JOURNAL OF MATERIALS AND ELECTRONIC DEVICES

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

Vahtettin Cem BAYDOGAN* — Mon, 01 Dec 2025 00:00:00 +0300

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

Emine Buse Dağlı Özkol, Serkan Eymur, Nihat Tuğluoğlu* — Mon, 01 Dec 2025 00:00:00 +0300

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

Turan Gurgenc*, Mahmud Allavi — Mon, 01 Dec 2025 00:00:00 +0300

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

Kadir DEMIRELLI* — Mon, 01 Sep 2025 00:00:00 +0300

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

Leyla Arslan, Muhammet Aydın* — Mon, 01 Dec 2025 00:00:00 +0300

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.

Investigation of the N-Si Dative Bond at the atomistic level in drug carrier/drug delivery nanosensor applications of Si-CNTs

Serap SENTURK DALGIC* — Wed, 10 Dec 2025 00:00:00 +0300

This study investigates the interaction of drug molecules with silicon-functionalized carbon nanotubes (Si-CNTs) through N→Si dative bonding, aiming to elucidate their potential in drug delivery and carrier systems. Quantum chemical analysis of N→Si bonds, based on bond length, electron density at bond critical points (ρ_BCP), Laplacian (∇²ρ), and energy density parameters revealed varying bond strengths and characters across different drug–Si-CNT complexes. Most N→Si interactions exhibited weak to moderate dative character, suggesting transient binding suitable for controlled drug release in delivery applications. Notably, one complex of Imiquimod (IMQ)/Si-CNTs structures displayed a strong covalent-like dative bond, indicating its potential as a stable drug carrier. Findings from QTAIM, NCI, and RDG analyses underscore the key role of N→Si dative bonds in governing drug adsorption and release on Si-CNT platforms and demonstrate the usefulness of topological and energetic parameters for predicting drug–nanotube interactions. It may be suitable for both drug delivery and carrier systems. The N→Si coordinative bond is not only a fundamental bonding mode that controls the electronic and chemical properties of Si-CNTs; it is also a decisive theoretical element in targeted drug delivery, nanosensor design, and the rational design of functional carbon nanomaterials

Dielectric Characteristics of Bi doped ZnO Thin Film in Al/(Bi:ZnO)/p-Si/Au (MIS) Structures

Özkan GÖÇGELDİ, Çiğdem Şükriye GÜÇLÜ, Mümin Mehmet KOÇ, Burhan COŞKUN* — Wed, 10 Dec 2025 00:00:00 +0300

Thin films are fundamentals of modern electronic and optoelectronic devices and modern technologies. Various characterization techniques are proposed to characterize electrical and dielectric properties of thin films. These techniques are essential techniques to understand and illustrate the intrinsic characteristics of multi-layered structures and thin films. To be able to produce materials with advanced characteristics it is important to know and characterize such characteristics. Metal-insulator semiconductors (MIS) structures can be applied many optic and optoelectronic applications such as solar cells, photodiodes, photodetectors, etc. In the production process of such materials, doping or co-deposition is one of the most favourable methods. In this work, Zn thin films were doped with Bi at a rate of 5 %. As result product, Al/(Bi:ZnO)/p-Si/Au (MIS) type structures or Schottky diodes (SDs) were produced. In the previous works of our group investigated various electrical properties of MIS structures. In this work, ε'-V (real), ε''-V (imaginary) parts of dielectric characteristics, C-V, G/ꞷ-V, and Cole/Cole plots, and electric modulus were investigated. Frequency related dielectric characteristics were assessed. Using such data, intrinsic characteristics of Al/(Bi:ZnO)/p-Si/Au SDs were evaluated. The obtained value of ε' (~9) even at 10kHz is considerable higher than the maximum value of traditional SiO₂, so they can more storage electrons or energy.

From Defect Detection to Defect Type Recognition: A Vision Transformer-Based Hybrid Framework for Magnetic Tile Surface Defect Inspection

Vahtettin Cem BAYDOGAN* — Wed, 10 Dec 2025 00:00:00 +0300

Defects such as cracks, blowhole, fray, uneven, breaks, and surface irregularities that occur during the production process of magnetic tiles directly and negatively affect the performance and lifespan of electric motors. Therefore, early and accurate inspection of magnetic tiles on the production line is critically important for industrial quality control processes. However, traditional visual inspection methods have significant limitations such as high cost, susceptibility to human error, and low consistency. In this study, a two-stage deep learning framework based on Vision Transformer (ViT-Base Patch16, 224×224) is proposed for the automatic detection of magnetic tile surface defects and detailed classification of defect types. In the first stage of the proposed system, magnetic tile images were subjected to binary classification as "defective" and "free". In this stage, performance evaluation was carried out using different machine learning (ML) classifiers after ViT-based feature extraction. Experimental results showed that the highest performance was obtained by the ViT+ Multilayer Perceptron (MLP) hybrid model with 97.77% accuracy, 97.30% F₁-score, and 97.30% Area Under Curve (AUC) value. In the second stage, the goal was to recognize different defect types (cracks, blowhole, fray, uneven, and breaks) in a multi-class manner on the images identified as defective. The most successful results in this stage were again obtained with the ViT+MLP model, achieving 94.27% accuracy, 92.43% F-score, and 96.42% AUC. These findings demonstrate that the ViT-based global context learning capability provides high success in both detecting defects and discriminating defect types in magnetic tile surfaces. In conclusion, the proposed two-stage ViT-based hybrid approach is considered to offer an effective solution for fast, reliable, and low-cost automated surface inspection systems in magnetic tile production.

Investigation of Optical Parameters of Different Rates Al doped ZnO by Electrochemical Deposition

Fatih ÜNAL* — Wed, 10 Dec 2025 00:00:00 +0300

In this study, pure ZnO and Al-doped ZnO thin films with different doping ratios were fabricated using the electrochemical deposition method, and their fundamental optical parameters were examined in detail. The Al doping ratios were set to 2.5%, 5%, 7.5% and 10%. The wavelength range selected for optical analysis was 300–700 nm. Accordingly, all Al-doped ZnO thin films exhibited higher absorption compared to the pure ZnO film, with the maximum absorption observed for the 2.5% Al-doped ZnO thin film. As the doping ratio increased, the absorption decreased. The incorporation of Al reduced the transmittance of the ZnO thin films. The average transmittance values of ZnO, 2.5% Al-doped ZnO, 5% Al-doped ZnO, 7.5% Al-doped ZnO, and 10% Al-doped ZnO thin films were 95%, 79%, 82%, 83% and 83%, respectively. The optical band gap (Eg) of the Al-doped ZnO thin films was higher than that of the pure ZnO thin film. The Eg values for ZnO, 2.5% Al-doped ZnO, 5% Al-doped ZnO, 7.5% Al-doped ZnO and 10% Al-doped ZnO thin films were 3.51, 4.01, 4.00, 3.99 and 3.98 eV, respectively. The highest extinction coefficient (k) for all films was observed at 300 nm. While the k value of the pure ZnO film at 300 nm was 0.004, the corresponding values for 2.5%, 5%, 7.5% and 10% Al-doped ZnO thin films were 0.046, 0.039, 0.039, 0.041 and 0.043, indicating that k increased nearly tenfold with Al doping. For all thin films, the maximum refractive index (n) was also observed at 300 nm, and the n values decreased with increasing wavelength. At 300 nm, the refractive index of the pure ZnO film was 1.124, while the values for 2.5%, 5%, 7.5% and 10% Al-doped ZnO films were 1.338, 1.322, 1.328 and 1.333, respectively. Additionally, the optical conductivity (σ), real dielectric constant (ϵr), imaginary dielectric constant (ϵi), and dielectric loss (ϵi/ϵr) of the films were determined. All these parameters decreased with increasing wavelength but increased compared to the pure ZnO film. The maximum values were obtained for the 2.5% Al-doped ZnO thin film.

Walter Bleakney and His Contributions to Mass Spectroscopy

Ayşen ORHAN ERKOVAN — Wed, 10 Dec 2025 00:00:00 +0300

Mass spectrometry is a fundamental analytical technique that revolutionized atomic and molecular physics by allowing the precise measurement of the mass-to-charge ratio of ions. Since its early development and the foundational improvements in calculating atomic masses by pioneers such as A. J. Dempster and F. W. Aston in the early 20th century, the field has continuously evolved toward higher sensitivity and resolution. This paper presents a historical overview of Dr. Walker Bleakney (1901–1992), a professor of physics at Princeton University, whose innovative research significantly advanced these capabilities. Dr. Bleakney made pivotal contributions to the field, ranging from the confirmation of the existence and abundance of deuterium to the discovery of rare isotopes such as Sr⁸⁴ and Ba¹³⁴. Special emphasis is placed on his development of the "Bleakney-Hipple" analyzer, which utilized crossed electric and magnetic fields (E×B) to generate trochoidal ion paths, thereby establishing the theory of velocity-independent perfect focusing. Additionally, the review covers his introduction of automatic recording systems and improvements in vacuum technology. These innovations not only solved significant questions in gas ionization and shock waves during his time but also laid the theoretical and experimental groundwork for modern ion optics and spectroscopic methods still in use today.

Ensemble Learning-Based Fault Diagnosis of Electric Vehicle Lithium-Ion Batteries Using Operational Data

Merve PARLAK BAYDOĞAN* — Wed, 10 Dec 2025 00:00:00 +0300

In this study, Lithium-ion batteries, while being the fundamental energy component of electric vehicles, can experience critical failures such as internal short circuits and over-discharge, triggering thermal runaway risks. This study proposes a high-accuracy, ensemble learning-based fault diagnosis system to improve operational safety in battery management systems (BMS). To this end, the model was trained using a numerical dataset of lithium-ion battery systems. Within the proposed methodology, the performance of eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Random Forest (RF), Categorical Boosting (CatBoost), Extremely Randomized Trees (ExtraTrees), and Adaptive Boosting (AdaBoost) algorithms was analyzed in both binary and multi classification. The effectiveness of the models was evaluated using accuracy, sensitivity, recall, and F1 score. Experimental results show that ensemble learning-based methods exhibit consistent and balanced performance in both classification processes. In the binary classification process, the ExtraTrees model achieved the highest performance with 80.14% accuracy, while in the multi-classification process, the CatBoost model stood out with 92.64% accuracy and 0.9448 AUC. These findings demonstrate that ensemble learning-based approaches offer a viable and reliable framework for lithium-ion battery fault diagnosis problems.

Ag substitution effect on NdBa2Cu3Oz high- Tc superconductor system

Mehmet Eyyüphan YAKINCI* — Wed, 10 Dec 2025 00:00:00 +0300

Many research teams are actively studying NdBa₂Cu₃O_x-based High-T_c superconducting systems, conducting experimental and theoretical research to adapt them for technological use and enable various applications. The fact that these materials have a high transition temperature of 94 K and maintain good current-carrying capacity even under strong magnetic fields makes them a preferred choice for many uses. This study examines how 1% Ag substitution affects the overall properties of the NdBa₂Cu₃O_x superconducting system. The results show that Ag substitution did not significantly change the crystal structure of the base material. However, the superconducting transition temperature increased by 1.5 K, and the critical current density reached 4.9 x 10⁵ A/cm². Additionally, no impurities were detected in the structure due to Ag substitution, which also showed stabilizing effects concerning O₂ concentration. This is believed to further enhance the usability of Nd_0.9Ag_0.1Ba₂Cu₃Ox in high-tech applications.

Degradation of superconducting properties of the boron-doped FeSe0.5Te0.5 superconductor system

Mehmet Eyyüphan YAKINCI*, Kübra YAKINCI — Wed, 10 Dec 2025 00:00:00 +0300

In this study, superconducting samples were prepared using the solid-state method by adding 0, 1, 2, 3, 4, and 5 % boron to the FeSe_0.5Te_0.5 superconductor system. The structural, microstructural, electrical, and magnetic properties of the samples are examined, and the results are presented. The findings show that boron doping disrupted both the structural and electrical conduction mechanisms in the material, leading to the loss of superconductivity as the doping level increased. Structural shrinkage and increased dislocations were observed in the crystal structure. As the doping ratio increased, the normal-state resistivity, the critical transition temperature, and the zero resistance temperature all decreased significantly. The T_c^on values decreased from 15.7 K in the undoped sample to 13.9 K in the 5 % boron doped sample, and T_zero decreased from 14.8 K in the undoped sample to 10.8 K in the 5 % boron doped sample. Additionally, it was found that the diamagnetic properties deteriorated with increased boron doping and shifted toward ferromagnetic properties, negatively affecting the critical current density.