures can successfully achieve natural finger motion using PID-based independent joint control. The findings provide an important foundation for future applications in prosthetics, rehabilitation robotics, and dexterous robotic manipulation.